1 INTRODUCTION

Working memory (WM) is a crucial cognitive competence when multiple goals are pursued. In addition, it is important to guide behavior on the basis of information that is not available in the direct environment (Braver & Ruge, 2006; D'Esposito & Postle, 2015). As an important part of higher cognitive functions, working memory enables us to temporarily hold and manipulate information with limited capacity (Baddeley, 2012). The n-back task is a popular working memory task which asks subjects to monitor a series of, for instance, verbal stimuli and to indicate when the currently presented stimulus is the same as the one presented n trials previously. Thus, online monitoring, updating, and manipulation of information are required (Owen et al., 2005).

Functional MRI studies consistently report a cortico-striatal network subserving WM processes. This executive control network (ECN) encompasses core hubs such as the dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), parietal cortex, and striatum (Murty et al., 2011; Tan et al., 2007). Working memory impairments and ECN dysfunction are consistently reported in psychiatric diseases such as schizophrenia or attention-deficit hyperactivity disorder (Jiang et al., 2015; Kofler et al., 2018). For instance, Jiang et al., (2015) investigated how neuronal activation is altered in schizophrenia using a n-back working memory task that has included a 0-back and a 2-back condition. They found patients compared to healthy controls showing an exaggerated response in the right DLPFC (Brodmann area (BA) 46) and bilateral VLPFC, as well as reduced activation in the bilateral DLPFC (BA 9). Jansma et al., (2004) used a parametric fMRI design and a spatial 3-back working memory task to examine relations between working memory load, performance, and brain activity in patients with schizophrenia taking second-generation antipsychotics compared to healthy controls. Despite the increasingly poor performance in patients, activity increased normally in DLPFC and inferior parietal cortex bilaterally as well as in the anterior cingulate cortex (ACC) with increasing working memory load. At the 3-back condition, activity dropped in the DLPFC in comparison with controls, but not in the other regions. The results indicate that peak activation of the WM system is reached at a lower processing load in patients than in healthy controls. As a decline of DLPFC activity at high processing loads in itself is not abnormal, WM dysfunction in schizophrenia was suggested to be the result of an impaired functional output of the whole WM system.

Prior work has especially focused on the role of the ECN in working memory, but recent research suggests that the ECN is just one of several interacting networks being critical for task engagement (Kamp et al., 2018; McCormick & Telzer, 2018; Vatansever et al., 2015). Specifically, the default mode network (DMN) has gained attention in this respect. The DMN refers to a resting-state network that is thought to show greater activity at rest. More specifically, the DMN tends to be active when one is engaging in internally driven cognitive processes such as self-generated thoughts, mind-wandering, or autobiographical memory retrieval (Andrews-Hanna et al., 2014; Raichle et al., 2001). Core hubs of the DMN are the posterior cingulate cortex (PCC) and the adjacent precuneus (Prec), medial prefrontal cortex (mPFC), mesial and inferior temporal lobes (mTL/iTL), and inferior parietal lobe (iPL) (Bär et al., 2016; Raichle et al., 2001). In most cognitive control tasks, where attention is directed externally, the ECN shows increases and the DMN decreases in neuronal activation. Unsworth and Robison (2017) summarized that during attention control tasks, also including working memory tasks, lapses of attention and mind-wandering are related to a reduced ECN and increased DMN BOLD activation. In consequence, goal-directed behavior often worsens. Thus, in many cognitive control tasks where attention has to be allocated to external stimuli interactions between the ECN and DMN are critical for success. The ECN is needed to maintain task goals and prevent lapses of attention by suppressing the DMN.

In patients with schizophrenia, many fMRI studies have demonstrated abnormal activity within the DMN during a broad range of tasks (Hu et al., 2017), also including working memory tasks (Kim et al., 2009; Pomarol-Clotet et al., 2008). Reduced suppression of the DMN in patients with schizophrenia is often interpreted as a failure to allocate cognitive resources adequately resulting in an impaired task performance. However, whether reduced suppression of the DMN is better interpreted as the cause or the consequence of impaired cognition is still a matter of debate. Interestingly, in a study by Whitfield-Gabrieli et al., (2009) patients with schizophrenia were found to continue to exhibit reduced DMN suppression, especially in the MPFC, even when statistically controlling for cognitive performance as well as when analyzing an easy task condition in which patients performed comparable to controls.

One major candidate influencing PFC function in schizophrenia is the norepinephrine (NE) system which arises from the locus coeruleus (LC) in the brainstem. The LC-NE system widely projects throughout the cortex and is well suited to modulate widely distributed neuronal networks such as those engaged by the PFC during higher-order cognition. There is plentiful evidence in both animal and human studies that NE strongly modulates PFC function during cognitive processes, that is, working memory (Aston-Jones & Cohen, 2005; Durstewitz & Seamans, 2008; Moore et al., 1999; Robbins & Arnsten, 2009). In a recent network analysis, we found evidence that the LC is integrated into the ECN (Bär et al., 2016).

The importance of the LC-NE system for working memory and attention is also emphasized by psychopharmacological studies. For example, it has been shown that drugs increasing the central NE concentration (e.g., modafinil) lead to more subjective alertness and a better performance on some attention control and working memory measures (Chamberlain & Robbins, 2013). Modafinil has also been shown to be related to the deactivation of the DMN during task performance (Minzenberg et al., 2018). Furthermore, pharmacological manipulations typically depend on baseline levels of arousal, suggesting the importance of tonic NE levels in determining the attentional state (Coull et al., 2004; Smith & Nutt, 1996).

While a major prevailing hypothesis is that altered dopaminergic and/or glutamatergic signaling contributes to the development and etiology of schizophrenia, there is also evidence that the LC-NE system might be involved (Borodovitsyna et al., 2017; Yamamoto & Hornykiewicz, 2004). It has been proposed that the development of both positive (delusions, hallucinations, and thought disorder) and negative symptoms (affective blunting, inattention, and abulia) of schizophrenia might be related to NE dysregulation. For instance, Yamamoto and Hornykiewicz (2004) concluded that the psychopathology of positive and negative symptoms might be caused from hyper- and hypo-vigilant states of consciousness, respectively. Other imaging studies using positron emissions tomography also proposed hyper-activation of the temporal cortex and limbic areas, as well as hypo-activation of prefrontal areas as correlates of positive and negative symptoms, respectively (Andreasen et al., 1992; Silbersweig et al., 1995). Further, NE has been found to be elevated in both the blood plasma (Kemali et al., 1982) and cerebrospinal fluid of patients with schizophrenia, especially those with positive symptoms (Kemali et al., 1990; Lake et al., 1980). Postmortem studies have also reported increased markers for NE in the brains of patients who suffered from schizophrenia (Bird et al., 1980; Farley et al., 1978).

2 METHODS

3 RESULTS

3.1 Behavioral results and task performance

The repeated measures ANOVA of the number of correct reactions with the within-subject factor task (x-back, 1-back, 2-back, 3-back) and the between-subject factor group (healthy controls, HC; patients with schizophrenia, SZ) revealed a significant main effect of task (F(1.917, 183.987)=83.288, p <.001; partial η²=0.465), a significant main effect of group (F(1, 96)=35.098, p <.001; partial η²=0.268), and a significant interaction between task and group (F(1.917, 183.987)=28.45, p <.001; partial η²=0.229).

As indicated by post hoc t tests, patients performed significantly worse in the 1-, 2-, and 3-back condition compared to healthy controls (1-back: t(42.413)=2.556, p_FDR = 0.019, 2-back: t(45.116)=4.842, p_FDR = 0.002, 3-back: t(48.969)=5.738, p_FDR = 0.002). There was no significant group difference in the control condition (x-back: t(57.417)=1.566, p =.123). Figure 1 pictures performance accuracy in both groups for all task conditions (Figure 1A) on the left-handed side. On the right-handed side, the percent change of performance accuracy normed on the control condition for healthy controls and patients is pictured (Figure 1B).

3.2 Differential neuronal (de-)activation patterns within and between groups

In general, we found increased BOLD activation patterns during 2- and 3-back in core hubs of the ECN, especially the DLPFC and VLPFC, in both healthy controls and schizophrenia patients (Figure 2A&B (yellow-red color); Table S2, S3). Compared to x-back, we were also able to identify BOLD deactivations in core hubs of the DMN, that is, the PCC and VMPFC, in HC as well as in patients (Figure 2A&B (blue-green color); Table S2, S3).

As hypothesized, we found significantly increased BOLD activations in, for instance, the DLPFC, in the 2-back condition in HC compared to SZ patients (Figure 2c; Table S4). Surprisingly and in contrast to our expectation, we did not find differences in DLPFC activation between groups in the most demanding 3-back condition. However, in the 3-back contrast, we identified significantly increased BOLD activation patterns in regions associated with the DMN, especially the PCC, in patients compared to HC (Figure 2c; Table S4). This finding, however, met our expectation. There were no significant BOLD activation differences in the easy 1-back condition between both groups.

3.3 Neuronal activation patterns with regard to the transition from the 2- to 3-back condition

Overall, we found significant BOLD activation increases from the 2- to the 3-back condition in main regions of the ECN, that is, the DLPFC and VLPFC, in both healthy controls and patients (Figure 3A&B, Table 3A&B). The finding in HC accords well with the common view of the literature. However, we did not expect to find this activation pattern in patients. We also identified an increase in BOLD activation in the PCC, as a main hub of the DMN, from the 2- to the 3-back condition in patients only.

TABLE 3. Whole-brain within- and between-group fMRI analysis in healthy controls and schizophrenia patients in the 3-back > 2-back contrast

Region of activation	Right/Left	Brodmann Area	Cluster size	MNI coordinates			T value
				X	Y	z
A: HC 3-back > 2-back
Superior Parietal Cortex	R	7	1,506	45	−62.5	48.5	5.13
Angular Gyrus	R	39		51	−53.5	48.5	4.51
Inferior Parietal Cortex	R	40		54	−40	53	3.99
Medial Frontal Cortex	R	8	1,206	6	29	47	5.08
(pre) Supplementary motor area	R	6		19.5	23	62	4.44
Superior Frontal Cortex	R	8		18	45.5	48.5	4.01
Middle Frontal Cortex	R	10	422	45	48.5	−5.5	4.65
Inferior Frontal Cortex	R	10		42	51.5	3.5	3.79
Dorsolateral Prefrontal Cortex	R	9	378	40.5	17	39.5	3.83
Ventrolateral Prefrontal Cortex	R	45		54	24.5	23	3.64
Dorsolateral Prefrontal Cortex	R	46		52.5	21.5	32	3.61
B: SZ 3-back > 2-back
Middle Temporal Cortex	L	21	86,247	−64.5	−34	−5.5	6.10
Anterior Insula	R	13		48	−44.5	18.5	5.55
Dorsolateral Prefrontal Cortex	R	9		46.5	20	33.5	4.05
Ventrolateral Prefrontal Cortex	R	47		42	26	−7	4.13
Anterior Insula	L	13		−37.5	12.5	−11.5	4.74
Posterior Cingulate Cortex	L	23		−7.7	−43	38	4.25
Putamen	L			−18	14	0.5	5.49
Caudate Nucleus	L			−13.5	0.5	15.5	5.03
Medial Frontal Gyrus	L	8	2,919	−7.5	23	54.5	5.25
(pre) Supplementary motor area	L	6		−13.5	8	54.5	4.17
Superior Frontal Gyrus	R	8		4.5	33.5	53	4.16
C: SZ 3-back > 2-back > HC 3-back > 2-back
Superior Temporal Cortex	R	39	65,622	49.5	−44.5	18.5	5.64
Primary Motor Cortex	R	4		16.5	−32.5	59	5.56
(Pre) Supplementary Motor Area	L	6		−9	20	53	4.77
Ventrolateral Prefrontal Cortex	L	44/45		−49.5	15.5	3.5	3.92
Posterior Cingulate Cortex	R	23		1.5	−46	36.5	3.61
Middle cingulate Cortex	L	24		−4.5	5	38	3.68
Caudate Nucleus	L			−15	8	14	3.49
Cerebellum	L		558	−13.5	−40	−20.5	4.79
Somatosensory Cortex	L	2	709	−25.5	−35.5	71	4.22
Angular Gyrus	L	40		−24	−41.5	57.5	4.06
Somatosensory Cortex	L	3		−30	−32.5	59	4.04

Note

• Maxima of regions showing significant BOLD activation differences when comparing healthy controls as well as schizophrenia patients in the in the 2-back versus. 3-back condition at the whole-brain level (voxel-level p <.001 uncorr., cluster-level, p <.05, FDR corr.).
• Abbreviations: HC – healthy controls, SZ – schizophrenia; posterior cingulate cortex – PCC, Angular Gyrus – AnG, dorsolateral prefrontal cortex – DLPFC, ventrolateral prefrontal cortex – VLPFC, anterior insula – aIN, (pre) supplementary motor area – (pre) SMA, temporal cortex – TCx, cerebellum – Cereb.

Further, we identified BOLD activation increases in SZ patients compared to HC in core nodes of the ECN, for example, in the VLPFC, and the DMN, for example, in the PCC, when analyzing changes from the 2- to the 3-back condition (Figure 3c; Table 3C). The finding of increased PCC activation met our expectation of less deactivation in core regions of the DMN in patients compared to HC with increasing cognitive load.

In addition, we identified an increased BOLD activation pattern in the left LC (x=−4, y=−39, z=−24, t = 3.08, cluster size = 4, p <.001 voxel-level uncorr., p <.05 FWE voxel-level corr.) in SZ patients compared to HC in the 3-back > 2-back contrast (Figure 3d). In regard to this analysis, we used the spatially unbiased atlas template (SUIT) preprocessed brainstem/cerebellum functional images, and the LC as anatomical mask image (small volume correction, SVC; mask image based on Keren et al., 2009).

3.4 Correlational analyses

To get further insight in the role of the LC regarding dynamic network modulation, we extracted parameter estimates from the DLPFC (x = 40.5, y = 32, z = 35), as a core hub of the ECN, from the PCC (x=−9, y=−50.5, z = 30.5), as a core hub of the DMN, and from the LC (x=−4, y=−39, z=−24) in the between-group comparison (SZ 3-back > 2-back) > (HC 3-back > 2-back). We then correlated the LC BOLD activation with the BOLD activation in the DLPFC as well as with the PCC. Most interestingly, in SZ patients, we found significant positive correlations between both DLPFC (r = 0.56, p <.001) and PCC (r = 0.51, p <.001) BOLD activations with LC BOLD activation which was not the case in the HC group (Figure 4). These findings confirm our supposition of a relation between LC and DLPFC/PCC BOLD activation which varies between patients and controls.

3.5 Subgroup analyses

An additional aim of our study was to get insight in performance as a critical differentiating factor. Therefore, we compared the low- and high-performing SZ groups in the demanding 3-back condition. We identified significant BOLD activation patterns in the temporal cortex (TCx), hippocampus (HIPP), and amygdala as well as a medial prefrontal region (Figure 5a, Table S5A). Further, we found increased BOLD activation, for example, in the PCC/precuneus and VMPFC (Figure 5b, Table S5B), in the low-performing patient sample compared to HC. Comparing BOLD activation changes from the 2- to the 3-back condition between the low-performing SZ and the HC group, we found increased BOLD activation, for instance, in the left DLPFC, the anterior/posterior insula (a/pIN), HIPP, caudate nucleus, putamen, amygdala, SMA, PCC/Prec, and TCx (Figure 5c, Table S5C). No significant neuronal activation differences were found by comparing high-performing patients with control subjects in the most demanding task condition. Thus, the neuronal activation differences between patients and healthy controls seem to be driven by low-performing patients.

Using the SUIT preprocessed brainstem/cerebellum functional images and the LC as anatomical mask images (SVC; Keren et al., 2009), a significantly increased BOLD activation was detected in the 2- versus. 3-back condition in the low-performing SZ group compared to the HC group in the left LC (x=−4, y=−39, z=−23, t = 2.82, cluster size = 4, p <.005 voxel level (uncorr.), p <.05 FWE voxel-level corr.; Figure 5d).

4 DISCUSSION

The main findings of our present study were increased BOLD activations in the ECN with increasing task difficulty in both SZ and HC. In controls, increasing cognitive demand also led to decreased BOLD activation in the DMN. However, patients seemed not to be capable to maintain this DMN deactivation over conditions. Interestingly, in the interaction contrast between groups and conditions, we have identified increased BOLD activation in the LC in patients compared to HC. LC BOLD activation significantly correlated with both, the main hub of the ECN, that is, the DLPFC, as well as of the DMN, that is, the PCC. In a subgroup analysis, we discovered that most of the SZ versus. HC group differences were driven by the low-performing patient group. Most remarkably, in the low-performing patient group we found the LC being significantly correlated with both the DLPFC and the PCC once again. Thus, the LC seems to play an exquisite role in modulating neuronal activity of the ECN and DMN in a 3-back working memory task and might contribute to cognitive impairments in SZ.

Overall, we confirmed our hypothesis with respect to the behavioral results. Patients performed increasingly worse with rising task demands. Nevertheless, it was surprising that patients performed also quite well on both the 2- and 3-back conditions. Further, as indicated by scores on symptom rating scales, patients were not in an acute state of illness which might also contribute to the good performance.

Our finding of increased ECN BOLD activation, especially in the DLPFC, with increases in working memory load in healthy controls goes along with former research findings (Jansma et al., 2004). In patients with schizophrenia, we also observed increased BOLD activations in core regions of the ECN in regard of the 3- versus. 2-back contrast which was rather surprising. Various research groups are discussing whether PFC hypo- or hyper-activation observed in schizophrenia patients is related to the poor performance on cognitive tasks which is, in general, a feature of this patient group (Jansma et al., 2004; Jiang et al., 2015). Weinberger et al., (2001) proposed that even when patients with schizophrenia are able to keep up with processing demands, they do so less efficiently than controls and need to work harder to keep up which requires the recruitment of greater and/or less focused neuronal activity. Callicott et al., (2003) and Manoach (2003) have further proposed that there is an inverted U-shaped function between working memory load and PFC activation, such that increasing task demands are first associated with increasing activation, which then falls off after the subject's working memory capacity is exceeded. They argue that this curve is shifted to the left in schizophrenia, causing patients first to show more activation than controls at low task demands, but then to reach their maximum capacity earlier and thereafter show less activation. Another possibility is that increased PFC activation in schizophrenia reflects a failure of DMN deactivation (Greicius et al., 2003; Gusnard et al., 2001; Raichle et al., 2001).

In our present study, we consistently found core hubs of the DMN, that is, the PCC, being deactivated in both healthy controls and schizophrenia patients. However, in patients we found BOLD activation increases in the 3- versus. 2-back contrast in core hubs of the DMN. Thus, with increasing task demands patients seemed not to be capable to maintain DMN deactivation over conditions.

The ECN is needed to maintain task goals and prevent lapses of attention by suppressing the DMN (Unsworth and Robinsn, 2017). Accordingly, former research has suggested that individuals with higher working memory capacities demonstrate a stronger anti-correlation between ECN and DMN (Keller et al., 2015). Besides, Kelly et al., (2008) and Esterman et al., (2013) have found that greater DMN BOLD activation is associated with greater variability in response times. This finding suggests an association between attentional lapses and DMN activation. In fact, Kelly et al. found that the greater the negative correlation between ECN and DMN, the more consistent behavior was. The weaker the negative correlation between ECN and DMN, the more inconsistent behavior became.

However, when tasks require retrieval or access of information from memory, the ECN and DMN were also found to act together (Konishi et al., 2015; Smallwood et al., 2013; Spreng et al., 2014; Vatansever et al., 2015). Konishi et al., (2015) used a spatial n-back task and found, among others, increased BOLD activations in DMN regions. In this context, DMN activation was proposed to be crucial for, for example, task judgments depending on recollections based on memory. Spreng et al., (2014) also demonstrated that the DMN and ECN cooperate to perform a working memory task.

Considering our results, in schizophrenia patients we found both increases in ECN BOLD activation accompanying increasing task demands and DMN BOLD deactivation. However, patients seemed not to be capable to maintain this DMN deactivation with increasing memory load. Further, we found a strong positive correlation between the DLPFC and PCC which are core hubs of the ECN/DMN, respectively. Most interestingly, we have also found increased BOLD activation in the locus coeruleus in SZ patients compared to HC.

The LC-NE system plays a pivotal role in cognitive control (Unsworth & Robison, 2017). It is also known that working memory is highly dependent upon noradrenergic neurotransmission in the PFC. For instance, delay-related firing, an electrophysiological correlate of working memory, occurs in prefrontal neurons in response to a behaviorally relevant stimulus. This type of activation of PFC neurons is potentiated by activation of the α2A receptor and diminished by its antagonists. Thus, working memory might be improved or impaired (Wang et al., 2007). Further, the LC was found to be functionally integrated into the ECN in the resting condition (Bär et al., 2016). Regarding our present results, we suppose that the LC directly modulates proper ECN function by indirectly impacting on the DMN during task performance. Thus, an intact LC-NE system supports working memory function by influencing large-scale brain networks. One might even suggest that a highly synchronous neuronal activation between both the LC-NE system and core hubs of the ECN supports cognitive functioning. The network reset theory of Bouret and Sara (2005) suggests that a major function of NE is to reset ongoing brain activity in order to synchronize large-scale brain networks in preparation for responding. This, together with theories that the P3, which is an event-related potential being influenced by conflict monitoring and adaptation, is the result of NE release in the cortex suggests that network reset is a potential mechanism leading to coordinated cortical responses following task-relevant stimuli (Rawls et al., 2020). Subsequently, decreases in task performance, that is, with increasing cognitive demand, might also be driven by a desynchronization of the LC-NE system and the ECN. In consequence, attentional lapses are probable as well as increases in DMN BOLD activation and a decrease in ECN BOLD activity. What is more, it is assumed that individuals with high attentional control abilities are also characterized by enhanced cognitive control capacities, for example, working memory capacities, than individuals with low attentional control abilities. Thus, during cognitive task performance, desynchronization phenomena might sooner be present in individuals with lower cognitive capacities and reduced attentional control (Unsworth & Robison, 2017). This might be the case in individuals suffering from schizophrenia. As a result, on the neuronal level one might still see increased LC and ECN BOLD activation by, however, simultaneously increasing DMN BOLD activation. In this case, depending on task challenges, behavioral performance will also very likely worsen.

However, when task demands are either too easy or too challenging performance worsens (Aston-Jones & Cohen, 2005; Berridge & Waterhouse, 2003; Chamberlain & Robbins, 2013; Ramos & Arnsten, 2007). Specifically, when tonic LC activity is low (hypo-arousal), individuals are inattentive, nonalert, and disengaged from the current task leading to poor behavioral performance and little to none phasic LC activity in response to task-relevant stimuli. As tonic LC activity increases to an intermediate range (phasic response), attention becomes more focused and behavioral performance improves. However, as tonic LC activity increases further, individuals experience a more distractible attentional state (hyper-arousal and stress) leading to task disengagement, lowered LC phasic activity, and a reduction in behavioral performance. Animal experiments and psychopharmacological studies provide evidence in support of the noted inverted U relationship between the LC-NE system and behavioral performance (Aston-Jones & Cohen, 2005; Berridge & Waterhouse, 2003; Chamberlain & Robbins, 2013; Ramos & Arnsten, 2007). However, it might be assumed that hypo- and hyper-arousal states of the LC are accompanied by reduced large-scale brain network synchrony.

Subgroup analyses of our present study were performed to get further insight into neuronal activation changes, how these are related to LC BOLD activation, and whether performance is a critical differentiating factor which should be accounted for. Indeed, the subgroup analyses were very intriguing. We found the low-performing SZ group accounting for most of the between-group differences even though patient groups did not differ in pathology and medication equivalent doses. The high-performing SZ group exhibited similar neuronal activation patterns such as healthy controls. Even correlational analyses were driven by the low-performing patient group and, as was the case in the HC group, nonsignificant in the high-performing SZ group. Even after controlling for inferential logic, results remained constant.

Contrary to our expectation that LC BOLD activation is positively related to DLPFC and negatively related to PCC BOLD activation in HC but not in patients, we found significant relations between this brainstem structure and the core hub of the ECN as well as DMN in SZ patients. Further, no significant correlations were found in healthy controls. These relations in SZ patients, in particular the low-performing ones, can be ascribed as a constant effort of the LC-NE system to sustain or enhance ECN BOLD activation while deactivating DMN activity as a disruptive neuronal network activation. This relation is interpreted in order to optimize the attentional focus to improve or maintain task performance. In contrast, in high-performing individuals this continuous LC firing is not necessary because an optimal neuronal network interaction might have been initially initiated and maintained over the task. Finally, another explanation might be that LC allocates additional resources when ECN activation alone fails to optimally perform on a task. Thus, low-performing patients might strongly try to retrieve information from their memory to recall seen letters. In this case, ECN and DMN co-activation with the LC being the driving structure might be a helpful strategy.

Some study limitations should be discussed. Although sample sizes seem to be sufficient regarding the overall group analyses, formed subgroups were small, and thus, those results lack power and should be interpreted with some caution. Furthermore, future studies should pay attention to a more balanced sample distribution, especially regarding the ratio between female and male subjects. Moreover, it would be of immense interest to study healthy controls in a low-performance condition and see whether found neuronal patterns are similar to the ones found in patients. This is necessary to get further insight into the exact role of the LC-NE system regarding neuronal and large-scale brain network modulation. Unfortunately, in the present study we were not able to form a low-performing HC group because just four healthy controls could have assigned to this group. In this context, we want to note another disadvantage of the study. Because of the high-performance scores, we cannot rule out that task demands were not challenging enough to elucidate the particular role of the LC-NE system in light of large-scale brain network modulation. However, task demands allowed the allocation of balanced patient subgroups which lead to very interesting results which should be kept in mind. Furthermore, to analyze dynamic changes in neuronal activation over the entire length of the task as well as condition-specific, an event-related design would be most helpful. Thus, the dynamic role of the LC-NE system and its relation to large-scale brain networks could be determined further. Another limitation is the analysis of merely medicated patients with schizophrenia. Although we described patient subgroups in terms of drug treatment and found no significant differences in their distribution, an influence of medication on neuronal networks and corresponding neurotransmitter systems cannot be excluded. Accordingly, future studies should examine sufficiently large subgroups with respect to drug therapy to better understand the influence of psychopharmacological treatment on neuronal networks and neurotransmitter systems. Nevertheless, in our present study we divided the sample in high- and low-performing patients and found differences even though patient subgroups did not differ in terms of CPZ equivalent doses, drug distribution, or psychopathology.

In conclusion, our study adds to the growing body of research that demonstrates that higher-order tasks cannot be attributed to a single neural network. Instead, proper cognitive function depends on the coordinated activity of multiple brain networks in a flexible fashion, most probable being driven by the LC-NE system.

However, one should keep in mind that we have only considered one memory task and that we observed correlations which do not allow causal conclusions. Nonetheless, present results indicate interesting patterns of network interactions and, thus, can be considered as a basis for subsequent studies allowing deeper insights using approaches such as network-based analyses, Granger Causality, or Dynamic Causal Modeling.