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Abstract

Since its discovery in 1997, the default mode network (DMN) and its components have been extensively studied in both healthy individuals and psychiatric patients. Several studies have investigated possible DMN alterations in specific mental conditions such as bipolar disorder (BD). In this review, we describe current evidence from resting-state functional magnetic resonance imaging studies with the aim to understand possible changes in the functioning of the DMN in BD. Overall, several types of analyses including seed-based and independent component have been conducted on heterogeneous groups of patients highlighting different results. Despite the differences, findings seem to indicate that BD is associated with alterations in both frontal and posterior DMN structures, mainly in the prefrontal, posterior cingulate and inferior parietal cortices. We conclude this review by suggesting possible future research directions.

Footnotes

*

Both authors contributed equally to this work.

Introduction

Bipolar disorder (BD) is a complex mental disorder characterised by severe mood fluctuations associated with functional and cognitive disabilities that often persist throughout the entire life of patients (Solé et al., Reference Solé, Jiménez, Torrent, Reinares, Bonnin, Torres, Varo, Grande, Valls, Salagre, Sanchez-Moreno, Martinez-Aran, Carvalho and Vieta2017). Specifically, BD is expressed with alternating phases of (i) mania, including abnormally elevated mood and increased energy levels and (ii) depression, consisting of severely depressed mood, anhedonia and insomnia/hypersomnia. Moreover, patients with BD show various degrees of neuro-cognitive impairments affecting several domains such as working memory, attention and executive control, all impacting on socio-occupational functioning (Robinson et al., Reference Robinson, Thompson, Gallagher, Goswami, Young, Ferrier and Moore2006).

Considering the neurobiological nature of BD, it is crucial to study the structural and functional brain alterations associated with the disease, in order to provide novel insights for future neurobiological models and treatments. To date, however, neurobiological underpinnings of the disorder are still discussed, hindering the development of new pharmacological therapies and targeted rehabilitative interventions. Findings on brain structure in BD suggest ventricular, prefrontal and temporal abnormalities but remain largely inconsistent (Delvecchio et al., Reference Delvecchio, Maggioni, Squarcina, Arighi, Galimberti, Scarpini, Bellani and Brambilla2020). The most consistent findings on brain function in BD concern brain activation patterns during emotion processing, emotion regulation and reward processing tasks. Overall, BD seems to be marked by a generalised dysfunction of the dorsal and ventral systems, composed respectively of (i) the prefrontal cortex (PFC), anterior cingulate cortex (ACC) and the hippocampus and (ii) the insula, amygdala and ventral striatum (Chen et al., Reference Chen, Suckling, Lennox, Ooi and Bullmore2011; Phillips and Swartz, Reference Phillips and Swartz2014). However, it is not clear whether the aforementioned networks are altered only during the execution of specific tasks or also at rest (Bellani et al., Reference Bellani, Bontempi, Zovetti, Gloria Rossetti, Perlini, Dusi, Squarcina, Marinelli, Zoccatelli, Alessandrini, Francesca Maria Ciceri, Sbarbati and Brambilla2020).

Recently, greater attention has been given to the investigation of brain activity and connectivity in BD at rest, mostly by means of resting-state functional magnetic resonance imaging (rs-fMRI). Particularly, rs-fMRI is used to evaluate the functional interactions among remote neural systems that occur in a resting or task-negative condition, i.e. when an explicit task is not being performed. This technique provides information about intrinsic brain activity, not dependent on specific tasks or experimental settings. Most commonly, the brain functional connectivity (FC) at rest is studied by analysing the statistical dependencies among the blood oxygenation level-dependent (BOLD) signals from multiple brain regions, which reflect the spontaneous fluctuations in the oxygenation state of blood (determined by blood flow, blood volume and oxygen consumption) within them (Smitha et al., Reference Smitha, Akhil Raja, Arun, Rajesh, Thomas, Kapilamoorthy and Kesavadas2017).

The physiological substrates of resting-state activity are currently the object of speculation (Rosazza and Minati, Reference Rosazza and Minati2011). By contrast, functional connectivity (FC) at rest (i.e. the statistical dependence among spatially remote neuronal systems (Friston et al., Reference Friston, Frith, Liddle and Frackowiak1993)) is suggested to be not a simple ‘background’ activity but a complex dynamic involved in cognition and memory consolidation (Rosazza and Minati, Reference Rosazza and Minati2011) consuming more than 20% of body's energy supporting neuronal signalling and functioning (Raichle and Mintun, Reference Raichle and Mintun2006).

Rs-fMRI networks can be studied employing different analysis pipelines that primarily fall in two main categories, (1) seed-based analyses (SBA), which require a-priori definition of the brain network nodes, (2) blind-source separation techniques, which adopt an unbiased approach by grouping brain voxels/regions based on their latent time series (independent component analysis, ICA) (Joel et al., Reference Joel, Caffo, van Zijl and Pekar2011). For details on these approaches see Smitha et al. (Reference Smitha, Akhil Raja, Arun, Rajesh, Thomas, Kapilamoorthy and Kesavadas2017).

These techniques have enabled the discovery of multiple brain networks characterised by highly correlated spontaneous BOLD fluctuations. The most commonly studied networks include (i) the Salience Network (SN), composed of the ACC and the insula; (ii) the central-executive network (CEN) consisting of fronto-parietal regions and the posterior parietal cortex; (iii) the fronto-parietal network (FPN) that includes the cortex surrounding the intraparietal sulcus, the inferior parietal lobe, the dorsal motor cortex and the inferior frontal cortex; and (iv) the default-mode network (DMN) extending from the prefrontal medial cortex (mPFC) to the precuneus, posterior cingulate cortex (PCC) and inferior parietal cortex. The DMN is recognised as a key component of the brain functional architecture. The vmPFC composes the anterior part of the DMN and has been shown to modulate social behaviour, mood regulation, executive functioning and control processes (Damasio et al., Reference Damasio, Grabowski, Frank, Galaburda and Damasio1994). The PCC, the precuneus and the lateral parietal cortices, which form the posterior DMN, are involved respectively in attentional regulation, consciousness, mental imagery and episodic memory processes (Cavanna, Reference Cavanna2007; Leech and Sharp, Reference Leech and Sharp2013). All these functions have been found altered (at different extents) in psychotic disorders and BD (Clark et al., Reference Clark, Iversen and Goodwin2002; Torres et al., Reference Torres, Boudreau and Yatham2007). Moreover, the precuneus has been recently suggested to be part of a hippocampal-parietal network involved in learning and consolidation of everyday experiences. The global DMN is involved in multiple cognitive and affective functions such as emotional processing, self-referential mental activity, mind wandering, recollection of experiences and possibly exerts a modulatory role during attentional demanding tasks (Raichle, Reference Raichle2015). Since attentional deficits are a core feature of BD (Solé et al., Reference Solé, Jiménez, Torrent, Reinares, Bonnin, Torres, Varo, Grande, Valls, Salagre, Sanchez-Moreno, Martinez-Aran, Carvalho and Vieta2017), studying the DMN in patients with BD might prove particularly beneficial to help to understand the neurobiological underpinnings of the disease.

Considering the potential role of the DMN in BD, the aim of this review is to describe current evidence of DMN connectivity in patients with BD.

The data search was conducted on the Pubmed, Scopus and Google Scholar databases. The following keywords were used for the search: ‘default mode network’ AND ‘bipolar disorder’. The inclusion criteria were: (i) original publication in a peer-reviewed journal between 2010 and 2020, (ii) English language, (iii) a diagnosis of BD for the patient groups and (iv) the analysis of the DMN activity patterns through rs-fMRI. After an initial screening 71 studies were identified of which 23 studies were included in the review.

Table 1 provides a description of methods and results of the included studies. In detail, 11 studies (47%) analysed the rs-fMRI activity through ICA, providing thus an unbiased overview of brain activity at rest as a whole (Joel et al., Reference Joel, Caffo, van Zijl and Pekar2011), while ten studies (43%) adopted an SBA approach, which requires a-priori hypotheses on the regions of interest within the brain (Smitha et al., Reference Smitha, Akhil Raja, Arun, Rajesh, Thomas, Kapilamoorthy and Kesavadas2017). Finally, the remaining two studies analysed the rs-fMRI activity through a network analysis (Wang et al., Reference Wang, Zhong, Jia, Sun, Wang, Liu, Pan and Huang2016) and the Data Processing Assistant for Resting-State (DPARSF) analysis pipeline, a MATLAB toolbox based on the SBA principles which gives information about regional FC and homogeneity (Brady et al., Reference Brady, Tandon, Masters, Margolis, Cohen, Keshavan and Öngür2017). Only six out of 23 studies enrolled unmedicated BD patients. Yet, only four out of 23 studies recruited mixed samples of BD patients based on the subtypes (i.e. BD-1, BD-2) or the clinical phases of the disorder (e.g. depression, mania, euthymia) (Martino et al., Reference Martino, Magioncalda, Huang, Conio, Piaggio, Duncan, Rocchi, Escelsior, Marozzi, Wolff, Inglese, Amore and Northoff2016; Rive et al., Reference Rive, Redlich, Schmaal, Marquand, Dannlowski, Grotegerd, Veltman, Schene and Ruhé2016; Brady et al., Reference Brady, Tandon, Masters, Margolis, Cohen, Keshavan and Öngür2017; Zhong et al., Reference Zhong, Wang, Gao, Xiao, Lu, Jiao, Su and Lu2019).

Table 1. Studies exploring the DMN with rs-fMRI in BD

↑, increased; ↓, decreased; ALFF, amplitude low frequency fluctuations; BD, bipolar disorder patients; BD-1, bipolar disorder type 1; BD-2, bipolar disorder type 2; dlPFC, dorsolateral PFC; DMN, default mode network; DPARSF, Data Processing Assistant for Resting-State fMRI; FC, functional connectivity; FG, frontal gyrus; GM, grey matter; HC, healthy controls; ICA, independent component analysis; MDD, patients with major depression; PFC, prefrontal cortex; MFG, medial frontal gyrus; mPFC, medial PFC; PACC, perigenual anterior cingulate cortex; PANSS, positive and negative symptoms scale; PCC, posterior cingulate cortex; ReHO, regional homogeneity; SBA, seed based analysis; SCZ, schizophrenic patients; SMN, sensorimotor network.

Overall, findings from the reviewed studies suggest that BD patients compared to Healthy Controls (HC) are characterised by marked functional alterations of both anterior and posterior hubs of the DMN, including the PFC, PCC, precuneus and inferior parietal cortex. These alterations are mostly mixed across studies, consisting of both hypo and hyper-connectivity of the anterior DMN at rest (Table 1).

Specifically, 14 studies (60%) showed aberrant functional activity in the posterior hubs of the DMN, of which five in the precuneus (21%) and 12 in the PCC (52%). These alterations are mostly hypo-activations of BD patients when compared with HC. Specifically, most of the studies showed FC reductions in BD compared to HC within these regions (Khadka et al., Reference Khadka, Meda, Stevens, Glahn, Calhoun, Sweeney, Tamminga, Keshavan, O'Neil, Schretlen and Pearlson2013; Meda et al., Reference Meda, Ruaño, Windemuth, O'Neil, Berwise, Dunn, Boccaccio, Narayanan, Kocherla, Sprooten, Keshavan, Tamminga, Sweeney, Clementz, Calhoun and Pearlson2014; Teng et al., Reference Teng, Lu, Wang, Li, Tu, Hung, Su and Wu2014; Magioncalda et al., Reference Magioncalda, Martino, Conio, Escelsior, Piaggio, Presta, Marozzi, Rocchi, Anastasio, Vassallo, Ferri, Huang, Roccatagliata, Pardini, Northof and Amore2015; Rey et al., Reference Rey, Piguet, Benders, Favre, Eickhoff, Aubry and Vuilleumier2016; Wang et al., Reference Wang, Zhong, Jia, Sun, Wang, Liu, Pan and Huang2016, Reference Wang, Zhong, Chen, Liu, Zhao, Sun, Jia and Huang2017; Luo et al., Reference Luo, Chen, Jia, Gong, Qiu, Zhong, Zhao, Chen, Lai, Qi, Huan and Wang2018; Qiu et al., Reference Qiu, Chen, Chen, Jia, Gong, Luo, Zhong, Zhao, Lai, Qi, Huang and Wang2019). However, few recent studies found also hyper-connectivity between PCC, the mPFC and the insula (Liu et al., Reference Liu, Wu, Zhang, Guo, Long and Yao2015). A single study by Zhong and colleagues differentiated between psychotic and non-psychotic BD and showed an increase in the FC of the PCC only in psychotic BD v. HC, possibly indicating specificity of the PCC alterations for the psychotic conditions (Zhong et al., Reference Zhong, Wang, Gao, Xiao, Lu, Jiao, Su and Lu2019). Moreover, a study by Ford and colleagues (Reference Ford, Théberge, Neufeld, Williamson and Osuch2013) found a significant negative correlation between FC in the PCC and a so-called ‘bipolarity index’ based on five key domains of the disorder: signs and symptoms, age of onset, course of illness, response to treatment and family history (Ford et al., Reference Ford, Théberge, Neufeld, Williamson and Osuch2013). Even though the authors do not speculate about this specific finding, the PCC has been previously found to be a key region in several cognitive processes such as monitoring of awareness, arousal, internal thought and attention control (Leech and Sharp, Reference Leech and Sharp2013). Of note, a single study performed a machine learning discrimination analysis based on the DMN FC between depressed BD patients and patients suffering from major depression (MDD), reaching a 69% accuracy (Rive et al., Reference Rive, Redlich, Schmaal, Marquand, Dannlowski, Grotegerd, Veltman, Schene and Ruhé2016). This result suggests that specific alterations of the DMN could be considered an endophenotype of BD. Partially in line with this hypothesis, two studies investigated FC of the DMN in both BD and schizophrenic (SCZ) patients and their close relatives (Khadka et al., Reference Khadka, Meda, Stevens, Glahn, Calhoun, Sweeney, Tamminga, Keshavan, O'Neil, Schretlen and Pearlson2013; Meda et al., Reference Meda, Ruaño, Windemuth, O'Neil, Berwise, Dunn, Boccaccio, Narayanan, Kocherla, Sprooten, Keshavan, Tamminga, Sweeney, Clementz, Calhoun and Pearlson2014). Notably, only the study by Khadka and colleagues found alterations in the frontal hub of the DMN in both BD patients and their relatives (Khadka et al., Reference Khadka, Meda, Stevens, Glahn, Calhoun, Sweeney, Tamminga, Keshavan, O'Neil, Schretlen and Pearlson2013).

Some studies show the presence of altered connectivity in BD patients, specifically between the posterior part of the DMN and the anterior cingulate cortex (ACC) suggesting a possible relationship between the two regions (Magioncalda et al., Reference Magioncalda, Martino, Conio, Escelsior, Piaggio, Presta, Marozzi, Rocchi, Anastasio, Vassallo, Ferri, Huang, Roccatagliata, Pardini, Northof and Amore2015; Rey et al., Reference Rey, Piguet, Benders, Favre, Eickhoff, Aubry and Vuilleumier2016). Specifically, Magioncalda and colleagues (Reference Magioncalda, Martino, Conio, Escelsior, Piaggio, Presta, Marozzi, Rocchi, Anastasio, Vassallo, Ferri, Huang, Roccatagliata, Pardini, Northof and Amore2015) found that in BD patients, alterations in the FC between the frontal and posterior hubs of the cingulate cortex can lead to excessive focusing on external contents and, ultimately, manic phases (Magioncalda et al., Reference Magioncalda, Martino, Conio, Escelsior, Piaggio, Presta, Marozzi, Rocchi, Anastasio, Vassallo, Ferri, Huang, Roccatagliata, Pardini, Northof and Amore2015).

Only two studies found no alterations in the DMN of BD patients (Yip et al., Reference Yip, Mackay and Goodwin2014; Nguyen et al., Reference Nguyen, Kovacevic, Dev, Lu, Liu and Eyler2017). Nguyen and colleagues (Reference Nguyen, Kovacevic, Dev, Lu, Liu and Eyler2017) failed to find DMN alterations of BD patients, however, they found a reduction in the variability of the dynamic connectivity between the medial PFC and PCC associated with impaired processing speed (Nguyen et al., Reference Nguyen, Kovacevic, Dev, Lu, Liu and Eyler2017). Unlike the standard rs-fMRI analysis pipelines, which estimate the average brain connectivity over time, dynamic connectivity methods allow to analyse how the FC between two or more brain regions changes during the entire scan duration obtaining useful information about the dynamic variability of the brain regional synchronization (Rashid et al., Reference Rashid, Damaraju, Pearlson and Calhoun2014). This result suggests the importance of considering not only the average FC over time but also the dynamic interplay between brain regions. Goya-Maldonado and colleagues (Reference Goya-Maldonado, Brodmann, Keil, Trost, Dechent and Gruber2016) found alterations in the DMN of patients suffering from major depression but not in the BD group. However, they found alterations in the FPN, a network partially overlapping with the DMN itself (Goya-Maldonado et al., Reference Goya-Maldonado, Brodmann, Keil, Trost, Dechent and Gruber2016; Bellani et al., Reference Bellani, Bontempi, Zovetti, Gloria Rossetti, Perlini, Dusi, Squarcina, Marinelli, Zoccatelli, Alessandrini, Francesca Maria Ciceri, Sbarbati and Brambilla2020). Lastly, also Yip et al. (Reference Yip, Mackay and Goodwin2014)did not find any alteration of the FC of the DMN in BD(. However, the authors recruited only young BD-II patients (mean age 23.07 ± 3.73) without any history of medication. The authors suggest that the absence of alterations within the DMN may be due to specificity of the recruited group (e.g. diagnosis, last clinical phase) or on the absence of medication.

To conclude, current evidence from rs-fMRI studies suggests that BD patients show FC alterations (i.e. hypo and hyper-connectivity) compared to HC in both frontal and posterior hubs of the DMN. In particular, the evidence to date suggests that BD patients compared to HC have altered FC of the DMN in a number of regions including the PFC, the ACC and PCC and the precuneus. However, only a paucity of studies has been conducted and evidence is still sparse. The inconsistency of findings may be due (in part) to methodological issues such as heterogeneous analysis pipelines (e.g. ICA v. SBA v. regional homogeneity analysis), different BD populations (e.g. euthymic, BD-I, BD-II) or the lack of control for confounders (e.g. pharmacological treatment) that may have influenced the results (Vargas et al., Reference Vargas, López-Jaramillo and Vieta2013; Martino et al., Reference Martino, Magioncalda, Huang, Conio, Piaggio, Duncan, Rocchi, Escelsior, Marozzi, Wolff, Inglese, Amore and Northoff2016). Moreover, a recent study by Bellani et al. (Reference Bellani, Bontempi, Zovetti, Gloria Rossetti, Perlini, Dusi, Squarcina, Marinelli, Zoccatelli, Alessandrini, Francesca Maria Ciceri, Sbarbati and Brambilla2020), examining the resting state activations of a group of euthymic BD patients and HC, found alterations in the DMN only when examining between network connectivity, reporting no differences between the two groups in within-network functioning. The result suggests the importance of examining both within and between-network connectivity to achieve a global understanding of the BD euthymic condition.

Detailed neurophysiological correlates of resting-state activity are currently the object of speculation and only specific analysis techniques (i.e. dynamic functional connectivity) can account for the complex dynamics in the interactions between brain regions as suggested by some studies (Brady et al., Reference Brady, Tandon, Masters, Margolis, Cohen, Keshavan and Öngür2017; Wang et al., Reference Wang, Wang, Huang, Jia, Zheng, Zhong, Chen, Huang and Huang2020). Furthermore, since the BOLD contrast reflects hemodynamic/metabolic processes in the brain, the interpretation of rs-fMRI findings in terms of the underlying neuronal activity is not straightforward and would require the integration of electrophysiological information (Maggioni et al., Reference Maggioni, Zucca, Reni, Cerutti, Triulzi, Bianchi and Arrigoni2016). Despite these limits, rs-fMRI studies allow the whole-brain mapping of the functional connectomes of specific pathologies such as the BD and provide new data for the development of neurobiological models and theories.

In view of the recent conceptualisation of BD in terms of neural circuitry, future studies should consider a multimodal perspective in order to (1) investigate the complex link between structure and function in the DMN and other networks involved in BD pathophysiology, (2) identify neurodevelopmental and maturation trajectories of FC patterns in relation to the natural course of the disease and in response to different therapeutic strategies, (3) disentangle the contributions of genetics, environment and their interaction to the large-scale functional brain network abnormalities underlying BD symptomatology.

Data

The data that support the findings of this study (search query) are available on request from the corresponding author.

Acknowledgements

None.

Financial support

This paper was partly supported by grants from the Italian Ministry of Health to PB (RF-2016-0236458), EM (GR-2018-12367789) and MB (GR-2010-2319022)

Conflict of interest

None.

References

Bellani, M, Bontempi, P, Zovetti, N, Gloria Rossetti, M, Perlini, C, Dusi, N, Squarcina, L, Marinelli, V, Zoccatelli, G, Alessandrini, F, Francesca Maria Ciceri, E, Sbarbati, A and Brambilla, P (2020) Resting state networks activity in euthymic bipolar disorder. Bipolar Disorders. https://doi.org/10.1111/bdi.12900.
Brady, RO, Tandon, N, Masters, GA, Margolis, A, Cohen, BM, Keshavan, M and Öngür, D (2017) Differential brain network activity across mood states in bipolar disorder. Journal of Affective Disorders 207, 367376.
Cavanna, AE (2007) The precuneus and consciousness. CNS Spectrums 12, 545552.
Chen, CH, Suckling, J, Lennox, BR, Ooi, C and Bullmore, ET (2011) A quantitative meta-analysis of fMRI studies in bipolar disorder. Bipolar Disorders 13, 115.
Chen, G, Zhao, L, Jia, Y, Zhong, S, Chen, F, Luo, X, Qiu, S, Lai, S, Qi, Z, Huang, L and Wang, Y (2019) Abnormal cerebellum-DMN regions connectivity in unmedicated bipolar II disorder. Journal of Affective Disorders 243, 441447.
Clark, L, Iversen, SD and Goodwin, GM (2002) Sustained attention deficit in bipolar disorder. British Journal of Psychiatry 180, 313319.
Damasio, H, Grabowski, T, Frank, R, Galaburda, AM and Damasio, AR (1994) The return of Phineas Gage: clues about the brain from the skull of a famous patient. Science (New York, N.Y.) 264, 1102.
Delvecchio, G, Maggioni, E, Squarcina, L, Arighi, A, Galimberti, D, Scarpini, E, Bellani, M and Brambilla, P (2020) A critical review on structural neuroimaging studies in BD: a transdiagnostic perspective from psychosis to fronto-temporal dementia. Current Behavioral Neuroscience Reports 7, 8695.
Ford, KA, Théberge, J, Neufeld, RJ, Williamson, PC and Osuch, EA (2013) Correlation of brain default mode network activation with bipolarity index in youth with mood disorders. Journal of Affective Disorders 150, 11741178.
Friston, KJ, Frith, CD, Liddle, PF and Frackowiak, RS (1993) Functional connectivity: the principal-component analysis of large (PET) data sets. Journal of Cerebral Blood Flow & Metabolism 13, 514.
Goya-Maldonado, R, Brodmann, K, Keil, M, Trost, S, Dechent, P and Gruber, O (2016) Differentiating unipolar and bipolar depression by alterations in large-scale brain networks. Human Brain Mapping 37, 808818.
Joel, SE, Caffo, BS, van Zijl, PCM and Pekar, JJ (2011) On the relationship between seed-based and ICA-based measures of functional connectivity. Magnetic Resonance in Medicine 66, 644657.
Khadka, S, Meda, SA, Stevens, MC, Glahn, DC, Calhoun, VD, Sweeney, JA, Tamminga, CA, Keshavan, M, O'Neil, K, Schretlen, D and Pearlson, GD (2013) Is aberrant functional connectivity a psychosis endophenotype? A resting state functional magnetic resonance imaging study. Biological Psychiatry 74, 458466.
Leech, R and Sharp, DJ (2013) The role of the posterior cingulate cortex in cognition and disease. Brain 137, 1232.
Liu, Y, Wu, X, Zhang, J, Guo, X, Long, Z and Yao, L (2015) Altered effective connectivity model in the default mode network between bipolar and unipolar depression based on resting-state fMRI. Journal of Affective Disorders 182, 817.
Luo, X, Chen, G, Jia, Y, Gong, J, Qiu, S, Zhong, S, Zhao, L, Chen, F, Lai, S, Qi, Z, Huan, L and Wang, Y (2018) Disrupted cerebellar connectivity with the central executive network and the default-mode network in unmedicated bipolar II disorder. Frontiers in Psychiatry 9, 705.
Maggioni, E, Zucca, C, Reni, G, Cerutti, S, Triulzi, FM, Bianchi, AM and Arrigoni, F (2016) Investigation of the electrophysiological correlates of negative BOLD response during intermittent photic stimulation: an EEG-fMRI study. Human Brain Mapping 37, 22472262.
Magioncalda, P, Martino, M, Conio, B, Escelsior, A, Piaggio, N, Presta, A, Marozzi, V, Rocchi, G, Anastasio, L, Vassallo, L, Ferri, F, Huang, Z, Roccatagliata, L, Pardini, M, Northof, G and Amore, M (2015) Functional connectivity and neuronal variability of resting state activity in bipolar disorder—reduction and decoupling in anterior cortical midline structures. Human Brain Mapping 36, 666682.
Marchand, WR, Lee, JN, Johnson, S, Gale, P and Thatcher, J (2014) Abnormal functional connectivity of the medial cortex in euthymic bipolar II disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry 51, 2833.
Martino, M, Magioncalda, P, Huang, Z, Conio, B, Piaggio, N, Duncan, NW, Rocchi, G, Escelsior, A, Marozzi, V, Wolff, A, Inglese, M, Amore, M and Northoff, G (2016) Contrasting variability patterns in the default mode and sensorimotor networks balance in bipolar depression and mania. Proceedings of the National Academy of Sciences 113, 4824.
Meda, SA, Ruaño, G, Windemuth, A, O'Neil, K, Berwise, C, Dunn, SM, Boccaccio, LE, Narayanan, B, Kocherla, M, Sprooten, E, Keshavan, MS, Tamminga, CA, Sweeney, JA, Clementz, BA, Calhoun, VD and Pearlson, GD (2014) Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia. Proceedings of the National Academy of Sciences 111, E2066.
Nguyen, TT, Kovacevic, S, Dev, SI, Lu, K, Liu, TT and Eyler, LT (2017) Dynamic functional connectivity in bipolar disorder is associated with executive function and processing speed: a preliminary study. Neuropsychology 31, 7383.
Öngür, D, Lundy, M, Greenhouse, I, Shinn, AK, Menon, V, Cohen, BM and Renshaw, PF (2010) Default mode network abnormalities in bipolar disorder and schizophrenia. Psychiatry Research: Neuroimaging 183, 5968.
Phillips, ML and Swartz, HA (2014) A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of underlying neural circuitry and a road map for future research. American Journal of Psychiatry 171, 829843.
Qiu, S, Chen, F, Chen, G, Jia, Y, Gong, J, Luo, X, Zhong, S, Zhao, L, Lai, S, Qi, Z, Huang, L and Wang, Y (2019) Abnormal resting-state regional homogeneity in unmedicated bipolar II disorder. Journal of Affective Disorders 256, 604610.
Raichle, ME (2015) The brain's default mode network. Annual Review of Neuroscience 38, 433447.
Raichle, ME and Mintun, MA (2006) Brain work and brain imaging. Annual Review of Neuroscience 29, 449476.
Rashid, B, Damaraju, E, Pearlson, GD and Calhoun, VD (2014) Dynamic connectivity states estimated from resting fMRI identify differences among schizophrenia, bipolar disorder, and healthy control subjects. Frontiers in Human Neuroscience 8, 897.
Rey, G, Piguet, C, Benders, A, Favre, S, Eickhoff, SB, Aubry, JM and Vuilleumier, P (2016) Resting-state functional connectivity of emotion regulation networks in euthymic and non-euthymic bipolar disorder patients. European Psychiatry 34, 5663.
Rive, MM, Redlich, R, Schmaal, L, Marquand, AF, Dannlowski, U, Grotegerd, D, Veltman, DJ, Schene, AH and Ruhé, HG (2016) Distinguishing medication-free subjects with unipolar disorder from subjects with bipolar disorder: state matters. Bipolar Disorders 18, 612623.
Robinson, LJ, Thompson, JM, Gallagher, P, Goswami, U, Young, AH, Ferrier, IN and Moore, PB (2006) A meta-analysis of cognitive deficits in euthymic patients with bipolar disorder. Journal of Affective Disorders 93, 105115.
Rosazza, C and Minati, L (2011) Resting-state brain networks: literature review and clinical applications. Neurological Sciences 32, 773785.
Smitha, KA, Akhil Raja, K, Arun, KM, Rajesh, PG, Thomas, B, Kapilamoorthy, TR and Kesavadas, C (2017) Resting state fMRI: a review on methods in resting state connectivity analysis and resting state networks. The Neuroradiology Journal 30, 305317.
Solé, B, Jiménez, E, Torrent, C, Reinares, M, Bonnin, CDM, Torres, I, Varo, C, Grande, I, Valls, E, Salagre, E, Sanchez-Moreno, J, Martinez-Aran, A, Carvalho, AF and Vieta, E (2017) Cognitive impairment in bipolar disorder: treatment and prevention strategies. International Journal of Neuropsychopharmacology 20, 670680.
Teng, S, Lu, CF, Wang, PS, Li, CT, Tu, PC, Hung, CI, Su, TP and Wu, YT (2014) Altered resting-state functional connectivity of striatal-thalamic circuit in bipolar disorder. PloS one 9, e96422e96422.
Torres, IJ, Boudreau, VG and Yatham, LN (2007) Neuropsychological functioning in euthymic bipolar disorder: a meta-analysis. Acta Psychiatrica Scandinavica 116, 1726.
Vargas, C, López-Jaramillo, C and Vieta, E (2013) A systematic literature review of resting state network—functional MRI in bipolar disorder. Journal of Affective Disorders 150, 727735.
Wang, Y, Zhong, S, Jia, Y, Sun, Y, Wang, B, Liu, T, Pan, J and Huang, L (2016) Disrupted resting-state functional connectivity in nonmedicated bipolar disorder. Radiology 280, 529536.
Wang, Y, Zhong, S, Chen, G, Liu, T, Zhao, L, Sun, Y, Jia, Y and Huang, L (2017) Altered cerebellar functional connectivity in remitted bipolar disorder: a resting-state functional magnetic resonance imaging study. Australian & New Zealand Journal of Psychiatry 52, 962971.
Wang, J, Wang, Y, Huang, H, Jia, Y, Zheng, S, Zhong, S, Chen, G, Huang, L and Huang, R (2020) Abnormal dynamic functional network connectivity in unmedicated bipolar and major depressive disorders based on the triple-network model. Psychological Medicine 50, 465474.
Yip, SW, Mackay, CE and Goodwin, GM (2014) Increased temporo-insular engagement in unmedicated bipolar II disorder: an exploratory resting state study using independent component analysis. Bipolar Disorders 16, 748755.
Zhong, Y, Wang, C, Gao, W, Xiao, Q, Lu, D, Jiao, Q, Su, L and Lu, G (2019) Aberrant resting-state functional connectivity in the default mode network in pediatric bipolar disorder patients with and without psychotic symptoms. Neuroscience Bulletin 35, 581590.