Skip to main content
NCBI home page
European Journal of Psychotraumatology logoLink to European Journal of Psychotraumatology
. 2023 Jun 8;14(2):2213595. doi: 10.1080/20008066.2023.2213595

Refugee visa insecurity disrupts the brain’s default mode network

La inseguridad de la visa de refugiado altera la red de modo predeterminado del cerebro

难民签证不安全扰乱了大脑的默认模式网络

Belinda J Liddell a,CONTACT, Pritha Das b,c,d, Gin S Malhi b,c,d, Angela Nickerson a, Kim L Felmingham e, Mirjana Askovic f, Jorge Aroche f, Mariano Coello f, Jessica Cheung a, Miriam Den a, Tim Outhred b,c,d, Richard A Bryant a
PMCID: PMC10251781  PMID: 37289090

ABSTRACT

Background:

Research has largely focused on the psychological consequences of refugee trauma exposure, but refugees living with visa insecurity face an uncertain future that also adversely affects psychological functioning and self-determination.

Objective:

This study aimed to examine how refugee visa insecurity affects the functional brain.

Method:

We measured resting state brain activity via fMRI in 47 refugees with insecure visas (i.e. temporary visa status) and 52 refugees with secure visas (i.e. permanent visa status) residing in Australia, matched on key demographic, trauma exposure and psychopathology. Data analysis comprised independent components analysis to identify active networks and dynamic functional causal modelling tested visa security group differences in network connectivity.

Results:

We found that visa insecurity specifically affected sub-systems within the default mode network (DMN) – an intrinsic network subserving self-referential processes and mental simulations about the future. The insecure visa group showed less spectral power in the low frequency band in the anterior ventromedial DMN, and reduced activity in the posterior frontal DMN, compared to the secure visa group. Using functional dynamic causal modelling, we observed positive coupling between the anterior and posterior midline DMN hubs in the secure visa group, while the insecure visa group displayed negative coupling that correlated with self-reported fear of future deportation.

Conclusions:

Living with visa-related uncertainty appears to undermine synchrony between anterior-posterior midline components of the DMN responsible for governing the construction of the self and making mental representations of the future. This could represent a neural signature of refugee visa insecurity, which is marked by a perception of living in limbo and a truncated sense of the future.

KEYWORDS: Refugee, visa insecurity, resting state fMRI, uncertainty, default mode network, self, mental simulation, trauma, temporary status

HIGHLIGHTS

  • Refugee visa insecurity disrupts default mode network (DMN) connectivity – a core network that supports the internal construction of the self.

  • Refugees living with insecure visa status showed decreased connectivity in the DMN and more negative coupling between midline anterior–posterior hubs of the DMN, compared to refugees living with secure visas.

  • Diminished DMN connectivity may represent a neural basis for the psychological effects of refugee visa insecurity, which is associated with prolonged uncertainty regarding the future self and increased risk for psychological distress.

Refugees are exposed to high levels of pre-migration trauma including torture, mass violence and persecution, which places them at increased risk for PTSD and depression (Steel et al., 2009). Refugees also commonly experience significant post-displacement stress, which has deleterious effects on mental health or can exacerbate the psychological aftermath of past trauma exposure (Li et al., 2016; Miller & Rasmussen, 2017; Porter & Haslam, 2005). A major source of stress for many refugees arises from the challenge of navigating pathways towards permanent resettlement. With the increasing flow of forcibly displaced migrants due to recent conflicts (UNHCR, 2022), there has been a trend towards destination host countries (e.g. Australia, Denmark, United Kingdom) adopting protection policies that provide temporary residence without permanent settlement, with the intention to deter and limit ‘irregular’ migration (Gammeltoft-Hansen & Tan, 2017). A refugee living with this form of visa insecurity faces the threat of being returned to the country from which they have fled which overshadows any attempts to establish a new life in a host country without permanent residency. Research has shown that refugee visa uncertainty adversely affects psychological functioning (Momartin et al., 2006; Newnham et al., 2019; Nickerson et al., 2019), self-determination (Steel et al., 2011) and trauma recovery pathways (Schick et al., 2018). Holders of insecure visas show higher levels of psychological distress and prevalence of mental health disorders including PTSD, depression and suicidality compared to refugees living with secure visa status (Momartin et al., 2006; Nickerson et al., 2019). Supporting refugees in this state of visa uncertainty, which also may entail limited access to financial support and services (Andrew and Renata Kaldor Centre for International Refugee Law, 2020), and having to periodically re-assert claims for protection (Andrew and Renata Kaldor Centre for International Refugee Law, 2020), is a problem for which novel solutions are required. However, the field is currently lacking a brain-based model of the impact of visa status to guide refugee service provision and practice.

We suggest that there is a need to understand the broader effects of visa insecurity on refugee adaptation, which includes understanding its effects on brain function (Abbott, 2016), in order to develop innovative ways to help refugees to manage these adversities (Holmes et al., 2014). Only one study to date has examined the effects of post-migration stress on the brain, which included immigration-related visa status resolution stressors (Liddell et al., 2019). Higher levels of post-migration stress was associated with increased activity and connectivity in face processing networks during fear face processing (Liddell et al., 2019), supporting the idea that refugee stress is associated with altered social and emotional brain functioning. However, this task-related fMRI study did not explicitly consider the role of visa status. Moreover, the resting state brain could be the ideal mode in which to consider the neural impact of visa insecurity given that measuring the brain at rest enables observation of the core functional architecture of the brain as it operates in real life (Anderson et al., 2019).

The dominant functional network at rest is the default mode network (DMN), which consists of multiple interconnected sub-networks (Buckner & DiNicola, 2019) that integrate external experiences with internal processes to promote self-knowledge (Andrews-Hanna et al., 2014). The DMN comprises a core anterior-posterior midline hub based in the anterior medial prefrontal cortex and posterior cingulate cortex (PCC), which is responsible for self-referential processing and conceptualising the self in the past, present and future (Andrews-Hanna et al., 2010). This midline hub also coordinates: (a) a dorsomedial prefrontal cortical (DMPFC) subsystem (involving the DMPFC and temporoparietal junction (TPJ)) that underpins social-based processes including social cognition and mentalising about the self or others (Andrews-Hanna et al., 2014); and (b) a medial temporal lobe (MTL) subsystem (including ventromedial prefrontal cortex (VMPFC), parahippocampal gyrus) that drives autobiographical memory processes and internally derived mental simulations (Andrews-Hanna et al., 2010). These multiple DMN systems work in synthesis to form the key brain network that underscores the self (Andrews-Hanna et al., 2014).

The key characteristic of insecure visa status is the uncertainty it entails over the future. A new theoretical model suggests the link between uncertainty and its negative emotional impact occurs via enacting ‘mental simulations’ about alternative possibilities for the future (Anderson et al., 2019). These mental simulations – which could include mind-wandering or imagining – are highly dependent on the DMN (Andrews-Hanna et al., 2014) – and in the case of uncertain situations, commonly result in increased negative affect. This model has possible relevance to refugees with insecure visas. If an insecure visa holder engages in mental simulations concerning their uncertain future connected to the outcome of their impending visa review process, this could result in increased negative affect, leading to psychological distress and anxiety. Anderson’s model of uncertainty also suggests uncertain situations may trigger affective change via sensorimotor processes (Anderson et al., 2019), which accords with Damasio’s somatic marker hypothesis (Damasio, 1996). As such, somatomotor networks may also be a candidate brain system underpinning the uncertainty embodied by refugees with visa insecurity.

In this exploratory study, we tested resting state brain functioning and connectivity of a cohort of refugees with insecure (N = 47) compared to secure (N = 52) visas for the first time in the literature. We drew hypotheses from the models outlined above that visa insecurity may affect functioning in the DMN, specifically that refugees with insecure visas may show dysregulated functional connectivity patterns within the DMN. The DMN is one of three functional networks in the triple network model of psychopathology (Menon, 2011), and given this was an exploratory analysis, we also tested the influence of visa insecurity on the functioning of the other two networks in this model – namely the central executive network (CEN) which underpins cognitive control processes, and the salience network (SN) which detects important information in the environment (Menon, 2011). Additionally, we also examined visa security group differences in somatomotor cortical networks and any other prefrontal network due to the role of the prefrontal cortex in self-related processing.

1. Materials and methods

1.1. Participants

All 104 participants had a refugee background and were living in Australia. Participants were recruited from either a torture and trauma treatment service in Sydney Australia (NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors – STARTTS; N = 51; 49%), or from advertisements at refugee support services in Sydney (N = 53; 51%). Inclusion criteria were being over 18 years old, no history of psychosis, bipolar, alcohol or substance-use disorders, no history of neurological disorder or major traumatic brain injury, no current suicidality, and capacity to undertake an MRI scan. Participants who met inclusion criteria provided informed consent according to approved protocols by the Human Research Ethics Committee of the Northern Sydney Local Health District and were reimbursed for their time.

1.2. Demographic and symptom measures

Participants were interviewed by a clinical or research psychologist with professional interpreter support if required. The Harvard Trauma Questionnaire (HTQ) was used to assess lifetime exposure to 16 potentially traumatic events (PTEs) commonly reported by refugees (Mollica et al., 1992). A total count of exposure (experienced or witnessed) was computed. PTSD symptoms were measured with the PTSD Symptom Scale-Interview (PSS-I) (Foa & Tolin, 2000), adjusted to index DSM-5 PTSD. PTSD symptom severity was calculated by summing scores of responses across 20 symptoms experienced during the previous two weeks on a 4-point scale (0 = no symptoms, 3 = 5 + times or very much); α = .93. PTSD diagnosis (DSM-5) was calculated by algorithm, with scores > = 2 indicating the presence of symptoms and consisted of at least one re-experiencing symptom (criterion B), one avoidance symptom (criterion C), two alterations to mood/cognition symptoms (criterion D) and two hyperarousal symptoms (criterion E). Depression was measured via the Hopkins Symptom Check-List (Derogatis et al., 1974), which includes 15 depression symptoms rated on a 4-point scale (1 = Not at all to 4 = Extremely), of which a mean score was computed to indicate symptom severity (α = .90). Major depressive disorder (MDD DSM-5) diagnosis was determined via the MINI interview (Sheehan et al., 1998). Postmigration stress was measured using the Postmigration Living Difficulties Checklist (Steel et al., 1999). Participants indicated the degree to which a range of post-migration stressors were a problem over the past 12 months (1 = not a problem at all, 5 = a very significant problem). A total score was computed to represent level of post-migration stress (internal consistency was adequate α = .76). We computed the presence (stressor rated at least a 3 = moderately serious problem) or absence of each stressor and compared frequencies by visa status using Chi-square tests (p < .05).

1.3. MRI data acquisition

Imaging data were acquired on a University of Sydney 3T Siemens Magnetom Trio Scanner based at the Advanced Research and Clinical High-field Imaging (ARCHI) facility at Royal North Shore Hospital, St Leonards in Sydney. A T2*-weighted gradient-echo echo-planar imaging (EPI) sequence (29 axial slices, slice thickness 4 mm with 1 mm gap, repeat time (TR) = 2000 ms, echo time (TE) = 35 ms, flip angle (FA) = 70°, 64 × 64 matrix) was used to acquire 155 whole-brain volumes of functional data – sufficient to capture connectivity states (Damaraju et al., 2014). Participants were instructed to fixate on a centrally located fixation cross during scanning, with the aim to facilitate network delineation (Van Dijk et al., 2010) and prevent participants falling asleep. Head motion was restrained via foam pads inserted on each side of the head.

1.4. fMRI data analysis

Pre-processing of fMRI data: Pre-processing of data was conducted in SPM8 (https://www.fil.ion.ucl.ac.uk/spm/software/spm8/) and GIFT (https://trendscenter.org/software/gift/). Each subject’s functional and structural images were first inspected visually for scanner artefacts and gross anatomical abnormalities, and then re-oriented so that the origin of the image lay within 3 cm of the anterior commissure. The initial five images were discarded to remove longitudinal equilibration effects. A rigid body motion correction was performed using the INRIAlign – a motion correction algorithm (Freire & Mangin, 2001) which is unbiased by local signal changes. This was followed by slice time correction, using the middle slice as the reference frame, to account for timing differences in slice acquisition. Then fMRI data were despiked to mitigate the impacts of outliers using Despike algorithm implemented within the GIFT software. These images were then spatially normalised to a common stereotactic space using the Montreal Neurological Institute (MNI) EPI template and spatially smoothed with a Gaussian kernel of 8 mm3 full width at half maximum. Following spatial normalisation, the data (originally acquired at 3.75 × 3.75 × 5 mm3) were slightly subsampled to 3 × 3 × 3 mm3, resulting in 53 × 63 × 46 voxels. Finally, prior to performing group independent component analysis (ICA), each voxel time course was variance normalised as this approach has been shown to yield better decompositions of subcortical and cortical sources (Damaraju et al., 2014).

Group spatial independent components analysis (ICA): Group spatial ICA (Calhoun et al., 2001; Erhardt et al., 2011), as implemented in GIFT was used to identify independent components as active networks. From pre-processed fMRI data, the number of independent ‘sources’/components/networks were determined using the minimum description length (MDL) criteria (Li et al., 2007) – at both the individual participant and group level. Principal component analysis (PCA) was then performed on each participant, data then concatenated and reduced further with PCA, followed by independent component estimation using the Infomax algorithm (Bell & Sejnowski, 1995). This algorithm was repeated 10 times in ICASSO (http://research.ics.tkk.fi/ica/icasso/) and the most central run was selected for further analysis. Following group decomposition, single subject time courses (TCs) and spatial maps (SMs) were then back-reconstructed using GICA and calibrated using z scores. Finally, components were visually inspected for artefacts.

Post ICA processing: Three conditions were used to select networks for analysis: (1) The peak activation cluster must exist within grey matter with low spatial overlap with vascular, ventricular, susceptibility and edge regions indicating head motion; (2) the majority of the component’s spectral power should fall in the low frequency band (0.01–0.10 Hz) (Allen et al., 2011); (3) components should fall within the default mode network, central executive network, salience network, prefrontal regulatory network, or somatomotor cortical regions. To remove remaining noise sources including scanner drift and movement related artefacts, individual participant’s TCs from the selected components were detrended, and orthogonalised with respect to estimated subject motion parameters. The impact of movement ‘spikes’ on subsequent functional network connectivity (FNC) measures was reduced by despiking the TCs (Damaraju et al., 2014).

Within and between network functional connectivity: The MANCOVAN toolbox within GIFT software was used to determine within network functional connectivity. Within network connectivity was measured using spatial maps (SMs) of networks and also distribution of spectral powers (SPs) at different frequencies. The temporal dynamic functional network connectivity (dFNC) toolbox within GIFT was used to identify inter-network connectivity patterns (Damaraju et al., 2014; Malhi et al., 2019). To compute dFNC between chosen network time courses, a sliding window approach was adopted where the window segment was tapered by convolving a rectangle (width = 20, TRs = 40 s) with a Gaussian filter (σ = 3 TRs), and advancing 1 TR at each step (Du et al., 2016), which resulted in W = 128 windows. The chosen window length 40 s (2 s × 20), has been suggested to be suitable for capturing dynamics in FNC (Du et al., 2017). Covariance between components was estimated and covariance matrices for each window were concatenated to form a Component × Component × Window array to represent the changes in covariance (correlation) between networks (components) as a function of time. Using Matlab’s implementation of k-means clustering with the squared Euclidean distance, 500 iterates and 150 replicates dynamic FNC windows were partitioned into four clusters. The centroids of these clusters can be treated as a small set of prototype connectivity ‘states’ (these can be thought of as average patterns that subjects tend to return to during the course of the experiment) (Calhoun et al., 2014; Miller et al., 2016). The optimal number of centroid states was estimated using the elbow criterion, defined as the ratio of within cluster to between cluster distances. A k of 4 was obtained using this method in a search window of k from 2 to 10 (Damaraju et al., 2014). To examine the structure of dFNC states across groups, we evaluated group-level dFNC states.

Differences between visa security groups within and between network connectivity were investigated using two sample t-tests and results were corrected for multiple comparisons using the false discovery rate (p < .05) for intra-network connectivity. Inter-network connectivity was determined using dFNC, and Bonferroni correction was applied (p < .005).

2. Results

2.1. Participant characteristics

Five participants were removed due to excess movement, resulting in a final sample of 99 individuals for analysis. Of these, 47 participants held an insecure visa, including 33 participants on a bridging visa (indicating their visa application process was undecided), 1 participant on a temporary protection visa and 13 participants were residing in community detention (as asylum seekers). Fifty-two (N = 52) held a secure visa, including 11 participants being Australian citizens and 41 participants with a permanent protection visa with permanent residency entitlements.

Table 1 presents participant demographics. Insecure and secure visa groups were similar in terms of sex, marital status, education and country-of-origin (p > .05). The insecure group was younger than the secure group (t(92.1) = 2.51, p = .014); and age was controlled for in all subsequent analyses. Groups differed on a number of expected factors related to their visa status – namely employment, time in Australia, and post-migration stress. The insecure visa group were more likely to be unable to work due to visa restrictions or health issues compared to the secure visa group, who were more likely to be employed or studying (χ2(4) = 27.18, p < .001). The insecure visa group had been in Australia for less time (t(56.8) = 2.51, p = .015) and reported higher levels of post-migration living difficulties (t(97) = 3.23, p = .002), compared to the secure visa group.

Table 1.

Participant demographic and mental health symptoms.

    Insecure visa group (N = 47) Secure visa group (N = 52) Group difference p-value
    N/mean %/SD N/mean %/SD
Age (years) 34.83 9.04 40.35 12.69 p = .014
Visa category Australian citizen 0 0% 11 21.2% p < .001
Permanent protection visa 0 0% 41 78.8%
Temporary protection visa 1 2.1% 0 0%
Bridging visa 33 70.2% 0 0%
Community detention – asylum seeker 13 27.7% 0 0%
Sex Male 30 63.8% 34 65.4% p = .872
Female 17 36.2% 18 34.6%  
Marital status Married 28 59.6% 26 50.0% p = .422
Widow/widower 2 4.3% 2 3.8%
Divorced/separated 4 8.5% 2 3.8%
Single/Never married 13 27.7% 22 42.3%
Education No education 2 4.3% 2 3.9% p = .816
Completed primary school 12 25.5% 9 17.6%
Completed high school 13 27.7% 16 31.4%
Completed tertiary or vocational training 20 42.6% 24 47.1%
Employment Employed (Full or part time) 6 12.8% 12 23.1% p < .001
Studying 4 8.5% 16 30.8%
Unemployed 3 6.4% 9 17.6%
Unable to work 31 66.0% 8 15.4%
Home duties or retired 3 6.4% 7 13.5%
Country-of-origin Iran 25 53.2% 14 26.9% p = .223
Iraq 7 14.9% 13 25.0%
Sri Lanka 2 4.3% 2 3.8%
Other* 13 27.7% 23 44.2%
Medication Psychotropic medication 4 8.5% 15 28.8% p = .01
Treatment Psychological treatment 23 53.8% 20 47.6% p = .331
Time in Australia (years) 2.70 2.04 5.64 8.10 p = .015
PTSD diagnosis (DSM-5) 14 29.8% 20 38.5% p = .364
MDE diagnosis (DSM-5) 22 46.8% 20 38.5% p = .401
PTSD Symptom severity (PSSI; sum) 21.28 13.70 23.88 15.28 p = .369
Depression symptom severity (HSCL) 2.29 0.85 2.29 0.85 p = .662
Trauma exposure (HTQ); excluding torture item (count) 10.49 3.78 11.44 3.57 p = .200
Post-migration living difficulties 47.44 10.64 40.25 11.46 p = .002
Open in a new tab

*Other countries of origin include Afghanistan, Bosnia-Herzegovnia, Cambodia, Bhutan, Morocco, Myanmar, Chile, Fiji, Ghana, Kuwait, Laos, Nigeria, Tibet and Vietnam.

In terms of postmigration stressors (see Figure 1), the insecure visa group were more likely to report fear of being sent back to their country-of-origin compared to the secure visa group (χ2(1) = 34.33, p < .001; Bonferroni-corrected). Trend effects were also observed whereby the insecure visa group were more likely to report problems associated with being unable to return home in an emergency (χ2(1) = 6.98, p = .008), being separated from family (χ2(1) = 4.99, p = .025), immigration-related interviews (χ2(1) = 6.48, p = .011), not being able to find work (χ2(1) = 4.99, p = .025) and accessing non-government organisation support (χ2(1) = 4.40, p = .036), relative to the secure group. The secure visa group was more likely to report experiencing discrimination from other ethnic groups in Australia compared to the insecure group (χ2(1) = 7.96, p = .005).

Figure 1.

Figure 1.

Open in a new tab

Prevalence of post-migration living difficulties reported at least a moderately significant level by insecure (blue) and secure (orange) visa holder groups. *p < .05; **p < .003 (Bonferroni-corrected).

Insecure and secure groups did not report significant differences in PTSD or depression diagnostic rates, or severity of PTSD or depression symptoms, and were equally likely to be receiving psychological treatment (p > .05). There was a trend towards the secure group showing higher rates of prescription of psychotropic medication (χ2(1) = 6.58, p = .01), but this did not survive a Bonferroni-corrected threshold (p < .003). Treatment was stable for all participants at least six weeks prior to test.

2.2. Networks of interest

A total of 25 networks were identified using the MDL criteria, and of these, 10 met inclusion criteria. Spatial maps for these 10 networks are visualised in Figure 2; regions within the networks are listed in Supplementary Table S1, which were used to generate specific labels for each network. Five networks constituted sub-systems of the DMN. DMN1 centred on temporoparietal regions (tpDMN), including the parahippocampal gyrus. DMN2 encompassed anterior dorsomedial prefrontal regions (admDMN), particularly the medial frontal gyrus and dorsal anterior cingulate cortex. DMN3 involved anterior ventromedial prefrontal regions (avmDMN), including the ventral anterior cingulate cortex. DMN4 focused on posterior frontal regions of the DMN (postfrDMN), particularly dorsal regions including the superior frontal gyrus, motor and supplementary motor areas. DMN5 involved posterior regions of the DMN (postDMN), including the posterior cingulate cortex and precuneus. We recognise that we have labelled networks as sub-networks of the DMN, but which may include regions which form part of other networks – including the salience network. For example, the DMN1 (tpDMN) includes the insula and other temporal regions associated with the SN. However, the identified network component is more extensive than just these regions associated with the salience network, including lateral parietotemporal regions (particularly right lateralised) which are typically included in the DMN (Menon, 2011). Therefore, we labelled the network as a sub-network of the DMN. A left and right lateralised central executive network (CEN) were identified. One prefrontal regulatory network was identified focused on lateral prefrontal cortical regions (latFN). Finally, two somatomotor networks were identified – one which was anteriorally located (antSMN) and the second, which was posteriorally located (postSMN). The cluster stability/quality of the 10 networks were very high (Iq > 0.9).

Figure 2.

Figure 2.

Open in a new tab

Networks identified by independent components analysis.

2.3. Network activity differences between visa security groups

Two DMNs showed group differences according to visa security. The insecure visa group showed decreased spectral power in the low frequency band in the avmDMN compared to the secure visa group (Figure 3(A)). Additionally, the spatial map analysis showed significantly reduced activity within the postfrDMN – namely the left superior frontal gyrus [MNI −20, −2, 56], cluster size of 27 voxels (Figure 3(B)) for the insecure visa group.

Figure 3.

Figure 3.

Open in a new tab

Between group effects in identified networks. (A) Spectral frequency analysis of between group effects within each of the 10 identified networks. (B) Spatial frequency analysis investigating between group differences in network activity; (C): Dynamic functional connectivity differences between visa status groups.

2.4. Network functional connectivity differences between visa security groups

We tested whether there were any group differences in functional connectivity between the 10 identified networks. Significant effects were restricted to the connectivity between two DMN networks (Figure 3(C)). Namely, we observed that the while the secure group showed stronger positive coupling between the avmDMM and postDMN (M = 0.01, SD = 0.10), the insecure visa group showed relatively stronger negative coupling between the avmDMN and postDMN (M = −0.07, SD = 0.10); t(1) = 3.52, p = .001, Bonferroni-corrected). This finding suggests that insecure visa holders exhibited anti-synchronous activity between anterior and posterior DMN hubs, whereby when activity in one network increased, activity levels in the other network decreased (Turalska et al., 2019). No other group differences were observed.

2.5. Posthoc partial correlations activity and connectivity with fear of deportation

We conducted a targeted post hoc analysis to examine whether group differences observed in dFNC were related to fear of deportation (p < .05). Using partial correlations to control for age, we confirmed visa security group differences in that fear of being returned to participant’s country-of-origin was negatively correlated with avmDMN – postDMN connectivity (r = −.261, p = .009), but not with spectral power in the avmDMN (r = .175, p = .085) or spatial activity in the postfrDMN (r = −.002, p = .982).

3. Discussion

This study demonstrates that refugee insecure visa status is associated with functional differences in the default mode network of the brain. Refugees with visa insecurity (i.e. living with a temporary visa) matched to refugees with a secure visa (i.e. permanent residence) on demographic factors (controlling for age) and PTSD/depression psychopathology – showed reduced activity in sub-networks of the DMN (avmDMN and postfrDMN). Moreover, an opponent pattern of coupling was observed involving the anterior DMN and posterior DMN between groups. While the secure group showed positive coupling between the avmDMN and postDMN, consistent with robust evidence that these DMN sub-networks are generally co-activated (Buckner & DiNicola, 2019), the insecure group showed increased negative coupling, which was correlated with self-reported fears related to future deportation. Visa insecurity therefore appears to be associated with ‘anti-synchronous’ connectivity (Turalska et al., 2019) between anterior and posterior midline hubs of the DMN (Andrews-Hanna et al., 2014). This may reflect a neural marker of the disrupted sense of future that marks the experience of visa insecurity for refugees.

A key finding of this study was that connectivity within the anterior medial DMN spanning ventral and dorsal regions of the prefrontal cortex (PFC) was lower in insecure visa holders relative to secure visa holders. This was reflected in reduced spectral power within the avmDMN in the low frequency band (Figure 3(A)), possibly indicating decreased metabolic activity (Yuen et al., 2019), as well as lower activity was observed in the left superior frontal gyrus within postDMN in the insecure visa group (Figure 3(B)). The anterior medial PFC is a particularly interconnected hub in the DMN that underpins many aspects of self-related processing (Andrews-Hanna et al., 2010). For example, the VMPFC is essential for assigning personal significance to external events (D'Argembeau, 2013), and integrating these events with internal information (i.e. prior experiences) in order to shape the self and inform future expectations (Andrews-Hanna et al., 2014). The VMPFC is also part of the medial temporal lobe (MTL) sub-system of the DMN, which drives mental simulations of future events, working with the anterior-posterior midline DMN core to achieve this (Andrews-Hanna et al., 2010).

Connectivity differences in the anterior–posterior midline DMN were also observed between groups. The anterior–posterior midline DMN usually functions in synchrony (Buckner & DiNicola, 2019) to support self-relevant brain processes about personally significant events in the past or that may occur in the future (Andrews-Hanna et al., 2010). The secure visa holder group displayed such a synchronous connectivity pattern through positive coupling between the avmDMN and postDMN. By contrast, the insecure group demonstrated stronger negative coupling between the avmDMN and postDMN, a pattern of anti-synchronisation (Turalska et al., 2019) whereby as activity in one network increased, activity levels in the other network decreased during rest state scans (Figure 3(C)). We suggest that this anti-synchronous connectivity between DMN midline hubs may reflect the fundamental disruption to the self (Anderson et al., 2019) that visa insecurity entails (Andrews-Hanna et al., 2010). Refugees with insecure visa status live in a limbo state with an uncertain future (Hartonen et al., 2021; Newnham et al., 2019; Nickerson et al., 2019), while waiting for outcomes of various visa applications or a permanent solution to their forced displacement. To further support this notion, we observed negative associations between avmDMN-postDMN connectivity and fears related to possible future deportation in the whole sample. We suggest that anti-synchronous connectivity between the anterior DMN and posterior DMN may represent a neural basis for uncertainty experienced by refugees with insecure visas.

Previous research has also shown disturbances to the anterior–posterior midline DMN system are evident in those experiencing high levels of stress or psychopathology. Studies examining the effects of acute and chronic stress on the DMN found diminished connectivity between anterior and posterior regions of the DMN (Ginty et al., 2019), with connectivity increasing when stress was relieved (Weissman et al., 2018). While models of psychopathology generally report increased connectivity between subsystems of the DMN and other networks (Andrews-Hanna et al., 2014), patterns of hypoactivity have been reported in patients with major depression combined with high levels of social dysfunction (Saris et al., 2020). While the groups in the current study are matched on depression and PTSD symptom levels, we cannot exclude the possibility that our findings could be attributed to some other form of underlying psychopathology we did not measure such as generalised anxiety (Nickerson et al., 2019).

We did not observe group differences in other identified networks, including bilateral CEN, bilateral somatomotor networks and a lateral prefrontal network. As such, we did not find support for our hypothesis that uncertainty associated visa insecurity may be driven by sensorimotor processes (Anderson et al., 2019). It may be that functioning in these networks are conserved in insecure visa holders, but this will need to be examined in future research.

The findings must be considered in the context of the study’s limitations. The sample was recruited from both treatment seeking and community cohorts and included highly trauma-exposed individuals. Visa status group differences in depression and PTSD symptom severity or diagnostic rates were not observed, which is inconsistent with other research that typically reports higher levels of psychopathology amongst insecure visa holders (Nickerson et al., 2019). Our data-driven approach may have missed other relevant networks, including the salience network which was not formally identified in the resultant networks. The limitations of our data-driven analysis method are that identified networks may include specific brain areas that fall into different a priori defined networks but are labelled according to the predominant network pattern. Finally, the study is cross-sectional and it remains unclear whether the effect of visa status on DMN connectivity is causal or consequential.

4. Conclusion

Our study shows that refugee visa insecurity is associated with reduced activity and more negative coupling between anterior–posterior midline hubs of the DMN that underpin self-related processing. This study has humanitarian and practice-based significance. Immigration policies that provide more certainty to refugees and asylum seekers around the terms of their visa status are critical to alleviating the burden of visa insecurity, which may affect how a refugee is able to establish a new life and productively function in their new host country – in part because of its effects on DMN. Second, responding to the psychological needs of insecure visa holders is challenging for mental health practitioners and refugee service providers given the intractability of their situational stressor. Moreover, insecure status may also interfere with the treatment of and recovery processes of mental health difficulties, including PTSD. Our findings provide a first step towards understanding the neural processes affected by visa insecurity, which could lead to the development of innovative brain-based programmes to reduce the psychological burden of visa-related uncertainty for refugees.

Supplementary Material

Supplemental Material
Click here for additional data file. (48.4KB, docx)

Acknowledgements

The authors acknowledge the courage and contribution of the participants in this study. We thank the staff of the NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors (STARTTS). We acknowledge the efforts of the Refugee Trauma and Recovery Program (RTRP), UNSW team, for their assistance in some components of data collection and analysis.

Funding Statement

This work was supported by Australian Research Council [grant number LP120200284].

Disclosure statement

Authors Liddell, Das, Nickerson, Malhi, Felmingham, Cheung, Den, Outhred and Bryant report no potential conflicts of interest. Authors Askovic, Coello and Aroche are employees of the NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors (STARTTS), which provides counselling and psychological services for torture survivors and refugees. Author Aroche also previously served as the President of the International Rehabilitation Council for Torture Victims (IRCT).

Data availability statement

The data that support the findings of this study are available on request from the corresponding author, BL. The data are not publicly available due to the de-identified data possibly containing information that could compromise the privacy and safety of the research participants.

References

  1. Abbott, A. (2016). The mental-health crisis among migrants. Nature, 538(7624), 158–161. 10.1038/538158a [DOI] [PubMed] [Google Scholar]
  2. Allen, E. A., Erhardt, E. B., Damaraju, E., Gruner, W., Segall, J. M., Silva, R. F., Havlicek, M., Rachakonda, S., Fries, J., Kalyanam, R., Michael, A. M., Caprihan, A., Turner, J. A., Eichele, T., Adelsheim, S., Bryan, A. D., Bustillo, J., Clark, V. P., Feldstein Ewing, S. W., … Calhoun, V. (2011). A baseline for the multivariate comparison of resting-state networks. Frontiers in Systems Neuroscience, 5(2), 2. 10.3389/fnsys.2011.00002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Anderson, E. C., Carleton, R. N., Diefenbach, M., & Han, P. K. J. (2019). The relationship between uncertainty and affect. Frontiers in Psychology, 10. 10.3389/fpsyg.2019.02504 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Andrew and Renata Kaldor Centre for International Refugee Law . (2020). Research Brief: Temporary Protection Visas (TPVs) and Safe Haven Enterprise Visas (SHEVs).
  5. Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010). Functional-anatomic fractionation of the brain's default network. Neuron, 65(4), 550–562. 10.1016/j.neuron.2010.02.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Andrews-Hanna, J. R., Smallwood, J., & Spreng, R. N. (2014). The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Annals of the New York Academy of Sciences, 1316(1), 29–52. 10.1111/nyas.12360 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159. 10.1162/neco.1995.7.6.1129 [DOI] [PubMed] [Google Scholar]
  8. Buckner, R. L., & DiNicola, L. M. (2019). The brain’s default network: updated anatomy, physiology and evolving insights. Nature Reviews Neuroscience, 20(10), 593–608. 10.1038/s41583-019-0212-7 [DOI] [PubMed] [Google Scholar]
  9. Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14(3), 140–151. 10.1002/hbm.1048 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Calhoun, V. D., Miller, R., Pearlson, G., & Adali, T. (2014). The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262–274. 10.1016/j.neuron.2014.10.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. D’Argembeau, A. (2013). On the role of the ventromedial prefrontal cortex in self-processing: The valuation hypothesis. Frontiers in Human Neuroscience, 7, 372. 10.3389/fnhum.2013.00372 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Damaraju, E., Allen, E. A., Belger, A., Ford, J. M., McEwen, S., Mathalon, D. H., Mueller, B. A., Pearlson, G. D., Potkin, S. G., Preda, A., Turner, J. A., Vaidya, J. G., van Erp, T. G., & Calhoun, V. D. (2014). Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage: Clinical, 5, 298–308. 10.1016/j.nicl.2014.07.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 351(1346), 1413–1420. 10.1098/rstb.1996.0125 [DOI] [PubMed] [Google Scholar]
  14. Derogatis, L. R., Lipman, R. S., Rickels, K., Uhlenhuth, E. H., & Covi, L. (1974). The Hopkins Symptom Checklist (HSCL): A self-report symptom inventory. Behavioral Science, 19(1), 1–15. 10.1002/bs.3830190102 [DOI] [PubMed] [Google Scholar]
  15. Du, Y., Fryer, S. L., Fu, Z., et al. (2017). Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis. NeuroImage, 180(Pt B), 632–645. 10.1016/j.neuroimage.2017.10.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Du, Y., Pearlson, G. D., Yu, Q., He, H., Lin, D., Sui, J., Wu, L., & Calhoun, V. D. (2016). Interaction among subsystems within default mode network diminished in schizophrenia patients: a dynamic connectivity approach. Schizophrenia Research, 170(1), 55–65. 10.1016/j.schres.2015.11.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Erhardt, E. B., Rachakonda, S., Bedrick, E. J., Allen, E. A., Adali, T., & Calhoun, V. D. (2011). Comparison of multi-subject ICA methods for analysis of fMRI data. Human Brain Mapping, 32(12), 2075–2095. 10.1002/hbm.21170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Foa, E., & Tolin, D. F. (2000). Comparison of the PTSD symptom scale-interview version and the clinician-administered PTSD scale. Journal of Traumatic Stress, 13(2), 181–191. 10.1023/A:1007781909213 [DOI] [PubMed] [Google Scholar]
  19. Freire, L., & Mangin, J. F. (2001). Motion correction algorithms may create spurious brain activations in the absence of subject motion. Neuroimage, 14(3), 709–722. 10.1006/nimg.2001.0869 [DOI] [PubMed] [Google Scholar]
  20. Gammeltoft-Hansen, T., & Tan, N. F. (2017). The end of the deterrence paradigm? Future directions for global refugee policy. Journal on Migration and Human Security, 5(1), 28–56. 10.1177/233150241700500103 [DOI] [Google Scholar]
  21. Ginty, A. T., Kraynak, T. E., Kuan, D. C., & Gianaros, P. J. (2019). Ventromedial prefrontal cortex connectivity during and after psychological stress in women. Psychophysiology, 56(11), e13445. 10.1111/psyp.13445 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hartonen, V. R., Väisänen, P., Karlsson, L., & Pöllänen, S. (2021). A stage of limbo: A meta-synthesis of refugees’ liminality. Applied Psychology, 71, 1132–1167. 10.1111/apps.12349 [DOI] [Google Scholar]
  23. Holmes, E. A., Craske, M. G., & Graybiel, A. M. (2014). Psychological treatments: A call for mental-health science. Nature, 511(7509), 287–289. 10.1038/511287a [DOI] [PubMed] [Google Scholar]
  24. Li, S. S., Liddell, B. J., & Nickerson, A. (2016). The relationship between post-migration stress and psychological disorders in refugees and asylum seekers. Current Psychiatry Reports, 18(9), 82. 10.1007/s11920-016-0723-0 [DOI] [PubMed] [Google Scholar]
  25. Li, Y. O., Adali, T., & Calhoun, V. D. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 28(11), 1251–1266. 10.1002/hbm.20359 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Liddell, B. J., Cheung, J., Outhred, T., Das, P., Malhi, G. S., Felmingham, K. L., Nickerson, A., Den, M., Askovic, M., Coello, M., Aroche, J., & Bryant, R. A. (2019). Neural correlates of posttraumatic stress disorder symptoms, trauma exposure, and postmigration stress in response to fear faces in resettled refugees. Clinical Psychological Science, 7(4), 811–825. 10.1177/2167702619841047 [DOI] [Google Scholar]
  27. Malhi, G. S., Das, P., Outhred, T., Bryant, R. A., & Calhoun, V. (2019). Resting-state neural network disturbances that underpin the emergence of emotional symptoms in adolescent girls: Resting-state fMRI study. The British Journal of Psychiatry: The Journal of Mental Science, 215(3), 545–551. 10.1192/bjp.2019.10 [DOI] [PubMed] [Google Scholar]
  28. Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends in Cognitive Sciences, 15(10), 483–506. 10.1016/j.tics.2011.08.003 [DOI] [PubMed] [Google Scholar]
  29. Miller, K. E., & Rasmussen, A. (2017). The mental health of civilians displaced by armed conflict: an ecological model of refugee distress. Epidemiology and Psychiatric Sciences, 26(2), 129–138. 10.1017/S2045796016000172 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Miller, R. L., Yaesoubi, M., Turner, J. A., Mathalon, D., Preda, A., Pearlson, G., Adali, T., & Calhoun, V. D. (2016). Higher dimensional meta-state analysis reveals reduced resting fMRI connectivity dynamism in schizophrenia patients. PLoS One, 11(3), e0149849. 10.1371/journal.pone [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Mollica, R., Caspi-Yavin, Y., Bollini, P., Truong, T., Tor, S., & Lavelle, J. (1992). The Harvard Trauma Questionnaire; validating a cross cultural instrument for measuring torture, trauma, and post-traumatic stress disorder in Indochina refugees. The Journal of Nervous and Mental Disease, 180(2), 111–116. 10.1097/00005053-199202000-00008 [DOI] [PubMed] [Google Scholar]
  32. Momartin, S., Steel, Z., Coello, M., Aroche, J., Silove, D., & Brooks, R. (2006). A comparison of the mental health of refugees with temporary versus permanent protection visas. Medical Journal of Australia, 185(7), 357–361. 10.5694/j.1326-5377.2006.tb00610.x [DOI] [PubMed] [Google Scholar]
  33. Newnham, E. A., Pearman, A., Olinga-Shannon, S., & Nickerson, A. (2019). The mental health effects of visa insecurity for refugees and people seeking asylum: a latent class analysis. International Journal of Public Health, 64(5), 763–772. 10.1007/s00038-019-01249-6 [DOI] [PubMed] [Google Scholar]
  34. Nickerson, A., Byrow, Y., O'Donnell, M., Mau, V., McMahon, T., Pajak, R., Li, S., Hamilton, A., Minihan, S., Liu, C., Bryant, R. A., Berle, D., Liddell, B. J. (2019). The association between visa insecurity and mental health, disability and social engagement in refugees living in Australia. European Journal of Psychotraumatology, 10(1), Article 1688129. 10.1080/20008198.2019.1688129 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Porter, M., & Haslam, N. (2005). Predisplacement and postdisplacement factors associated with mental health of refugees and internally displaced persons: A meta-analysis. JAMA, 294(5), 602–612. 10.1001/jama.294.5.602 [DOI] [PubMed] [Google Scholar]
  36. Saris, I. M. J., Penninx, B., Dinga, R., van Tol, M.-J., Veltman, D. J., van der Wee, N. J. A., & Aghajani, M. (2020). Default mode network connectivity and social dysfunction in major depressive disorder. Scientific Reports, 10(1), 194. 10.1038/s41598-019-57033-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Schick, M., Morina, N., Mistridis, P., Schnyder, U., Bryant, R. A., & Nickerson, A. (2018). Changes in post-migration living difficulties predict treatment outcome in traumatized refugees. Frontiers in Psychiatry, 9, 476. 10.3389/fpsyt.2018.00433 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., Hergueta, T., Baker, R., & Dunbar, G. C. (1998). The mini-international neuropsychiatric interview (M.I.N.I.): The development and validation of structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry, 59(Suppl 20), 22–33. [PubMed] [Google Scholar]
  39. Steel, Z., Chey, T., Silove, D., Marnane, C., Bryant, R. A., & von Ommeren, M. (2009). Association of torture and other potentially traumatic events with mental health outcomes among populations exposed to mass conflict and displacement. JAMA, 302(5), 537–549. 10.1001/jama.2009.1132 [DOI] [PubMed] [Google Scholar]
  40. Steel, Z., Momartin, S., Silove, D., Coello, M., Aroche, J., & Tay, K. W. (2011). Two year psychosocial and mental health outcomes for refugees subjected to restrictive or supportive immigration policies. Social Science & Medicine, 72(7), 1149–1156. 10.1016/j.socscimed.2011.02.007 [DOI] [PubMed] [Google Scholar]
  41. Steel, Z., Silove, D., Bird, K., McGorry, P., & Mohan, P. (1999). Pathways from war trauma to posttraumatic stress symptoms among Tamil asylum seekers, refugees, and immigrants. Journal of Traumatic Stress, 12(3), 421–435. 10.1023/A:1024710902534 [DOI] [PubMed] [Google Scholar]
  42. Turalska, M., Svenkeson, A., & West, B. J. (2019). Synchronization and anti-synchroniztion of dynamically coupled networks. New Journal of Physics, 21(3), 0033043. 10.1088/1367-2630/ab0991 [DOI] [Google Scholar]
  43. UNHCR . (2022). Global Trends Forced Displacement 2021. UNHCR. [Google Scholar]
  44. Van Dijk, K. R., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., & Buckner, R. L. (2010). Intrinsic functional connectivity as a tool for human connectomics: Theory, properties, and optimization. Journal of Neurophysiology, 103(1), 297–321. 10.1152/jn.00783.2009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Weissman, D. G., Conger, R. D., Robins, R. W., Hastings, P. D., & Guyer, A. E. (2018). Income change alters default mode network connectivity for adolescents in poverty. Developmental Cognitive Neuroscience, 30, 93–99. 10.1016/j.dcn.2018.01.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Yuen, N. H., Osachoff, N., & Chen, J. J. (2019). Intrinsic frequencies of the resting-state fMRI signal: The frequency dependence of functional connectivity and the effect of mode mixing. Frontiers in Neuroscience, 13, 900. 10.3389/fnins.2019.00900 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material
Click here for additional data file. (48.4KB, docx)

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author, BL. The data are not publicly available due to the de-identified data possibly containing information that could compromise the privacy and safety of the research participants.

Articles from European Journal of Psychotraumatology are provided here courtesy of Taylor & Francis

RESOURCES