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Journal of Psychiatric Research 92 (2017) 147e159

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Journal of Psychiatric Research

journal homepage: www.elsevier.com/locate/psychires

Resting state brain network function in major depression e Depression symptomatology, antidepressant treatment effects, future research

Janis Brakowski, MD a, *, Simona Spinelli, PhD a, Nadja D€orig, PhD a, Oliver Gero Bosch, MD a, Andrei Manoliu, MD, PhD a, Martin Grosse Holtforth, PhD b, Erich Seifritz, MD a

a Psychiatric University Hospital, Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Lenggstrasse 31, 8032 Zurich, Switzerland b Division of Clinical Psychology and Psychotherapy, Department of Psychology, University of Bern, Fabrikstrasse 8, 3012 Bern, Switzerland

a r t i c l e i n f o

Article history: Received 20 December 2016 Received in revised form 21 March 2017 Accepted 21 April 2017

Keywords: Depression Resting-state fMRI Functional connectivity networks Depression symptomatology Depression treatment

* Corresponding author. Department of Psychiatry, Zurich, Switzerland.

E-mail addresses: [email protected] (J. (O.G. Bosch), [email protected] (A. Manoliu),

http://dx.doi.org/10.1016/j.jpsychires.2017.04.007 0022-3956/© 2017 Elsevier Ltd. All rights reserved.

a b s t r a c t

The alterations of functional connectivity brain networks in major depressive disorder (MDD) have been subject of a large number of studies. Using different methodologies and focusing on diverse aspects of the disease, research shows heterogeneous results lacking integration. Disrupted network connectivity has been found in core MDD networks like the default mode network (DMN), the central executive network (CEN), and the salience network, but also in cerebellar and thalamic circuitries. Here we review literature published on resting state brain network function in MDD focusing on methodology, and clinical characteristics including symptomatology and antidepressant treatment related findings. There are relatively few investigations concerning the qualitative aspects of symptomatology of MDD, whereas most studies associate quantitative aspects with distinct resting state functional connectivity alterations. Such depression severity associated alterations are found in the DMN, frontal, cerebellar and thalamic brain regions as well as the insula and the subgenual anterior cingulate cortex. Similarly, different therapeutical options in MDD and their effects on brain function showed patchy results. Herein, phar- maceutical treatments reveal functional connectivity alterations throughout multiple brain regions notably the DMN, fronto-limbic, and parieto-temporal regions. Psychotherapeutical interventions show significant functional connectivity alterations in fronto-limbic networks, whereas electroconvulsive therapy and repetitive transcranial magnetic stimulation result in alterations of the subgenual anterior cingulate cortex, the DMN, the CEN and the dorsal lateral prefrontal cortex. While it appears clear that functional connectivity alterations are associated with the pathophysiology and treatment of MDD, future research should also generate a common strategy for data acquisition and analysis, as a least common denominator, to set the basis for comparability across studies and implementation of functional connectivity as a scientifically and clinically useful biomarker.

© 2017 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 2. Literature search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 3. Methodology of resting state brain functional, connectivity analysis in MDD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 4. MDD symptomatology and resting state brain function e state or trait? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Lenggstrasse 31, 8032

Brakowski), [email protected] (S. Spinelli), [email protected] (N. D€orig), [email protected] [email protected], [email protected] (M.G. Holtforth), [email protected] (E. Seifritz).

J. Brakowski et al. / Journal of Psychiatric Research 92 (2017) 147e159148

4.1. Rumination and self-referential, thinking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 4.2. Helplessness and hopelessness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 4.3. Suicidal, tendency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 4.4. Clinical, characteristics of MDD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 4.5. Subclinical, depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 4.6. Functional, connectivity in depression as comorbidity and as neuronal, trait . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

5. Antidepressant treatments and resting state brain functional, connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 6. Antidepressant medication (ADM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

6.1. Ketamine in healthy subjects and MDD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 6.2. Treatment response and medication status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 6.3. ADM treatment effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 6.4. Treatment resistance versus treatment sensitivity to ADM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 6.5. Psychotherapy and neurofeedback training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 6.6. Neuromodulation (ECT and rTMS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

6.6.1. Electroconvulsive therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 6.7. Repetitive transcranial, magnetic stimulation (rTMS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

1. Introduction

Major depressive disorder (MDD) is one of the most prevalent illnesses (Kessler et al., 2003; DeRubeis et al., 2008) with an increased mortality (Ebmeier et al., 2006), high severity weight, and ranks third among the leading medical causes of disability worldwide (Mathers et al., 2008). Previous studies show that the core symptoms of MDD - depressed mood and anhedonia - are affected by key dysfunctions in brain circuitries mainly supporting emotion regulation (Davidson et al., 2002) and cognitive control (Wang et al., 2016). Within the last decade, a vast number of im- aging studies on MDD related altered brain network function at rest have been published using different methodologies (Dutta et al., 2014) focusing on the dysfunction of these networks themselves and their interaction with other functional, and dysfunctional, brain regions (Iwabuchi et al., 2015) as an explanatory model for cognitive and affective pathologies in depression (e.g. Kaiser et al., 2015b; Veer et al., 2010; Diener et al., 2012). Disruptive brain function in multiple localizations and a number of distinct intrinsic networks have been reported (Pandya et al., 2012), the core net- works affected in MDD include the default mode network (DMN), the central, executive network (CEN), and the salience network (SN) (Sheline et al., 2010; Manoliu et al., 2014; Zheng et al., 2015). Even though findings concerning altered neural, connections within and between these networks vary among studies, it is argued that MDD can be understood as a network-based disorder with consistent changes in functional, connectivity patterns (Mulders et al., 2015). Furthermore, De Kwaasteniet et al. (2013) related these functional, changes specifically to structural, dys- connectivities in MDD.

The central, role and relevance of the DMN in MDD resting state research is outlined by many studies (Buckner et al., 2008; Wei et al., 2015; Orosz et al., 2012; Greicius et al., 2007). Its functional, alterations are shown to be present not only in MDD patients but also in individuals at high risk to develop a depression (Posner et al., 2015), during different stages of the disease (Chen et al., 2015), and in different age groups (Gaffrey et al., 2012; Andreescu et al., 2013). Furthermore, analysis of topological, brain structure including re- gions of the DMN using graph theory can discriminate between patients and healthy controls with a high sensitivity and specificity (Zeng et al., 2012, 2014). There are fewer studies on altered con- nectivity and function of the CEN in MDD with partly inconsistent

results, showing both hypoconnectivity and hyperconnectivity of its core region, the dorsolateral, prefrontal, cortex (DLPFC) with other brain regions (Mulders et al., 2015). Moreover, its significance in the transgenerational, transmission of depression vulnerability is outlined (Clasen et al., 2014). Studies on the disrupted functioning of the salience network in MDD also display diverse results thus the majority of research reveal, an increased connectivity between the SN and the anterior regions of the DMN including the medial, prefrontal, cortex (MPFC), the posterior cingulate cortex (PCC) and bilateral, parietal, cortex (Mulders et al., 2015). The dysfunction of SN brain regions is present in different age groups (Yuen et al., 2014; Bebko et al., 2015), and can be used to discriminate be- tween healthy controls and patients by estimating the long term memory properties of the SN and other resting-state networks (Wei et al., 2013).

Recent studies and reviews furthermore reveal, the importance of resting state brain connectivity alterations within distinct net- works and sub networks (Peng et al., 2015; Alexopoulos et al., 2012; Sambatoro et al., 2014; Manoliu et al., 2014; Coutinho et al., 2016), interhemispheric connectivity/coordination (Hermesdorf et al., 2016; Lai and Wu, 2014; Zhang et al., 2015; Wang et al., 2013), the effects of antidepressant treatments (Wang et al., 2014; Li et al., 2013) including electroconvulsive therapy (Abbott et al., 2013, 2014) and repetitive transcranial, magnetic stimulation (Liston et al., 2013; Baeken et al., 2014), and correlations of changes in resting state brain function with clinical, characteristics/symp- tomatology of depression (Peng et al., 2014; Davey et al., 2015; Fan et al., 2013; Kühn et al., 2012).

This article reviews relevant literature published on resting state brain network function in MDD focusing on clinical, characteristics and symptomatology, and antidepressant treatment related find- ings. It further aims to integrate the heterogeneous results, and discusses the question whether the distinct functional, connectivity findings represent a surrogate for the disease state, certain char- acteristic symptoms or the trait neurobiological, underpinnings of risk for developing a MDD. Finally, this article proposes some strategic recommendations for future research.

2. Literature search

A search in PubMed/Medline was performed on 21.10.2016 for literature published in English on resting state connectivity in

J. Brakowski et al. / Journal of Psychiatric Research 92 (2017) 147e159 149

depression, using “depression” and “resting state” as search terms. The search revealed 1336 papers published between February 1923 and October 10th 2016. Online publications before print were also included. In a first step we screened all papers that were published in peer-reviewed journals including reviews, and identified 346 publications that met the following criteria:

(1) studies using resting state functional, connectivity MRI (rs- fcMRI)

(2) studies including patients with a major depressive disorder (MDD)

(3) studies comparing patients with MDD to matched healthy controls

In a second step, since the focus of this review was the symp- tomatology of patients with MDD as well as antidepressant treat- ment effects in relation to RSFC alterations, we full text assessed all 346 articles for eligibility applying the following criteria. Studies were included if they:

(1) correlated rs-fcMRI alterations with MDD symptomatology and/or

(2) correlated rs-fcMRI alterations with antidepressant treat- ment effects

Resulting in 94 papers which are discussed in this article. From these 94 papers, 45 address MDD symptomatology while 49 fall into the second category dealing with antidepressant treatment effects. The sub categories can be found in Table 1. Considering that the studies were too heterogeneous to permit a systematic review, we decided to provide a narrative review in order to develop a clear and broad conceptual, argumentation, and focus on the main im- plications for further research. Papers on resting state fMRI alter- ations through alternative antidepressant treatments (e.g. yoga, transcutaneous vagus nerve stimulation or acupuncture) were not included.

3. Methodology of resting state brain functional, connectivity analysis in MDD

In general, functional, connectivity is defined as the statistical, dependencies among observed neurophysiological, responses (Friston et al., 2014). In terms of resting state functional, magnetic resonance imaging (fMRI), distinct spatially and/or temporal, changes in blood oxygen level-dependent (BOLD) signal, occurring in the absence of a task or stimulus can be analysed to infer the functional, architecture of the human brain (Lee et al., 2013). Within the vast number of techniques to analyse resting state brain function, independent component analysis (ICA) (Beckmann et al., 2005), seed-based/region of interest analysis (Van Dijk et al., 2010), and graph theory (Bullmore and Sporns, 2009) are most prominent. While seed-based analyses use a previously defined region of interest (seed) and calculate its temporal, correlation with all other regions of the brain (Greicius, 2008; Wang et al., 2012), ICA extracts data from the entire brain and separates a set of signals into independent spatiotemporal, components resulting in func- tional, maps and time courses (Greicius et al., 2007; Seifritz et al., 2002). Consequently, connectivity analysis of brain regions using the seed-based approach results in a single metric, whereas ICA calculates the whole brain functional, connectivity and decomposes it into connectivity within each network plus connectivity between pairs of networks (Joel et al., 2011). Graph theory analyses func- tional, connectivity of brain networks in four steps. It defines nodes of the networks, estimates the continuous association between these nodes by spectral, coherence or volume, generates an

association matrix/graphical, by compiling all pairwise associations between nodes, and lastly calculates network parameters within groups (e.g. density, efficiency, global, average path length). Furthermore it estimates the contributions of specific brain regions to the organization of the system (degree-centrality, farness- centrality, betweenness-centrality) (Bullmore and Sporns, 2009; Ye et al., 2015; Lord et al., 2012). Compared to ICA and seed based approaches, one advantage of graph theory is its opportunity to learn from research in other fields studying complex systems. Moreover, it analyses whole brain connectivity instead of connec- tivity patterns between single brain regions (Reijnevelda et al., 2007).

Alternative methods to identify changes in functional, brain connectivity comprise the measure of differences in coherence- based regional, homogeneity (ReHo) (Liu et al., 2012a,b), and frac- tional, amplitude of low frequency fluctuations (fALFF) (Tadayonnejad et al., 2015; Smith, 2015; Liu et al., 2013). The regional, homogeneity approach calculates the synchronization between the time series of a given voxel and its nearest neighbours in a voxel-wise way, providing information about whole-brain activation patterns (Zang et al., 2004). Fractional, amplitude of low frequency fluctuations is a further development of the ampli- tude of low frequency fluctuations approach, as it reduces the method's sensitivity to physiological, noise. In summary, the fALFF method measures the power spectrum ratio of low-frequency to that of the entire frequency range in a specific brain region in order to directly estimate regional, spontaneous brain activity (Zou et al., 2008). It is important to note that although all presented methods are very valuable for evaluating potentially abnormal, patterns of distributed brain activity, they cannot be applied to deduce the potential, causal, influence of one brain region's activity over another (Friston et al., 2014).

4. MDD symptomatology and resting state brain function e state or trait?

The construct of affective disorders' neuropathology, especially major depression, is a complex interaction of actual, symptom- atology, MDD subtypes, genetic factors and long term persistence of certain symptom clusters (Kennedy, 2008). While core symptoms of MDD - depressed mood and anhedonia - have been subject to intense scientific investigation, the neurobiology, treatment- sensitivity and general, importance of frequent MDD associated symptoms like sexual, dysfunction, anxiety, guilt, helplessness, and rumination are poorly understood (Maletic et al., 2007; Lorenzetti et al., 2009). The analysis of symptom clusters across neuropsy- chiatric diseases comprises a variety of methods including computational, modelling (Moustafa et al., 2016), neurochemical, investigations (Nutt, 2008), neurophysiology (Putnam and McSweeney, 2008), and structural/functional, MRI (Drevets et al., 2008). Here, we discuss the still small number of resting state functional, connectivity research findings related to specific MDD symptomatology, correlations of resting state functional, connec- tivity (RSFC) alterations with symptom severity and other clinical, characteristics of MDD, and whether these alterations qualify as disease specific or rather as neuronal, traits of specific symptoms. However, it is important to note that the specificity of findings is still subject of ongoing research, since depressive symptoms are related to both similar and selective alterations in brain activation in numerous psychiatric disorders, ranging from unipolar affective disorders over bipolar disorder to schizophrenia (Kumari et al., 2016). Nevertheless, current literature regarding transdiagnostic findings is still scarce with only few meta-analyses addressing this important research question (Schilbach et al., 2016). Therefore, future studies are needed to conclusively infer on disease-

Table 1 Literature search.

J. Brakowski et al. / Journal of Psychiatric Research 92 (2017) 147e159150

specificity of evidence for altered brain activity in patients with MDD reported in the current review, particularly with respect to a potential, contribution to the concept of the recently proposed Research Domain Criteria (RDoC) (Insel et al., 2010).

4.1. Rumination and self-referential, thinking

The basic neuropathologies of rumination as MDD symptom have been intensively studied and precisely described (Cooney et al., 2010). In eight studies, the RSFC of ruminative and self- referential, thinking in MDD is examined. Kühn et al. (2012) explore the structural, and functional, neural, correlates of rumi- nation. They find that rumination is associated with volume and resting state reduction in brain areas associated with cognitive control processes of inhibition and thought suppression. Rumina- tion correlates negatively with gray matter volume in the bilateral, inferior frontal, gyrus, left anterior cingulate cortex (ACC), and bilateral, mid cingulate cortex. Furthermore there is a reduction of resting state activity in the ACC and the right inferior frontal, gyrus - both brain regions that are associated with MDD neuropathology. In a group of 35 patients, Zhu et al. (2012a) explore alterations of RSFC in rumination and overgeneral, autobiographical, memory as clinical, characteristics of MDD via the ICA approach. They find a dissociation between the anterior and posterior RSFC in the DMN, whereby increased RSFC in anterior medial, regions of the DMN (medial, prefrontal, cortex, PCC and bilateral, parietal, cortex) is associated with rumination, and decreased RSFC in posterior medial, regions is associated with overgeneral, autobiographical, memory. Hamilton et al. (2011) on the other hand examine the activity level in the DMN, and the task positive network (TPN) in

maladaptive (depressive) and adaptive rumination. They find that increased DMN dominance is associated with higher levels of maladaptive rumination and lower dominance with adaptive, reflective rumination. In a much larger study, Kaiser et al. (2015a) examine dynamic RSFC of the medial, prefrontal, cortex (MPFC) in relation to ruminative thinking. Dynamic RSFC herein measures the variability in the strength or spatial, organization of resting state functional, connectivity (networks). They find that an increased dynamic RSFC between the MPFC and the insula is related to higher levels of recent rumination, and therefore claim that these abnormal, patterns of fluctuating communication among brain systems are directly involved with depressive symptoms, e.g. frequent self-referential, thinking or rumination. In a small pilot study with depressed adolescents, Peters et al. (2016) show that amygdala to PCC hyperconnectivity was also positively correlated with greater rumination.

Since reduced optimism is related to depression, Wu et al. (2015) explore optimistic and depressive self-evaluations in MDD using a combination of the ICA, fALFF, and ReHo approaches. They show that firstly, the bilateral, superior gyri of the dorsal, lateral, prefrontal, cortex (DLPFC) and bilateral, superior medial, frontal, parts of the dorsal, medial, prefrontal, cortex (DMPFC) play a key role in maintaining spontaneous optimistic self-evaluative ten- dencies. Second, decreased activity in the DLPFC and medial, orbital, frontal, cortex is related to depressive symptom severity. Therefore they claim that the DLPFC and DMPFC play decisive roles in mediating valences of self-referential, content. In addition, a reduced RSFC between the DLPFC and brain regions associated with the representations of self or other mental, states (temporo-pari- etal, junction and precuneus) as well as salience detection and

J. Brakowski et al. / Journal of Psychiatric Research 92 (2017) 147e159 151

orientation (insula) is found in a seed-based analysis by Hwang et al. (2015). Herein, the FC changes in the DLPFC and temporo- parietal, junction are associated with depressive symptom scores.

4.2. Helplessness and hopelessness

In a novel approach examining modular organization and brain network modularity in relation to clinical, symptoms, Peng et al. (2014) detect five atypically organised modules in a small sample of 16 first-episode, medication naïve patients in comparison to healthy controls. In a second step they correlate these modules with various clinical, symptoms via the Hamilton depression rating scale (HAMD), and find a positive correlation between helplessness and alterations within the intra-module I. This module comprises 34 brain regions and is mainly responsible for the DMN in resting- state, cognition control and strategic/executive functions. Together with a decreased global, functional, connectivity including areas of the DMN they claim that altered patterns of brain modularity and connectivity may contribute to the feeling of helplessness in MDD. Moreover, a study by Yao et al. (2009) using the ReHo approach shows that hopelessness severity is positively correlated with ReHo in the right ventral, ACC and right insula.

4.3. Suicidal, tendency

Since suicidal, tendency is very common in MDD, Fan et al. (2013) explore resting state brain activity in depressed patients with and without a history of suicide attempt using the ALFF approach. They show that patients with a history of suicide attempt display an increased ALFF in the right superior temporal, gyrus (STG), and those without suicide attempt present an increased ALFF in right ventral, middle frontal, gyrus (MFG). Therefore they claim that abnormality in the right STG may be a potential, marker spe- cifically for suicide tendency in MDD. Similarly, Cao et al. (2016) reveal, that MDD patients with a history of suicidal, attempt show an increased fALFF in the right superior temporal, gyrus, the left middle temporal, gyrus, and the left middle occipital, gyrus when compared with healthy controls and MDD patients without a history of suicide attempt. Moreover they find significantly decreased fALFF values in the left superior frontal, gyrus and the left middle frontal, gyrus claiming that these two regions could serve as a potential, predisposition to suicidal, behaviour in depression. In a large study using ICA, Zhang et al. (2016) explore RSFC changes in DMN regions comparing suicidal, with non-suicidal, MDD patients without differences in clinical, scores and healthy controls. Com- plementary to the former results, suicidal, patients display an increased connectivity in the left cerebellum and the left lingual, gyrus plus a decreased connectivity in the right precuneus under- mining the importance of abnormal, DMN connectivity in the neuropathology of depression and suicidality.

4.4. Clinical, characteristics of MDD

A vast number of studies have shown the correlation of clinical, characteristics with RSFC. They can be divided into three main groups relating to depression symptom severity, the onset of the disease, and the number or length of episodes:

Symptom severity is associated with several, alterations in brain function. Decreased intra-intrinsic functional, connectivity within the salience network's right anterior insula (Manoliu et al., 2014), increased FC of the anterior DMN (Coutinho et al., 2016), increased fALFF in the left dorsal, DMPFC (Guo et al., 2013b), and fALFF values in the right superior frontal, gyrus are positively correlated with depression severity, whereas fALFF values in the left insula are negatively correlated (Zhu et al., 2012b). Furthermore, Sheline et al.

(2010) reveal, that the FC of the dorsal, nexus (a cortical, area in the dorsomedial, prefrontal, cortex) is also correlated with HAMD scores, and Orosz et al. (2012) find a reduced perfusion in the DMN for patients with higher levels of MDD severity. Concerning cere- bellar FC and symptom severity, a negative correlation with HAMD scores has been shown by Liu et al. (2012a,b) for the FC between the bilateral, lobule VIIb and the right superior frontal, gyrus, while Chen et al. (2015) reveal, that a decreased FC between the left thalamus and the cerebellar tonsil directly correlates with higher depression scores. Guo et al. (2015) find significant correlations between the RSFC of the cerebellar crus I with the bilateral, MPFC and scores in the automatic thoughts questionnaire, a measure for depressogenic thought frequency. Lai and Wu (2014) show that the strength of interhemispheric connectivity is negatively correlated with clinical, severity of MDD. Finally, Gong et al. (2017) explore network alterations within the reward circuit and its correlation with depression severity taking the bilateral, nucleus accumbens (NAc) as seed region. They reveal, that disrupted network organi- zation within the reward circuits is significantly associated with depression severity.

Concerning the onset and length of depressive episodes, resting state functional, connectivity between the sgACC and the pre- cuneus is increased in later onset of depression (Schilbach et al., 2014), while Davey et al. (2015) show that the onset of MDD cor- relates with amygdala-subgenual, ACC connectivity and negative affect. Concerning the number and length of episodes, Greicius et al. (2007) reveal, that the length of the current episode in a group of MDD patients is positively correlated with FC in the sub- genual, ACC. Zhang et al. (2011) reveal, in a graph theory analysis that altered nodal, centralities in the left hippocampus and left caudate nucleus are correlated with disease duration (and severity), whereas Meng et al. (2014) find a significant association between nodal, degree of the nucleus accumbens connectivity and the number of episodes.

4.5. Subclinical, depression

Recent studies suggest that individuals with subclinical, levels of depression show similar relationships between the severity of depressive symptoms and functional, connectivity alterations compared to patients with MDD. Investigating functional, con- nectivity in 28 participants with subclinical, levels of depression, Philippi et al. (2015) find an association between depression severity and functional, connectivity of the anterior cingulate cor- tex ACC subregions. In particular, higher depression severity was associated with reduced connectivity between the pregenual, ACC and the striatum, reduced connectivity between the subgenual, ACC and the anterior insula, increased connectivity between the subgenual, ACC and the posterior cingulate cortex (PCC), and increased connectivity between the subgenual, ACC and the dorsolateral, prefrontal, cortex. Combining the fALFF technique with a seed based functional, connectivity analysis, Wei et al. (2014) reveal, altered fALFF in the superior parietal, lobule and left lingual, gyrus in 17 college students with subclinical, depression compared to healthy controls. Furthermore, participants with subclinical, depression show reduced FC between the right superior parietal, lobule and the inferior frontal, gyrus as well as regions of the DMN, including the PCC, bilateral, inferior temporal, gyrus and the right parahippocampal, area. These findings link subclinical, depression with neural, substrates associated with MDD.

4.6. Functional, connectivity in depression as comorbidity and as neuronal, trait

In addition to findings in individuals with MDD, recent studies

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investigating functional, connectivity in various neurological, dis- orders commonly associated with depressive symptoms describe a relationship between distinct changes in functional, connectivity and symptom severity. Hu et al. (2015a) compare functional, con- nectivity in 20 depressed patients and 40 non-depressed patients with Parkinson's disease (PD). Compared to non-depressed pa- tients, PD patients with depressive symptoms show increased functional, connectivity between the left amygdala and the bilat- eral, mediodorsal, thalamus as well as between the right amygdala and the left superior temporal, gyrus. In a consecutive study, Hu et al. (2015b) find that patients with PD and depressive symp- toms yielded increased ALFF in the medial, cingulate cortex (MCC) and increased functional, connectivity between the left MCC and regions of the DMN, including PCC, precuneus, medial, prefrontal, cortex and inferior frontal, gyrus. Moreover, these alterations correlate with the severity of depressive symptoms. Investigating differences in functional, connectivity in patients with traumatic brain injury (TBI) with and without depressive symptoms, Han et al. (2015) reveal, increased functional, connectivity between the amygdala and regions of the SN and CEN as well as the DMPFC and thalamus in TBI patients with depressive symptoms compared to TBI patients without depression. Moreover, distinct connectivity changes in TBI patients with depressive symptoms correlate with the severity of distinct cognitive and affective symptoms. Riccelli et al. (2015) assess potential, changes in functional, connectivity in patients with multiple sclerosis and variable levels of depression. They find that higher severity of depression in patients with MS is correlated with lower functional, connectivity between the hip- pocampus and the orbitofrontal, and dorsolateral, prefrontal, cortex (DLPFC) as well as with the functional, connectivity between the amygdala and DLPFC.

To address the question, whether common FC findings might rather represent a neurobiological, trait for developing MDD, Chai et al. (2015) compare resting-state FC in 27 children (age range: 8e14 years) of parents with documented history of MDD and children of parents with no lifetime history of depression. Children with familial, risk of depression show decreased connectivity within the cognitive control network, decreased connectivity be- tween left dorsolateral, prefrontal, cortex and subgenual, ACC and increased connectivity between the right amygdala and the right inferior frontal, gyrus. Furthermore, a classification algorithm based on the resting state FC data show better sensitivity and specificity for detecting individuals at familial, risk compared to clinical, rating scales. Clasen et al. (2014) perform seed-based functional, con- nectivity analysis within the CEN in eleven adolescent women at high risk and thirteen adolescent women at low risk for developing major depression based on their parent's medical, history. Adoles- cent women at high risk display decreased connectivity between the right inferior prefrontal, region and several, nodes of the CEN, including the middle frontal, gyrus and the right supramarginal, gyrus. In addition, the severity of parents’ worst episode of depression is associated with lower levels of functional, connec- tivity between nodes of the CEN.

In summary, there are relatively few scientific investigations on correlations between RSFC and distinct depressive symptoms while a great number of studies associate depression severity with distinct RSFC alterations. This could be due to the complex in- teractions of depressive symptomatology and brain function as well as other limitations including small sample sizes/limited statistical, power, rating biases, and heterogeneity of single symptoms. Con- cerning rumination, the current research finds altered resting state brain function mainly in the ACC, regions of the DMN, the dorso- medial, and the dorsolateral, prefrontal, cortex while Hermesdorf et al. (2016) reveal, that MDD patients display a loss of synchrony between the two hemispheres, mainly in regions of self-referential,

and award processing (putamen, superior temporal, gyrus, insula, and precuneus).

Furthermore, the feeling of helplessness in MDD patients is associated with alterations within the DMN, right ventral, ACC, and right insula whereas the right superior temporal, gyrus, the left cerebellum and the left lingual, gyrus are claimed essential, when concerning suicidal, tendencies. In relation to depression severity, main correlations with RSFC alterations are found in the DMN, frontal, cerebellar and thalamic brain regions as well as the insula and the subgenual, ACC. Onset and duration of depressive episodes are correlated with amygdalar-subgenual, ACC connectivity respectively altered nodal, centralities in the left hippocampus and left caudate nucleus.

Although numerous studies revealed altered resting state FC in patients with depression, it is yet unclear whether these findings represent a surrogate for the disease state, certain characteristic symptoms or the trait neurobiological, underpinnings of risk for developing MDD. Taken together, recent findings suggest that re- ported data might not only reflect disease-specific alterations, but might also represent neuronal, surrogates of disease specific symptoms and/or neuronal, traits, which might be used as bio- markers to identify individuals with an increased predisposition towards developing MDD.

5. Antidepressant treatments and resting state brain functional, connectivity

A large number of studies have examined the effects of anti- depressant medication (ADM) (Papakostas et al., 2006; Fournier et al., 2010) including its influence on brain function (Willner et al., 2013) as well as the neurobiological, basis or substrate (Etkin et al., 2005), and consequences of psychotherapy (Linden, 2006; Varma and Karadag, 2012; Sharpley, 2010) for a more detailed understanding of the neuropathologies underlying MDD. The effects of alternative treatment options, especially electrocon- vulsive therapy (ECT) (Pagnin et al., 2004; Leaver et al., 2016; Lyden et al., 2014) and repetitive transcranial, magnetic stimulation (rTMS) (Blumberger et al., 2013; Berlim et al., 2014; Noda et al., 2015) have been subject of broad scientific discussion. In relation to therapeutic interventions and resting state brain function, the main studies deal, with medication, psychotherapy, and compari- sons of treatment-resistant versus treatment-sensitive patients using different methods.

6. Antidepressant medication (ADM)

6.1. Ketamine in healthy subjects and MDD

Exploring potential, beneficial/antidepressant effects of keta- mine in healthy subjects, Scheidegger et al. (2012) show that intravenous application of ketamine induces decreased functional, connectivity of the DMN to the dorsal, and pregenual, anterior cingulate, and medial, prefrontal, cortex via the PCC. In addition, Wong et al. (2016) reveal, that after ketamine administration to healthy subjects the subgenual, ACC activity becomes negatively correlated with the brainstem, hippocampus, parahippocampal, gyrus, retrosplenial, cortex, and thalamus. They claim that this reduction of subgenual, ACC connectivity with other brain regions may represent in part its antidepressant effect. Finally, the effects of duloxetine treatment are further explored in van Wingen et al. (2013) in healthy subjects revealing a reduction of RSFC within the DMN and the task positive network but no correlation with mood changes. On the other hand, Abdallah et al. (2016) explore the alterations of network connectivity by a single ketamine treatment using global, brain connectivity with global, signal, regression

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(GBCr) values in medication-free patients with MDD and healthy control subjects. These values are computed as the average of correlations of each voxel with all other gray matter voxels in the brain. The MDD group reveals a significant increase of GBCr in the prefrontal, cortex, a significant decrease of GBCr in the cerebellum and an overall normalization of connectivity between the PFC and the rest of the brain after treatment.

6.2. Treatment response and medication status

In a large study, Wang et al. (2014) compare responders to non- responders of short-term two week ADM showing significantly increased ALFF in the right lingual, gyrus, and decreased ALFF in the left cerebellar lobe for successful treatment. They claim the lingual, gyrus as the target region for AD treatment since its activity changes are correlated with short term reduction of symptom severity in both groups. Confirming former studies, responders and non-responders both display reduced ALFF of the PCC. Cullen et al. (2016) focus on the resting state functional, connectivity of the amygdala because of its role in negative emotions before and after receiving AD treatment with a selective serotonin reuptake inhib- itor for 8 weeks. They reveal, that treatment response is associated with both increased amygdala RSFC with the right frontal, cortex, and decreased amygdala RSFC with the right precuneus and right posterior cingulate cortex. Qin et al. (2015) explore the predict- ability of clinical, response and medication status (drug-naïve versus medicated patients) in MDD using a multivariate pattern whole brain analysis and a linear support vector machine classifier. Medication-naïve patients can be identified with 100% accuracy, and more importantly the most discriminative functional, con- nections are located within the DMN, affective network, sensori- motor network, and the cerebellum. After ADM 30% of these discriminative connections are normalised. Finally, Sikora et al. (2016) show in a combined placebo-medication study in MDD patients that increased baseline RSFC in the rostral, anterior cingulate within the SN is positively correlated with a response to one week of active placebo and ten weeks of ADM.

6.3. ADM treatment effects

Two studies using graph theory reveal, modulations of the DMN and fronto-limbic networks by AD treatment. Wang et al. (2015) finds a significant reduction of DMPFC functional, connectivity strength after AD treatment, the alteration being correlated with symptom improvement. Furthermore, patients show an increase in bilateral, hippocampal, connectivity strength, which is reduced at baseline. Since early improvement of symptoms is a predictive of later treatment outcome in MDD, Shen et al. (2015) examine the correlation of Hamilton depression rating scores (HAMD) for depression severity with sub-hubs in MDD following two weeks of ADM. A significant correlation of baseline connectivity degree centrality with early treatment success is found in the posterior DMN (left precuneus, supramarginal, gyrus, middle temporal, gy- rus, and - with the highest specificity and sensitivity - in right angular gyrus), and frontal, regions.

Exploring structural, and functional, changes of AD treatment in MDD at two time points Fang et al. (2015) reveal, a decreased gray matter volume and ALFF in patients versus healthy controls at baseline in multiple brain areas, and an increased gray matter volume and ALFF in some of these areas after treatment (compared to pre-treatment). However, some of the regions remained with a decreased gray matter volume and ALFF compared to healthy controls. They suggest that the alterations in different brain areas concerning structural, and functional, changes may be understood as partly independent effects of AD treatment. Wang et al. (2014) on

the other hand do not detect a difference in ReHo at baseline be- tween remitted and non-remitted patients but after eight weeks of treatment with escitalopram the patients versus healthy controls show a decrease in ReHo in the right precuneus, and an increase in the left anterior lobe of the cerebellum. Compared to baseline, patients display altered ReHo in fronto-limbic regions including the DLMPFC, the right insula, bilateral, thalamus, and right superior frontal, gyrus as potential, medication treatment effects.

In a seed-based analysis, Li et al. (2013) examines two former described sub networks of the DMN: the anterior sub network (medial, prefrontal, cortex, PCC and bilateral, parietal, cortex), and the posterior sub network (bilateral, precuneus, parts of the bilat- eral, parietal, cortex). They show that AD treatment has a selective effect on the posterior network claiming that alterations of the anterior network may serve as a biomarker for asymptomatic depression. Similarly, Posner et al. (2013) investigate the effect of AD treatment on DMN connectivity in patients with dysthymic disorder. Patients show increased DMN connectivity before treat- ment (between the PCC and bilateral, MPFC, bilateral, parietal, lobes and precuneus), and a normalization of DMN connectivity following AD treatment. Finally, Lai and Wu (2012) studies the in- fluence of six week therapy with duloxetine on ReHo proving that differential, modulations in the DMN are associated with im- provements of clinical, symptoms.

6.4. Treatment resistance versus treatment sensitivity to ADM

Treatment resistant depression is a severe medical, condition which needs multimodal, treatment (Fekadu et al., 2009; Trivedi et al., 2011), and its pathophysiology is only partly understood (Coplan et al., 2014; aan het Rot et al., 2009). A number of studies examine FC alterations at rest comparing treatment resistant with treatment sensitive patients. De Kwaasteniet et al. (2013) explore alterations of the described three core neurocognitive networks in depression (SN, CCN, DMN), and compare a non-treatment resis- tant with a treatment resistant patient group. Treatment resistant depressed participants show disturbed FC between the CCN and the DMN, and between the anterior and posterior DMN albeit no al- terations of the SN. Three studies use the ReHo approach. In one of the first studies, Guo et al. (2011) find altered ReHo in treatment resistant MDD in multiple brain regions including frontal, temporal, and occipital, cortex, and the cerebellum. Consequently, Guo et al. (2012) use the coherence-based ReHo approach to compare both groups with similar results for both patient groups versus healthy controls. They reveal, through further correlation analyses that specific alterations of ReHo in the bilateral, cerebellum can distin- guish between treatment sensitive and treatment resistant partic- ipants. This hypothesis is supported by another study of Guo et al. (2013a) using a cerebellar seed-based method showing cerebellar- DMN coupling (decreased FC between the cerebellum and regions within the DMN) to be specific for treatment resistance in MDD. Similar results of altered ReHo in treatment resistant depressed is revealed by Wu et al. (2011) with a significant intensification of reduced ReHo in frontal, parietal, and temporal, cortex, and the caudate, while increased ReHo is found within temporo-limbic structures. Lui et al. (2011) analyse the FC of 13 seeds in treat- ment resistant depression showing its pronounced connectivity alterations mainly in thalamo-cortical, networks while non- refractory depression is associated with a more distributed decrease in connectivity in the limbic-striatal-pallidal-thalamic circuit.

In a combined study comparing MDD and bipolar disorder pa- tients with healthy controls, He et al. (2016) analyse functional, network connectivity in patients with treatment resistant depres- sion versus patients with treatment sensitive depression. They find

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distinct network dysfunctions for patients with treatment sensitive depression mainly in the fronto-parietal, control network and a complex dysfunction for patients with treatment resistant depression in the salience, affective, auditory, visual, and language processing network argueing that treatment resistance may correlate mainly with disrupted emotion processing and affective cognition. Finally, Karim et al. (2016) show that there is a difference in network connectivity between remitters and non-remitters after pharmacotherapy with SSRI venlafaxine over twelve weeks: Re- mitters display increases in the executive control network con- nectivity in the right precentral, gyrus and decreases in the DMN connectivity in the right inferior frontal, gyrus and supramarginal, gyrus.

Concerning targets and mechanisms for antidepressant therapy and RSFC, there are relatively few studies. Bosch et al. (2013) examined sleep deprivation's neurobiological, effects in healthy adults since chronobiological, therapy is proven to be effective as an antidepressant measure (Dallaspezia and Benedetti, 2011). They show that sleep deprivation's main effect is an increase of the DMN's positive correlation to the dorsal, nexus, claiming that changes in prefrontal, connectivity with the dorsal, nexus may be a brain mechanism of antidepressant therapy.

6.5. Psychotherapy and neurofeedback training

A limited number of studies explore the effect of psychothera- peutic interventions on RSFC. Crowther et al. (2015) reveal, that in a small group of patients that underwent twelve sessions of Behav- ioural, Activation Treatment, psychotherapy response is predicted by pre-treatment FC of the right amygdala with the right middle temporal, gyrus and pre-treatment FC of the left intraparietal, sulcus with the orbital, frontal, cortex (OFC). Furthermore, the magnitude of pre-treatment MDD symptoms is correlated with the precuneus to left middle temporal, gyrus, and the dorsal, ACC to parahippocampal, gyrus functional, connectivity. Using the ReHo approach, Huang et al. (2014) examine functional, brain changes associated with five weeks of guided imagery therapy in MDD. ReHo in the ventromedial, prefrontal, cortex (VMPFC) and the ACC are increased by therapy and higher pre-treatment ReHo in the VMPFC is positively correlated with treatment response.

Neurofeedback training represents a novel experimental, method in MDD treatment and can be monitored by real-time fMRI (Young et al., 2014; Scharnowski et al., 2014; Brühl et al., 2014; Bruehl, 2015). In a group of 27 depressed patients versus healthy controls, Yuan et al. (2014) examine RSFC modulation and sus- tained changes after real, time fMRI neurofeedback training. They find that abnormal, hypoconnectivity of the left amygdala is reversed after one session of training. Post-treatment larger de- creases of depression severity are associated with larger increases of amygdala connectivity, and there is a positive correlation be- tween connectivity changes and days after neurofeedback. Furthermore, neurofeedback training of the amygdala enhances its connectivity with temporal, cortical, regions including the hippocampus.

6.6. Neuromodulation (ECT and rTMS)

6.6.1. Electroconvulsive therapy Neurobiological, effects of ECT as a non-invasive antidepressant

therapy are first shown in a small sample of six patients by Beall et al. (2012) revealing an increase of RSFC of the ACC with the right dorsolateral, prefrontal, cortex and with the PCC after (suc- cessful) ECT. In a group of eight patients, Abbott et al. (2013) reveals that an increase of FC in several, neural, networks and argues that this increase may be a potential, biomarker for treatment success or

recovery from depression, as remitted patients display a pattern of increased functional, network connectivity between the posterior DMN and left DLPFC. In a further study with 19 patients they show an increase respectively a normalization of former low hippocam- pal, connectivity in ECT responders (Abbott et al., 2014). Focusing on prediction of the outcome of ECT, van Waarde et al. (2015) use a multivariate pattern analysis to find distinct brain networks which could discriminate remitters from non-remitters. The highest specificity and sensitivity is found for two networks. The first network, centred in the DMPFC, included the DLPFC, OFC and PCC, and shows a positive predictive value of 88%. The second network, centred on the ACC, included the DLPFC, sensorimotor cortex, parahippocampal, gyrus, and midbrain reaching an 80% positive predictive value. In contrast, the prediction accuracy based on structural, gray matter images is not significant. Furthermore, using the ALFF approach, Liu et al. (2015) reveal, a negative correlation between the reduction in depressive severity with subgenual, ACC activity, and the connectivity of the left subgenual, ACC with the contralateral, middle temporal, pole. They therefore claim that the AD effect of ECT is correlated with subgenual, ACC brain activity and functional, network connectivity. To assess the effect of ECT treat- ment in patients with treatment-resistant depression, Argyelan et al. (2016) investigate changes in fALFF and seed-based connec- tivity in 16 patients before, after the first ECT administration and after the course of ECT treatment. They find higher fALFF in the subcallosal, cingulate cortex (SCC) for patients at baseline, which decreases over the course of treatment. Furthermore, the baseline level of fALFF in the SCC predicted the response to ECT. In addition, seed-based connectivity analysis yields a decreased connectivity between SCC and the bilateral, hippocampus, bilateral, temporal, pole and ventromedial, prefrontal, cortex, further underpinning the potential, role of resting state connectivity as a biomarker for treatment success applying ECT. Lastly, Mulders et al. (2016) show that a decreased network coherence in the precuneus and the angular gyrus of MDD patients is normalised in ECT responders but not in the non-responder group.

6.7. Repetitive transcranial, magnetic stimulation (rTMS)

A recent European expert commission revealed a level B recommendation (probable efficacy) for therapeutic application of rTMS applied on the DLPFC in MDD (Lefaucheur et al., 2014). The effect on RSFC through rTMS as a therapy is shown in Fox et al. (2012) by examining why some left DLPFC targets are more effec- tive than others. In a group of 13 patients they prove that the an- tidepressant efficacy of the different stimulated left DLPFC sites is anticorrelated with each site of the subgenual, ACC. Obtaining similar results, Baeken et al. (2014) reveal, that a high anti- correlation between the subgenual, ACC and the left PFC results in beneficial, outcome of accelerated high frequency rTMS. In a recent PET study, Baeken et al. (2015) endorse this claim by demonstrating that higher baseline subgenual, ACC metabolic activity correlates with the clinical, outcome of accelerated high frequency rTMS treatment. The importance of subgenual, ACC RSFC for treatment response is also shown in Salomons et al. (2014) in a larger sample of unipolar and bipolar depressed. Subjects improving the most in depressive symptoms display an increased anti-correlation of FC between the subgenual, ACC and the DMPFC as well as between the DMPFC and the insula. Furthermore, subjects responding badly show increased subgenual, ACC-caudate connectivity. The authors claim that treatment response corresponds with a relative shift in caudate connectivity with ventral, (sgACC) as compared to dorsal, (DMPFC) regions. Liston et al. (2013) examine the effect of rTMS on the CEN and the DMN in relation to treatment response showing that both network's functional, connectivities are modulated by

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TMS. At baseline, the FC of the depressed participants is reduced within the CEN while it is increased within the DMM. Repetitive TMS significantly reduces the elevated FC within the DMN as well as the internetwork connectivity of the subgenual, ACC, the DMN and the CEN. Finally, Kang et al. (2016) compare RSFC of active rTMS with sham rTMS in MDD patients finding a greater reduction of connectivity strength between the dorsal, lateral, prefrontal, cortex and the left caudate in the active group, and a positive correlation of this reduction with symptom improvement.

Considering a possible treatment response to rTMS, Philip et al. (2014) explore the effect of early life stress (ELS) events on brain regions targeted by rTMS in a small sample of 14 healthy adults with and 13 healthy adults without ELS events. They reveal, an increased connectivity of the CEN and the DMN, and a larger dissociation of both networks in the ELS group. They therefore suggest an assessment of ELS before treating patients with rTMS.

Different therapeutical, options in MDD and their effects on brain function are heterogeneous as studies yield diverse results. Pharmaceutical, treatment reveals RSFC alterations throughout multiple brain regions notably the DMN, fronto-limbic, and parieto-temporal, regions. Several, biomarkers for ADM effective- ness are proposed including the normalization of DMN functional, connectivity, the connectivity degree centrality of its posterior sub network, and the modulation of FC within prefrontal-limbic areas. The functional, disruptions of treatment resistant MDD comprise various functional, brain networks among others the DMN, the CCN, and cerebellar circuitries. Concerning the outcome of psy- chotherapeutical, interventions, further research is necessary as first results show significant FC alterations in fronto-limbic net- works. Comparisons of different types of psychotherapies therefore could be beneficial, in order to understand general, neurobiological, mechanisms of MDD treatment. Electroconvulsive therapy and repetitive TMS as main alternative non-invasive treatment options for MDD reveal, distinct alterations of RSFC. Regarding ECT, increase of FC in the hippocampus, the sgACC and between the posterior DMN and the DLPFC is shown to be beneficial. In rTMS studies, FC of the stimulated DLPFC as well as reduced FC of the subgenual, ACC with the CEN and DMN is correlated with treatment response, and brain regions targeted by rTMS show increased connectivity in patients with ELS events. Finally, novel experimental, treatment approaches like neurofeedback training underline the relevance of altered brain dynamics in MDD, and the possibility of sustained changes in RSFC through therapeutic interventions.

7. Conclusion

Within the last decade, a growing body of research applied different neuroimaging techniques to study resting state brain function and dysfunction in MDD. Findings of altered resting state functional, connectivity are linked to the neuropathology of MDD and appear to be relatively heterogeneous comprising multiple regions and functional, networks in the brain including frontal, parietal, temporal, and limbic areas as well as the cerebellum and basal, ganglia. This heterogeneity may express the complex in- teractions of brain regions in depression along with the heteroge- neity of MDD phenotypes and its symptomatology. Other important reasons for the diversified results are the large number of different methods, data acquisition and statistical, models used. Furthermore, it is of great importance to emphasise that several, recent meta-analyses investigating altered brain activity in patients with MDD found not only a lack of convergence across neuro- imaging studies but described also problems regarding the repli- cation of neuroimaging findings (Mueller et al., 2017), questioning the substantiality of the presented results.

The majority of studies characterise MDD as a severe disease

with fundamental, disruptions in brain networks associated with emotion regulation, emotion processing, cognitive functioning, attention, salience, and sensorimotor processing. Despite these diverse results, crucial, connectivity alterations in MDD are related primarily to three major brain networks: The default mode network, the salience network, and the central, executive network. In summary it can be argued that there is large evidence of network dysfunction or disrupted network organisation as a neurobiolog- ical, cause for or consequence of MDD. The core role of the DMN and its disrupted functional, connectivity with multiple brain areas in MDD is supported by many studies using different methodolo- gies and investigating various patient groups. Alterations within the DMN and its connectivity patterns are revealed in different age groups, before and after antidepressant treatment as well as in relation to specific depressive symptoms, e.g. hopelessness, rumi- nation or suicidality. Furthermore, significant disruptions in RSFC are consistently reported in regions associated with salience pro- cessing, such as the ACC, as well as with mediating attention, such as the DLPFC and amygdala, providing further evidence for a rele- vant disruption of the interplay of top-down and bottom-up mechanisms in MDD.

To address the central, issue of result heterogeneity in brain imaging studies, the following recommendations emerge from this article. First, in order to understand dynamic changes caused either by the course of the disease or by antidepressant/psychothera- peutic treatment, both cross-sectional, and longitudinal, studies are required. As antidepressant treatment significantly affects func- tional, network connectivities of the brain, studies should rather include treatment-naïve patients, and compare their network structure with treatment response at a second time point. However, concerning the majority of patients which have already undergone antidepressant treatment, a characterization of potential, bio- markers can only be realised by longitudinal, studies. Similarly, treatment related alterations in resting state functional, connec- tivity in MDD are still not sufficiently understood, especially psy- chotherapeutic interventions. Comparing different treatments in a longitudinal, setting could therefore serve as a method to find possible biomarkers for treatment response, and eventually facili- tate the decision for which treatment should be administered (c.f. concept of personalised medicine). Herein, first differences in brain function between treatment responders and non-responders are found amongst others in the fronto-limbic areas and cerebellar circuitries. Second, the intensively studied subtypes of depression including distinct symptom clusters should be compared in larger samples to provide a more comprehensive understanding of het- erogeneity in the MDD phenotype/symptomatology, its neurobio- logical, abnormalities, and the most effective treatment options (Arnow et al., 2015). Third, a comparison of different brain net- works dysfunction involved in depression at rest and during various tasks and neurofeedback/real-time fMRI could give an insight into direct temporal, and permanent induced changes in brain function. Finally, comorbid depressive symptoms in other neuropsychiatric diseases could be analysed by computational, modelling studies and other methods, in order to distinguish distinct connectivity patterns of MDD versus depression as co- morbidity. Such studies could help to explore the question whether MDD resting state connectivity alterations reflect disease-specific alterations or rather represent neuronal, surrogates of disease specific symptoms or neuronal, traits.

However, one important aspect is still complicating the above mentioned suggestions. Currently, the definition of mental, disor- ders is still mainly based on a combination of distinct symptoms without taking objective biological, markers into account, resulting in a lack of independent gold standards for psychiatric diagnoses (Abi-Dargham and Horga, 2016). The recently proposed RDoC

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approach, which aims to identify disease-specific features of mental, disorders from the cellular to the behavioural, level might provide a suitable framework for identifying neuroimaging bio- markers for certain symptom dimensions per se instead of func- tional, alterations associated with nosological, classifications (Insel et al., 2010). Although it is unlikely that this approach will replace the current diagnostic frameworks in the near future, it might nevertheless promote a better understanding of distinct functional, and behavioural, entities associated with various mental, disorders and eventually allow for the development of therapeutical, ap- proaches directly targeting symptom-dimensions instead of mental, disorders as a whole (Abi-Dargham and Horga, 2016).

In summary, there is large evidence that functional, connectivity networks are altered in MDD, and that these networks are modu- lated by therapeutic interventions. In order to determine func- tional, connectivity as a biomarker for MDD and develop personalised treatments, future studies should find a common strategy for similar data acquisition and data analysis which would lead to a better comparability of the results. The resulting large data sets with comparable methods could provide reliable assertions on specific functional, connectivity alterations concerning depression symptomatology, treatment response and the personal, risk for developing MDD. At the same time, research on methods with higher reliability, sensitivity and specificity is vital, as studies which show a reliable and constant functional, connectivity in patients and healthy controls are lacking.

Conflict of interest

All authors declare no conflict of interest. No external funding was received for the current review.

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