Altered EEG patterns in individuals with disorganized attachment: an EEG microstates study

doi:10.21203/rs.3.rs-3006924/v1

Background: Over the past years, different studies provided preliminary evidence that Disorganized Attachment (DA) may have dysregulatory and disintegrative effects on both autonomic arousal regulation and brain connectivity. However, despite the clinical relevance of this construct, few studies have investigated the specific alterations underlying DA using electroencephalography (EEG). Thus, the main aim of the current study was to extend the scientific literature on the EEG microstates correlates of DA in a non-clinical sample (N= 50) before and after the administration of the Adult Attachment Interview (AAI).

Methods: Two EEG Resting State (RS) recordings were performed before and after the AAI. Microstates indices were then calculated using Cartool software.

Results: the Disorganized/Unrevolved (D/U) group showed a lower mean duration of map E and a higher occurrence of map F than the organized individuals. Then, an effect of time also emerged for the microstates indices. Finally, a positive and significant correlation between mean duration of map E post-AAI and coherence of mind was found as well as a negative and significant correlation with segmentation density of map F post-AAI.

Conclusion: our results showed significant differences in the EEG dynamic patterns of mean duration of map E and segmentation density of map F between groups, and a time effect reflecting disintegration mechanisms after retrieval of attachment memories.

Disorganized Attachment

AAI

EEG

Microstates Analysis

large scale brain networks

In accordance with Bowlby (1973), attachment can be defined as a close relationship between a child and his or her primary caregiver, which is intended to promote feelings of safety and security in the child. More specifically, in the attachment relationship, the caregiver functions as a secure base for the child to explore the environment and as a source of comfort and refuge in times of distress (Waters & Cummings, 2000). The repeated experiences with the caregiver represent the basis for the formation of Internal Working Models (IWMs), i.e., cognitive structures in which implicit memories of past interactions with the caregiver enable the child to infer the possible behaviours of the attachment figure in response to individual attachment needs (Fariborz et al., 1996; Liotti et al., 2008). Whereas in organized attachment, the child's request for support usually triggers a predictable and appropriate response from the caregiver, Disorganized Attachment (DA) refers to an incongruent emotional and behavioural responses or disorientation (Ainsworth et al., 1978; Granqvist et al., 2017). DA is present when, at the time of separation, the child seeks out the caregiver and simultaneously avoids reconciliation (Liotti et al., 2008). It is also possible that these behaviours are accompanied by disorientation, freezing, tense and arched body, and fleeting or lost gaze (Main & Solomon, 1990). In this context, the term ‘attachment trauma’ (AT) has been proposed to describe the most severe and enduring forms of DA (Farina et al., 2019; Isobel et al., 2019; Massullo et al., 2023). According to Solomon and George (2011), it represents a vulnerable condition in the child-caregiver relationship, in which parents fail in their parental responsibilities. Therefore, while DA could be seen as a typical emotional and behavioural response of a child to a certain unusual condition (i.e., strange situation; Granqvist et al., 2017), AT is of clinical significance (Isobel et al., 2019).

Over the past years, the use of brain imaging techniques improved our understanding of the effects of DA on brain and cognitive functioning. Specifically, different studies have provided evidence that DA can have disintegrative effects on both autonomic arousal regulation and brain connectivity (Farina et al., 2014; Massullo et al., 2022; Oosterman et al., 2010; Zingaretti et al., 2020). An increasingly popular approach to study the temporal dynamics of Resting-State Networks (RSNs) is the EEG microstate analysis. EEG microstates are defined as “global patterns of scalp potential topographies recorded using multichannel EEG arrays that dynamically vary over time in an organized manner” (Lehmann et al., 1987; Michel & Koenig, 2018, p. 2). Specifically, brain activity during rest can be described by a few dominant topographical map configurations. These maps remain stable for a limited period, approximately 60-120ms, before changing to a new configuration that remains stable again. EEG microstates are considered as short periods of synchronized activity of large-scale brain networks. Changes in the topographies over time indicates alternating activation of different RSNs (Michel & Koenig, 2018). In this regard, microstates analysis, which provides information about RSN dynamics, could be a useful tool to enhance the neurophysiological understanding of DA.

An experimental way to study DA in adults is the administration of the Adult Attachment Interview (AAI) that assesses the individual's current emotional state in relation to early attachment experiences (George et al., 1996). The AAI allows to classify healthy study participants as ‘Organised/Resolved (O/R)’ or ‘Disorganised/Unresolved (D/U)’ and to activate the attachment system (Dozier & Kobak, 1992; George et al., 1996; Massullo et al., 2022). We here used this experimental setup to investigate whether the EEG microstates dynamics is influenced by the administration of the AAI, and whether these effects differ between subjects classified as O/R and as D/U.

Participants

All participants were recruited from October 2017 to May 2018 at the university campus. The final sample of the present study consisted of 50 undergraduate students (female N = 36, 72%; male N = 14, 28%, mean age 22.69 ± 2.37 years) and overlapped with previous studies conducted within the same project (Adenzato et al., 2019; Massullo et al., 2022).

For the present study, the following inclusion criteria were used: i) age between 18 and 30 years; ii) clear and fluent understanding of the Italian language; iv) reading, understanding, and accepting written informed consent. Participants were excluded if they i) had a positive history of neurological or psychiatric disease and head trauma; ii) were left-handed; iii) had taken drugs with active effects on the central nervous system within the two weeks preceding theassessment.

The study was conducted in accordance with the Helsinki Declaration and approved by the ethics review board of the European University of Rome. All participants were fully informed of the study's objectives and provided written informed consent prior to their participation. They received no compensation for participating in the study.

Experimental procedure

A detailed research protocol was reported in Massullo et al., 2022. All participants were administered a checklist of inclusion and exclusion criteria, the Italian version of the Brief Symptom Inventory (BSI; De Leo et al., 1993), and then the Adult Attachment Interview (AAI) to assess the individual's current emotional state in relation to early attachment experiences (George et al., 1996). In this study, and in line with the scientific literature (Dozier & Kobak, 1992; George et al., 1996; Massullo et al., 2022), the AAI was used to classify study participants as ‘Organised/Resolved (O/R)’ or ‘Disorganised/Unresolved (D/U)’ and to activate the attachment system.

All participants underwent an initial 5-minute eyes-closed EEG RS recording (T0), followed by the AAI session (lasting approximately 1.30 hours), and finally a second eyes-closed EEG RS recording was acquired (T1). All experimental procedures was performed at the Cognitive and Clinical Psychology Laboratory (CCPL) of the European University of Rome. All recordings were carried out using the Micromed System Plus digital EEGraph (Micromed S.p.A., Mogliano Veneto, TV, Italy). More specifically, a 31-electrode headset was used, and the montage was set according to the 10–20 International System.

The same offline preprocessing methods were applied to all raw EEG data traces using EEGlab software (Delorme & Makeig, 2004). Firstly, a band-pass filter of 1–40 Hz was applied. Subsequently, the average reference was computed and then, a first visual inspection of the most noticeable and severe artifacts was carried out. Then, to remove the main electrical, muscular, and visual artifacts, the Independent Component Analysis (ICA) based on the infomax decomposition algorithm was applied to all EEG channels ("runica" tool of EEGLAB). Finally, a three-dimensional spherical spline interpolation was performed on the most artifact channels (Ferree, 2006).

Microstates analysis

For the present study, a k-means clustering approach was used to determine the optimal set of topographies explaining the EEG signal without considering the polarity of the maps (Brunet et al., 2011; Pascual-Marqui et al., 1995). To determine the optimal number of clusters, a meta-criterion combining differrent independent optimization criteria was used [Gamma (GAMMA), Point-Biserial (BISERIAL), Davies-Bouldin (DB), Dunn Robust (DUNNR), Krzanowski - Lai (KL), Silhouettes;] (Bréchet et al., 2019).

The clustering analysis was performed exclusively on data at time points where the local maximum of the Global Field Power (GFP) occurred, thereby enhancing the signal-to-noise ratio (Koenig et al., 2002; Pascual-Marqui et al., 1995). The GFP is a scalar measure of scalp potential field strength and is calculated as the standard deviation of all electrodes at a given time point (Michel et al., 1993). The clustering analysis was performed first at the individual level and then at the global level, including all study participants (i.e., O/R and U/D) for all conditions (i.e., T0 and T1).

The template maps derived from the cluster analysis across all subjects and recordings were back-fitted the the original data of each subject using a winner-takes all spatial correlation (Koenig et al., 2022). The following temporal smoothing parameters were used to prevent noise during low GFP from interrupting temporal segments of stable topography: window half size = 6, strength (Besag Factor) = 10 (Brunet et al., 2011). Then, according to previous studies (Damborská et al., 2019), three temporal parameters were calculated for each cluster map: occurrence (also known as segmentation density index), coverage, and duration. While occurrence refers to the number of times a microstate class recurs within a second, coverage represents the amount of time spent in a particular microstate class, and finally, duration represents the time during which a given microstate class is continuously present, measured in milliseconds. All analyses were performed using the Cartool Software 4.09 (Brunet et al., 2011).

Statistical Analysis

All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 24.0. Difference between groups in sociodemographic and clinical variables were calculated by means of the Kolmogorov and Smirnov z tests, and the chi-square (χ2) test (dichotomous variables). The use of nonparametric tests was chosen because several variables (e.g., the global severity index of BSI) were not normally distributed (i.e., Shapiro–Wilk test, p < 0.05).

To assess the effect of DA on the physiological indices of microstates (i.e., time coverage, mean duration, and segmentation density), an ANalysis of the COVAriance (ANCOVA) was performed. More specifically, the microstates indices of all maps at T1 (i.e, post AAI) were defined as the dependent variables and the group classification (O/R and D/U) as a between-subject factor. Then, all the microstates indices at T0 (i.e., pre AAI) were included as covariates. All ANCOVA tests for mean duration, time coverage, and segmentation density were performed separately. Variables that were not normally distributed were transformed to normality using the Rankit approximation (Bliss et al., 1956), which has been shown to be the most accurate method for normalising data in the social and behavioural sciences (Soloman & Sawilowsky, 2009). Finally, following Morris (2008), Cohen’s dppc2 was calculated to estimate effect size for the Group effect.

As a secondary analysis, a bivariate r-Pearson correlation was performed between the indices that showed a significant difference in ANCOVA tests and the clinical subscales of the AAI. More specifically, considering their importance in the secure/insecure state of mind (Riggs et al., 2007), the coherence of mind, unresolved trauma, and unresolved loss subscales were selected.

According to the AAI coding, 21 participants (23.62 ± 1.94 years; 14 females) were classified as D/U and 29 as O/R (21.90 ± 1.63; 22 females). There were no significant differences between groups (D/U vs. O/R) for sociodemographic and BSI-Global Severity Index (BSI-GSI; z test = .928; p = .355) variables.

EEG microstates clustering analysis using the meta-criterion identified 6 different maps (labeled as A, B, C, D, E, F) at both the individual and group levels as reported in Fig. 1. Table 2 shows the ANCOVA results for the indices of each microstate for each map. Only the mean duration of map E and the segmentation density of map F showed a significant difference between groups with values F (1;49) = 4.584; p = .037 and F (1;49) = 4.051; p = .049, respectively. Of relevance, a Time effect also emerged for the mean duration of map A, C, E, F, for the time coverage of map C, D, E, F, and for the segmentation density of map D and E. Finally, the correlation analysis (Table 3) showed a positive correlation between the mean duration of map E post AAI and the coherence of mind (r_p =. 354; p = .012). A negative and significant correlation with segmentation density of map F post-AAI also emerged (r_p = − .405, p = .004). Moreover, the coherence of mind subscale showed a significant and negative correlation with unresolved trauma (r_p = − .364; p =. 009). No other significant correlations were found.

To our knowledge, this is the first study investigating the brain dynamics in subjects with DA using microstate analysis. Consistent with previous studies (Damborská et al., 2019), three indices were used: mean duration, time coverage, and segmentation density of microstates. Our results showed that microstates are sensitive in detecting significant differences between individuals with DA and individuals with organized attachment after an AAI session. In particular, EEG dynamics showed a significant difference between the two groups in the mean duration of map E and the segmentation density of map F. In addition, a significant Time effect emerged for the mean duration of map A, C, E, F, for the time coverage of map C, D, E, F, and for the segmentation density of map D and E. Finally, a positive correlation emerged between the mean duration of map E post AAI and the coherence of mind. On the other hand, the segmentation density of map F post AAI was negatively correlated with the mean duration of the map E post AAI, as was the coherence of mind, which also showed a negative correlation with the unresolved trauma subscale.

To date, few studies have described the clinical and functional characteristics of microstate E (Bréchet et al., 2019; Custo et al., 2017; D’Croz-Baron et al., 2019; Serrano et al., 2018; Wei et al., 2018). According to a recent work on a sample of individuals with Autism Spectrum Disorder (ASD) and control subjects, a significant difference was found between the two groups in the duration and occurrence of this map (D’Croz-Baron et al., 2019). More specifically, as in our case, the ASD group had a lower presence and occurrence of map E compared to controls. Similarly, Terpou et al. (2022) showed that individuals with Post Traumatic Stress Disorder had a shorter duration and lower occurrence of map E compared with the control group.

Previous studies suggested that microstate E is associated with regions of the Salience Network (SN) such as the insula, Anterior Cingulate Cortex (ACC), and Inferior Frontal Gyrus (IFG) (Bréchet et al., 2019; Custo et al., 2017). This network plays a crucial role in the integration and filtering of interoceptively and emotionally relevant information (Menon, 2011). Furthermore, the crucial node of this network is the insula, which is mainly involved in the selection of both internal and external relevant stimuli (Menon, 2011). In this line, according to previous work by Adenzato et al. (2019) and Massullo et al. (2022), it is possible that retrieval of attachment memories can increase brain arousal in individuals with D/U attachment (Parlar et al., 2018). In addition, a large number of studies have demonstrated the role of ACC in the processing and regulation of cognitive and emotional stimuli (Bush et al., 2000; Stevens et al., 2011), with specific deficits shown in individuals with insecure or DA (Brumariu, 2015; Forslund et al., 2016). Therefore, it is possible that the shorter mean duration of map E reflects these clinical patterns in our sample. Finally, there was a positive correlation between the duration of map E and the subscale of the AAI coherence of mind. Thus, one could speculate that a lower mean duration of map E could be considered a neurophysiological underpinning of the coherence symptoms of disorganized attachment.

Conversely, an increase in occurrence levels of map F has been found in D/U individuals. To date, few studies have discussed the clinical relevance of this map. According to previous reports (Pipinis et al., 2017; Tarailis et al., 2021; Zanesco et al., 2021), a negative correlation between the time coverage of map F and the somatic awareness subscale of the Amsterdam Resting State Questionnaire was found, and an increase in occurrence during propofol-induced anaesthesia for surgery was also reported (Liu et al., 2022; Shi et al., 2020). However, Custo et al. (2017) reported that map F partially reflects activation of some regions of Default Mode Network (DMN) such as the medial prefrontal cortex. Specifically, the DMN is a large-scale network consisting of regions that are synchronically active at rest or when subjects are not involved in performing specific tasks. This network would be involved in various processes such as mentalization, autobiographical memory, future episodic thought, and moral decision (Buckner et al., 2008).

Similar to our findings, specific DMN-related alterations have also been found in individuals with dysfunctional parenting following activation of attachment memories. In particular, a recent work reported a reduction in connectivity measure between some regions of the DMN such as the left temporoparietal junction and the right AAC in the theta frequency band (Adenzato et al., 2019). The authors suggested that the reduction in DMN connectivity observed in individuals with dysfunctional parenting experiences could be indicative of an impaired ability to mentalize their interactions with important individuals, which could be attributed to the activation of early attachment memories. In this sense, our results seem to be consistent with previous studies that revealed changes in the functional connectivity of the DMN in individuals with childhood trauma experiences (Dauvermann et al., 2021; Wang et al., 2022). Moreover, based on the negative correlation with the mean duration of map E, it can be assumed that a higher level of segmentation density of map F might reflect a typical neurophysiological pattern of DA.

Finally, an effect of the time also emerged between the two-time conditions (i.e., pre T0 and post T1 AAI). Taken together, these results, besides confirming what was previously discussed for maps E and F, would seem to report an effect of AAI over time indifferently from groups. For example, an increase in T1 was reported for the mean duration of map A. Accordingly to recent studies, it has been shown that this map is often associated with the activation of the auditory network (Bréchet et al., 2019; Britz et al., 2010; Tarailis et al., 2023). In addition, recent works have also reported the presence of this map with an increase in arousal levels (Antonova et al., 2022; Ke et al., 2021). Therefore, the present results would be in line with previous literature about the activating effects following the AAI administration (Adenzato et al., 2019; Dozier & Kobak, 1992; George et al., 1996).

Despite the innovative aspects of this study, some limitations should be noted. First, this study was conducted in a population of university students, and it would therefore be interesting to extend these findings in a clinical sample (e.g., psychiatric patients with D/U). In addition, future studies should replicate our reported observations in a different and larger group of participants to test the replicability of our data.

Notwithstanding the limitations, this is the first study to investigate the dynamics of RS large-scale brain networks activity in individuals with DA using EEG microstate analysis. According to our results, it is possible to speculate that microstates may be sensitive in detecting significant differences between individuals with DA and controls after an AAI session, providing a potential neurophysiological signature for DA in non-clinical sample.

Ethical Approval

All participants provided their written informed consent. The study was conducted according to the Helsinki declaration standards and the research project was approved by the European University’s ethics review board.

Informed consent

All subjects gave their written informed consent to take part to the study and to have the results of the study published in scientifc articles.

Competing interests

The authors have no relevant fnancial or non-fnancial interests to disclose.

Authors' contributions

Study concept and design: GAC, CMM, BF, MA, RBA, CI, FA;

(ii) Acquisition of subjects and/or data: GAC, BF, MA, RBA, CI;

(iii) Analysis and interpretation of the data GAC, CMM, CI, FA;

(iv) writing—original draft: GAC, CMM, CI, FA;

(v) writing—review and editing: BF, MA, RBA.

All authors read and approved the final manuscript.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript

Data availability

Aggregated data may be available from the corresponding author upon reasonable request.

Adenzato, M., Imperatori, C., Ardito, R. B., Valenti, E. M., Della Marca, G., D’Ari, S., Palmiero, L., Penso, J. S., & Farina, B. (2019). Activating attachment memories affects default mode network in a non-clinical sample with perceived dysfunctional parenting: An EEG functional connectivity study. Behavioural brain research, 372, 112059.
Ainsworth, M., Blehar, M., Waters, E., & Wall, S. (1978). Patterns of Attachment. Hillsdale, NJ: Lawrence Eribaum Associates. Inc., Publishers, 144-153.
Antonova, E., Holding, M., Suen, H. C., Sumich, A., Maex, R., & Nehaniv, C. (2022). EEG microstates: Functional significance and short-term test-retest reliability. Neuroimage: Reports, 2(2), 100089.
Beilharz, J. E., Paterson, M., Fatt, S., Wilson, C., Burton, A., Cvejic, E., Lloyd, A., & Vollmer-Conna, U. (2020). The impact of childhood trauma on psychosocial functioning and physical health in a non-clinical community sample of young adults. Australian & New Zealand Journal of Psychiatry, 54(2), 185-194.
Bowlby, J. (1973). Attachment and loss: Volume II: Separation, anxiety and anger. In Attachment and loss: Volume II: Separation, anxiety and anger (pp. 1-429). London: The Hogarth press and the institute of psycho-analysis.
Bréchet, L., Brunet, D., Birot, G., Gruetter, R., Michel, C. M., & Jorge, J. (2019). Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI. Neuroimage, 194, 82-92.
Britz, J., Van De Ville, D., & Michel, C. M. (2010). BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage, 52(4), 1162-1170.
Brumariu, L. E. (2015). Parent–child attachment and emotion regulation. New directions for child and adolescent development, 2015(148), 31-45.
Brunet, D., Murray, M. M., & Michel, C. M. (2011). Spatiotemporal analysis of multichannel EEG: CARTOOL. Computational intelligence and neuroscience, 2011, 1-15.
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008). The brain's default network: anatomy, function, and relevance to disease. Annals of the new York Academy of Sciences, 1124(1), 1-38.
Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in cognitive sciences, 4(6), 215-222.
Custo, A., Van De Ville, D., Wells, W. M., Tomescu, M. I., Brunet, D., & Michel, C. M. (2017). Electroencephalographic resting-state networks: source localization of microstates. Brain connectivity, 7(10), 671-682.
D’Croz-Baron, D. F., Baker, M., Michel, C. M., & Karp, T. (2019). EEG microstates analysis in young adults with autism spectrum disorder during resting-state. Frontiers in human neuroscience, 13, 173.
Damborská, A., Tomescu, M. I., Honzírková, E., Barteček, R., Hořínková, J., Fedorová, S., Ondruš, Š., & Michel, C. M. (2019). EEG resting-state large-scale brain network dynamics are related to depressive symptoms. Frontiers in psychiatry, 10, 548.
Dauvermann, M. R., Mothersill, D., Rokita, K. I., King, S., Holleran, L., Kane, R., McKernan, D. P., Kelly, J. P., Morris, D. W., & Corvin, A. (2021). Changes in default-mode network associated with childhood trauma in schizophrenia. Schizophrenia bulletin, 47(5), 1482-1494.
De Leo, D., Frisoni, G. B., Rozzini, R., & Trabucchi, M. (1993). Italian community norms for the Brief Symptom Inventory in the elderly. British journal of clinical psychology, 32(2), 209-213.
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9-21.
Dozier, M., & Kobak, R. R. (1992). Psychophysiology in attachment interviews: Converging evidence for deactivating strategies. Child development, 63(6), 1473-1480.
Fariborz, A., Thomas, L., Richard, L., Alan, L., Gordon, B., Teresa, M., & Schiff, E. Z. (1996). Affect, attachment, memory: Contributions toward psychobiologic integration. Psychiatry, 59(3), 213-239.
Farina, B., Liotti, M., & Imperatori, C. (2019). The role of attachment trauma and disintegrative pathogenic processes in the traumatic-dissociative dimension. Frontiers in psychology, 933.
Farina, B., Speranza, A. M., Dittoni, S., Gnoni, V., Trentini, C., Vergano, C. M., Liotti, G., Brunetti, R., Testani, E., & Marca, G. D. (2014). Memories of attachment hamper EEG cortical connectivity in dissociative patients. European Archives of Psychiatry and Clinical Neuroscience, 264, 449-458.
Ferree, T. C. (2006). Spherical splines and average referencing in scalp electroencephalography. Brain topography, 19, 43-52.
Forslund, T., Brocki, K. C., Bohlin, G., Granqvist, P., & Eninger, L. (2016). The heterogeneity of attention‐deficit/hyperactivity disorder symptoms and conduct problems: Cognitive inhibition, emotion regulation, emotionality, and disorganized attachment. British Journal of Developmental Psychology, 34(3), 371-387.
George, C., Kaplan, N., & Main, M. (1996). Adult attachment interview. (U.o.C.a. Berkeley, ed 3.).
Granqvist, P., Sroufe, L. A., Dozier, M., Hesse, E., Steele, M., van Ijzendoorn, M., Solomon, J., Schuengel, C., Fearon, P., & Bakermans-Kranenburg, M. (2017). Disorganized attachment in infancy: A review of the phenomenon and its implications for clinicians and policy-makers. Attachment & human development, 19(6), 534-558.
Isobel, S., Goodyear, M., & Foster, K. (2019). Psychological trauma in the context of familial relationships: A concept analysis. Trauma, Violence, & Abuse, 20(4), 549-559.
Ke, M., Li, J., & Wang, L. (2021). Alteration in resting-state EEG microstates following 24 hours of total sleep deprivation in healthy young male subjects. Frontiers in human neuroscience, 15, 636252.
Koenig, T., Prichep, L., Lehmann, D., Sosa, P. V., Braeker, E., Kleinlogel, H., Isenhart, R., & John, E. R. (2002). Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. Neuroimage, 16(1), 41-48.
Lehmann, D., Ozaki, H., & Pal, I. (1987). EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalography and clinical neurophysiology, 67(3), 271-288.
Liotti, G., Cortina, M., & Farina, B. (2008). Attachment theory and multiple integrated treatments of borderline patients. Journal of the American Academy of psychoanalysis and Dynamic Psychiatry, 36(2), 295-315.
Liu, Z., Si, L., Xu, W., Zhang, K., Wang, Q., Chen, B., & Wang, G. (2022). Characteristics of EEG microstate sequences during propofol-induced alterations of brain consciousness states. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 1631-1641.
Main, M., & Solomon, J. (1990). Procedures for identifying infants as disorganized/disoriented during the Ainsworth Strange Situation. Attachment in the preschool years: Theory, research, and intervention, 1, 121-160.
Massullo, C., De Rossi, E., Carbone, G. A., Imperatori, C., Ardito, R. B., Adenzato, M., & Farina, B. (2023). Child maltreatment, abuse, and neglect: An umbrella review of their prevalence and definitions. Clinical Neuropsychiatry, 20(2), 72.
Massullo, C., Imperatori, C., de Vico Fallani, F., Ardito, R. B., Adenzato, M., Palmiero, L., Carbone, G. A., & Farina, B. (2022). Decreased brain network global efficiency after attachment memories retrieval in individuals with unresolved/disorganized attachment-related state of mind. Scientific Reports, 12(1), 4725.
Menon, V. (2011). Large-scale brain networks and psychopathology: a unifying triple network model. Trends in cognitive sciences, 15(10), 483-506.
Michel, C., Brandeis, D., Skrandies, W., Pascual, R., Strik, W., Dierks, T., Hamburger, H., & Karniski, W. (1993). Global field power: A" time-honoured" index for EEG/EP map analysis.
Michel, C. M., & Koenig, T. (2018). EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review. Neuroimage, 180, 577-593.
Morris, S. B. (2008). Estimating effect sizes from pretest-posttest-control group designs. Organizational research methods, 11(2), 364-386.
Oosterman, M., De Schipper, J. C., Fisher, P., Dozier, M., & Schuengel, C. (2010). Autonomic reactivity in relation to attachment and early adversity among foster children. Development and psychopathology, 22(1), 109-118.
Parlar, M., Densmore, M., Hall, G. B., Lanius, R., & McKinnon, M. C. (2018). Neural and behavioural correlates of autobiographical memory retrieval in patients with major depressive disorder and a history of trauma exposure. Neuropsychologia, 110, 148-158.
Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1995). Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Transactions on Biomedical Engineering, 42(7), 658-665.
Pipinis, E., Melynyte, S., Koenig, T., Jarutyte, L., Linkenkaer-Hansen, K., Ruksenas, O., & Griskova-Bulanova, I. (2017). Association between resting-state microstates and ratings on the Amsterdam resting-state questionnaire. Brain topography, 30, 245-248.
Serrano, J. I., Del Castillo, M. D., Cortés, V., Mendes, N., Arroyo, A., Andreo, J., Rocon, E., Del Valle, M., Herreros, J., & Romero, J. P. (2018). EEG microstates change in response to increase in dopaminergic stimulation in typical Parkinson’s disease patients. Frontiers in neuroscience, 12, 714.
Shi, W., Li, Y., Liu, Z., Li, J., Wang, Q., Yan, X., & Wang, G. (2020). Non-canonical microstate becomes salient in high density EEG during propofol-induced altered states of consciousness. International Journal of Neural Systems, 30(02), 2050005.
Soloman, S. R., & Sawilowsky, S. S. (2009). Impact of rank-based normalizing transformations on the accuracy of test scores. Journal of Modern Applied Statistical Methods, 8(2), 9.
Solomon, J., & George, C. (2011). Disorganized attachment and caregiving. Guilford Press.
Stevens, F. L., Hurley, R. A., & Taber, K. H. (2011). Anterior cingulate cortex: unique role in cognition and emotion. The Journal of neuropsychiatry and clinical neurosciences, 23(2), 121-125.
Tarailis, P., Koenig, T., Michel, C. M., & Griškova-Bulanova, I. (2023). The functional aspects of resting EEG microstates: A Systematic Review. Brain topography, 1-37.
Tarailis, P., Šimkutė, D., Koenig, T., & Griškova-Bulanova, I. (2021). Relationship between spatiotemporal dynamics of the brain at rest and self-reported spontaneous thoughts: an EEG microstate approach. Journal of personalized medicine, 11(11), 1216.
Terpou, B. A., Shaw, S. B., Théberge, J., Férat, V., Michel, C. M., McKinnon, M. C., Lanius, R. A., & Ros, T. (2022). Spectral decomposition of EEG microstates in post-traumatic stress disorder. NeuroImage: Clinical, 35, 103135.
Wang, X., Liu, Q., Fan, J., Gao, F., Xia, J., Liu, X., Du, H., Liao, H., Tan, C., & Zhu, X. (2022). Decreased functional coupling within default mode network in major depressive disorder with childhood trauma. Journal of psychiatric research, 154, 61-70.
Waters, E., & Cummings, E. M. (2000). A secure base from which to explore close relationships. Child development, 71(1), 164-172.
Wei, Y., Ramautar, J. R., Colombo, M. A., Te Lindert, B. H., & Van Someren, E. J. (2018). EEG microstates indicate heightened somatic awareness in insomnia: toward objective assessment of subjective mental content. Frontiers in psychiatry, 9, 395.
Zanesco, A. P., Denkova, E., & Jha, A. P. (2021). Associations between self-reported spontaneous thought and temporal sequences of EEG microstates. Brain and cognition, 150, 105696.
Zingaretti, P., Giovanardi, G., Cruciani, G., Lingiardi, V., Ottaviani, C., & Spitoni, G. F. (2020). Heart rate variability in response to the recall of attachment memories. Attachment & human development, 22(6), 643-652.

Table 1. Clinical and sociodemographic descriptive measures and, differences between groups

Variables	D/U (N = 21)	O/R (N = 29)	Test	p
Age M ± SD	21.90 ± 1.63	23.62 ± 2.94	Z test = 1.043	.227
Females N - %	22 (75.86%)	14 (66.66%)	χ2 = .511	.475
Education (years) - M ± SD	15.24 ± 1.88	16.00 ± 1.52	Z test = .797	.550
GSI-BSI M ± SD	.68 ± .75	.72 ± .63	Z test = .928	.355
Clinical and sociodemographic variables of groups. M mean, SD standard deviation, O/R organized state of mind in relation to attachment, U/D unresolved/disorganized state of mind in relation to attachment, GSI-BSI Global Severity Index of the Brief Symptoms inventory;

Table 2 A. ANCOVA results table of Mean duration

Variable	Time	OG (N = 29) M ± SD	DG (N = 21) M ± SD	Test statistics	Cohen’s dppc2
Mean duration Map A	T0	27.354 ± 5.232	27.195 ± 4.418	*F_Time(1;49) = 6.752; p = .012*	.071
	T1	27.150 ± 4.347	26.635 ± 3.253	F_Group (1;49) = .271; p = .605	.071
Mean duration Map B	T0	26,124 ± 4.644	25.377 ± 2.847	F_Time(1;49) = 1.457; p = .233	.025
	T1	25.866 ± 5.426	26.134 ± 3.062	F_Group (1;49) = .819; p = .370	.025
Mean duration Map C	T0	23.783 ± 4.294	23.970 ± 4.101	F_Time(1;49) = 7.450; p = .009	.287
	T1	24.192 ± 5.050	23.151 ± 3.224	F_Group (1;49) = 1.018; p = .318	.287
Mean duration Map D	T0	22.918 ± 4.651	23.646 ± 4.063	F_Time(1;49) = .689; p = .411	.265
	T1	23.799 ± 6.038	23.339 ± 3.466	F_Group (1;49) = .020; p = .887	.265
Mean duration Map E	T0	24.886 ± 4.279	22.752 ± 3.764	F_Time(1;49) = 5.524; p = 0.23	.380
	T1	25.873 ± 6.801	22.167 ± 3.427	*F_Group (1;49) = 4.584; p =* .037**	.380
Mean duration Map F	T0	37.279 ± 14.973	32.379 ± 5.995	F_Time(1;49) = 4.162; p = .047	.422
	T1	35.043 ± 9.305	35.322 ± 6.991	F_Group (1;49) = .300; p = .587	.422
ANCOVAs results for Mean duration. Significant results are reported in bold. M mean, SD standard deviation, O/R organized/resolved group, U/D unresolved/disorganized group, T0 pre–Adult Attachment Interview, T1 post Adult Attachment Interview.

Table 2 B. ANCOVA results table for Time coverage
Variable	Time	OG (N = 29) M ± SD	DG (N = 21) M ± SD	Test statistics	Cohen’s dppc2
Time coverage Map A	T0	18.818 ± 8.387	20.183 ± 9.165	F_Time(1; 49) = .611; p = .438	.131
	T1	18.465 ± 7.236	18.672 ± 7.000	F_Group (1;49) = .002; p = .969	.131
Time coverage Map B	T0	15.491 ± 6.911	16.122 ± 6.754	F_Time(1; 49) = 1.677; p = .202	.161
	T1	14.967 ± 6.154	16.719 ± 6.092	F_Group (1;49) = .890; p = .350	.161
Time coverage Map C	T0	11.153 ± 6.580	13.594 ± 9.385	*F_Time(1; 49) = 4.620; p =* .037**	.468
	T1	12.176 ± 10.034	10.872 ± 5.930	F_Group (1;49) = .309; p = .581	.468
Time coverage Map D	T0	8.471 ± 5.352	10.866 ± 8.183	F_Time(1; 49) =4.291; p = .044	.195
	T1	8.469 ± 5.213	9.544 ± 6.229	F_Group (1;49) = .002; p = .961	.195
Time coverage Map E	T0	10.976 ± 7.119	8.561 ± 6.861	F_Time(1; 49) = 10.692; p = .002	.417
	T1	12.724 ± 10.045	7.341 ± 5.412	F_Group (1;49) = 2.056; p = .158	.417
Time coverage Map F	T0	35.142 ± 16.565	30.671 ± 12.641	F_Time(1; 49) = 4.468; p = .040	.531
	T1	33.196 ± 14.996	36.849 ± 12.952	F_Group (1;49) = 1.521; p = .224	.531
ANCOVAs results for Time coverage. M mean, SD standard deviation, O/R organized/resolved group, U/D unresolved/disorganized group, T0 pre–Adult Attachment Interview, T1 post Adult Attachment Interview.

Table 2 C. ANCOVA result table for Segmentation density
Variable	Time	OG (N = 29) M ± SD	DG (N = 21) M ± SD	Test statistics	Cohen’s dppc2
Segmentation density Map A	T0	4.058 ± 1.267	4.300 ± 1.082	F_Time (1;49) = .436; p = .512	.129
	T1	4.074 ± 1.194	4.159 ± 1.076	F_Group (1;49) = .004; p = .948	.129
Segmentation density Map B	T0	3.573 ± 1.186	3.778 ± 1.150	F_Time (1;49) = 1.131; p = .293	.096
	T1	3.517 ± 1.117	3.836 ± 1.053	F_Group (1;49) = .854; p = .360	.096
Segmentation density Map C	T0	2.847 ± 1.270	3.218 ± 1.304	F_Time (1;49) = 3.658; p = .062	.385
	T1	2.889 ± 1.372	2.758 ± 1.000	F_Group (1;49) = .327; p = .629	.385
Segmentation density Map D	T0	2.213 ± 1.210	2.673 ± 1.442	F_Time (1;49) = 6.043; p = .018	.140
	T1	2.188 ± 1.031	2.462 ± 1.277	F_Group (1;49) = .194; p = .662	.140
Segmentation density Map E	T0	2.590 ± 1.393	2.160 ± 1.446	F_Time (1;49) = 8.552; p = .005	.288
	T1	2.790 ± 1.546	1.944 ± 1.096	F_Group (1;49) = 3.374; p = .073	.288
Segmentation density Map F	T0	5.132 ± 1.416	5.241 ± 1.223	F_Time (1;49) = 3.689; p = .061	.284
	T1	5.114 ± 1.307	5.600 ± 1.098	F_Group (1;49) = 4.051; p = .049	.284
ANCOVAs results for Segmentation density. Significant results are reported in bold. M mean, SD standard deviation, O/R organized/resolved group, U/D unresolved/disorganized group, T0 pre–Adult Attachment Interview, T1 post Adult Attachment Interview.

Table 3. Values of Pearson's r correlation coefficient

Variables	Post Mean Duration Map E	Post Segmentation Density Map F	Unresolved Loss	Unresolved Trauma	Coherence of Mind
Post-AAI Mean Duration Map E	-	-.405**	-.265	-.207	.354*
Post- AAI Segmentation Density Map F		-	.086	.238	-.221
Unresolved Loss			-	-.106	-.364**
Unresolved Trauma				-	-.248
Coherence of Mind					-

No competing interests reported.

Altered EEG patterns in individuals with disorganized attachment: an EEG microstates study

Status:

Journal Publication

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Abstract

Figures

Introduction

Materials and method

Participants

Experimental procedure

Microstates analysis

Statistical Analysis

Results

Discussion

Declarations

References

Tables

Additional Declarations

Status:

Journal Publication

Version 1