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. 2018 Sep 10;40(1):163–174. doi: 10.1002/hbm.24362

Alterations in intrinsic fronto‐thalamo‐parietal connectivity are associated with cognitive control deficits in psychotic disorders

Rebekka Lencer 1, Li Yao 2, James L Reilly 3, Sarah K Keedy 4, Jennifer E McDowell 5, Matcheri S Keshavan 6, Godfrey D Pearlson 7, Carol A Tamminga 8, Elliot S Gershon 4, Brett A Clementz 5, Su Lui 2,✉, John A Sweeney 2,8,9
PMCID: PMC6289604  NIHMSID: NIHMS992427  PMID: 30260540

Abstract

Despite a growing number of reports about alterations in intrinsic/resting brain activity observed in patients with psychotic disorders, their relevance to well‐established cognitive control deficits in this patient group is not well understood. Totally 88 clinically stabilized patients with a psychotic disorder and 50 healthy controls participated in a resting‐state magnetic resonance imaging study (rs‐MRI) and performed an antisaccade task in the laboratory to assess voluntary inhibitory control ability. Deficits on this task are a well‐established biomarker across psychotic disorders as we found in the present patient sample. First, regional cerebral function was evaluated by measuring the amplitude of low frequency fluctuations (ALFF) in rs‐MRI BOLD signals. We found reduced ALFF in patients in regions known to be relevant to antisaccade task performance including bilateral frontal eye fields (FEF), supplementary eye fields (SEF) and thalamus. Second, areas with ALFF alterations were used as seed areas in whole‐brain functional connectivity (FC) analysis. Altered FC was observed in a fronto‐thalamo‐parietal network that was associated with inhibition error rate in patients but not in controls. In contrast, faster time to generate a correct antisaccade was associated with FC in FEF and SEF in controls but this effect was not seen in patients. These findings establish a behavioral relevance of resting‐state fMRI findings in psychotic disorders, and extend previous reports of alterations in fronto‐thalamo‐parietal network activation during antisaccade performance seen in task‐based fMRI studies.

Keywords: antisaccade error rate, antisaccade latency, functional connectivity, functional magnetic resonance imaging, psychoradiology, psychotic bipolar disorder, resting state, schizoaffective disorder, schizophrenia

1. INTRODUCTION

There is an increasing number of reports from patients with psychotic disorders of altered resting state physiology and functional connectivity in intrinsic brain systems. Several studies suggest alterations in thalamo‐cortical networks characterized by a pattern of reduced prefrontal‐thalamic connectivity and increased motor and sensorimotor system connectivity with the thalamus (Giraldo‐Chica & Woodward, 2017). In line with this, we previously reported altered functional connectivity in fronto‐thalamo‐striatal circuitry as part of the Bipolar and Schizophrenia Network on Intermediate Phenotypes (B‐SNIP) consortium study that was identified across patients with schizophrenia‐spectrum disorders and psychotic bipolar disorder (Lui et al., 2015). While there are now many studies indicating altered resting brain physiology in psychotic disorders, the neurocognitive significance of these deficits such as in cognitive control remains to be well established.

One widely used approach to study voluntary cognitive control in psychiatric patients has been the antisaccade task, which requires the suppression of a reflexive saccadic eye movement to a visual target presented in the periphery, and then a voluntary saccade in opposite direction (Hutton & Ettinger, 2006). Increased antisaccade error rates have been consistently reported across untreated first‐episode and chronically ill medicated patients with schizophrenia, schizoaffective disorder and psychotic bipolar disorder as well as in their unaffected relatives, supporting its use as a biomarker for psychotic disorders (Ettinger, Kumari, Crawford, et al., 2004; Harris, Reilly, Keshavan, & Sweeney, 2006; Harris, Reilly, Thase, Keshavan, & Sweeney, 2009; Karoumi et al., 2001; McDowell, Myles‐Worsley, Coon, Byerley, & Clementz, 1999; Radant et al., 2010). We recently replicated and extended these findings in a large cohort of patients with psychotic disorders from the B‐SNIP consortium (Reilly et al., 2014).

From neurophysiological studies of nonhuman primates and human brain imaging studies, the specific neural networks supporting antisaccade performance have been established. This network includes the frontal and supplementary eye fields (FEF and SEF, respectively), the dorsolateral prefrontal cortex (DLPFC), posterior parietal cortex (PPC), precuneus, anterior cingulate (ACC), and subcortical structures including the thalamus, basal ganglia, midbrain superior colliculus (SC), and cerebellum (Brown, Vilis, & Everling, 2007; Domagalik, Beldzik, Fafrowicz, Oginska, & Marek, 2012; Jamadar, Fielding, & Egan, 2013; O'Driscoll et al., 1995; Sweeney, Luna, Keedy, McDowell, & Clementz, 2007; Sweeney et al., 1996; Wegener, Johnston, & Everling, 2008). Additionally, studies of nonhuman primates using single cell recordings have pointed out the importance of intrinsic brain activity in prefrontal‐subcortical circuitry prior to trial onset as being critical for task performance (Koval, Lomber, & Everling, 2011).

In healthy human participants, antisaccade error rate and the latency of correct antisaccades have been associated with task‐related changes in fronto‐thalamo‐parietal networks during active task performance (Herweg et al., 2014; Jamadar, Johnson, Clough, Egan, & Fielding, 2015; Polli et al., 2005; Talanow et al., 2016). Resting state activity has only recently been related to antisaccade performance in healthy participants (Jamadar, Egan, Calhoun, Johnson, & Fielding, 2016; Marek, Hwang, Foran, Hallquist, & Luna, 2015) revealing associations of antisaccade error rate and response latency with modulations of resting state activity in orbitofrontal and ventrolateral prefrontal cortex as well as medial premotor regions (Cieslik, Seidler, Laird, Fox, & Eickhoff, 2016).

By taking advantage of noninvasive imaging approaches, the psychoradiology (https://radiopaedia.org/articles/psychoradiology), a subfield of radiology, is increasingly of clinical importance in guiding diagnostic and therapeutic decision making in patients with mental disorders (Lui, Zhou, Sweeney, & Gong, 2016). This prior literature indicates the potential utility of using antisaccade task performance to establish the neurobehavioral relevance of resting‐state fMRI data for cognitive control deficits in patients with serious mental illness. In patients with psychotic disorders, functional brain imaging studies have revealed abnormalities in fronto‐thalamo‐parietal networks during active antisaccade task performance that were associated with increased error rates independent from medication status (Ettinger, Kumari, Chitnis, et al., 2004; Fukumoto‐Motoshita et al., 2009; McDowell et al., 2002; Raemaekers et al., 2002; Tu et al., 2010). However, whether impairments of antisaccade performance in patients with psychotic disorders are also related to alterations of resting brain activity in fronto‐thalamo‐parietal brain systems remains to be established.

There were two hypotheses driving the present study. First, we predicted that increased antisaccade error rates in patients would be associated with reduced coherent activity in distinct brain regions associated with antisaccade performance, for example, FEF, SEF, and thalamus, and second that error rates would also be related to alterations of functional connectivity within this circuitry. Based on our previous findings of elevated antisaccade error rates across psychotic disorders in the B‐SNIP sample (Reilly et al., 2014) and similar disturbances of resting state measures in task‐relevant fronto‐thalamo‐striatal circuitry in clinically stable patients with schizophrenia‐spectrum and bipolar disorders (Lui et al., 2015), we tested these hypotheses in a combined patient sample with both affective and nonaffective psychotic disorders.

2. MATERIAL AND METHODS

2.1. Subjects

Antisaccade performance was assessed in 88 patients with a psychotic disorder (schizophrenia N = 32, schizoaffective disorder N = 13, and bipolar disorder with psychotic features N = 43), and 50 healthy control participants studied at the Chicago site of the B‐SNIP consortium as described previously (Tamminga et al., 2013). Diagnoses were made by a consensus process using all available clinical information including the Structured Clinical Interview for DSM IV (First, Spitzer, Gibbon, & Williams, 1995). Patients were clinically stable and receiving consistent psychopharmacological treatment for at least 1 month. Current symptoms and cognitive status were assessed using standard rating scales, Table 1 (Supporting Information Table S1).

Table 1.

Characteristics of patients with psychosis and healthy controls

Psychosis patients N = 88 Healthy controls N = 50 Comparison
Age, mean (SD) 33 (12.5) 35.0 (12.5) n.s.
Sex (% male) 57% 39% n.s.
% Caucasian 57% 60% n.s.
% African–American 32% 26% n.s.
WRAT 4 word Readinga, mean (SD) 100.3 (16.1) 103.5 (14.4) n.s.
BACSb, mean z scores (SD) −1.1 (1.4) 0.2 (1.0) t (136) = 5.4; p < .001
PANSSc positive, mean (SD) 15.8 (5.3)
PANSSc negative, mean (SD) 16.1 (6.0)
PANSSc Total, mean (SD) 65.3 (16.4)
YMRSd, mean (SD) 6.3 (6.1)
MADRSe, mean (SD) 10.8 (8.9)
Medication status
Chlorpromazine Equivalentsf, mean (SD) 325 mg (296)
Antidepressants, N (%) 45 (51%)
Mood stabilizer, N (%) 39 (44%)
Antisaccade performance
Error rate, mean (SD) 31% (20) 18% (12) t(136) = −4.35; p < 0.001
Antisaccade latency, mean (SD) 385 ms (74) 386 ms (54) n.s.
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a

Wide Range Achievement Test 4th ‐ Edition: Reading (Wilkinson & Robertson, 2006).

b

Brief Assessment of Cognition in Schizophrenia (Keefe et al., 2008), z‐scores are given relative to norms.

c

Positive and Negative Symptom Scale (Kay, Fiszbein, & Opler, 1987).

d

Montgomery Asberg Depression Rating Scale (Montgomery & Asberg, 1979).

e

Young Mania Rating Scale (Young, Biggs, Ziegler, & Meyer, 1978).

f

According to (Andreasen, Pressler, Nopoulos, Miller, & Ho, 2010).

Inclusion criteria for all subjects were: (1) age 15–65; (2) WRAT reading score ≥ 65 (Wilkinson & Robertson, 2006); (3) no history of neurologic illness or systemic disease; (4) minimum of 20/40 acuity (with or without correction), (5) no history of substance abuse within the last month or substance dependence within the last 3 months, and negative urine toxicology on assessment day, (6) no potentially sedating medication, that is, benzodiazepines, and (7) head translation <1.5 mm and rotation <1.5° during fMRI scanning (Li et al., 2016; Power, Barnes, Snyder, Schlaggar, & Petersen, 2012). Inclusion criteria for control subjects additionally included: (1) no personal or family history (first‐degree relative) of psychotic or bipolar disorder; (2) no history of recurrent depression; and (3) no history of psychosis spectrum personality traits defined as meeting full or within one criteria of a Cluster A (psychosis spectrum) Axis‐II diagnosis. The median time for completion of the full protocol was 2 days. T1 and T2 weighted magnetic resonance images were inspected by an experienced neuroradiologist to exclude significant structural abnormalities, for example, tumors, in all participants. The study was approved by the institutional review board of the University of Illinois at Chicago and written informed consent was obtained prior to study participation.

2.2. Eye movement testing

Antisaccade testing and analysis procedures have been described in detail previously (Reilly et al., 2014). Eye movements were acquired with a video‐based eye tracker in a darkened room (Eyelink II, SR Research Ltd., Kanata, ON, sampling rate 500 Hz). Before testing, participants refrained from nicotine or caffeine exposure for at least 30 min. They were seated 60 cm from a 22 in. CRT monitor (1,360 × 768 resolution; 150 Hz refresh rate) with their heads stabilized with a chin and forehead restraint. The target was a red cross in a box covering 0.5°. The antisaccade task consisted of 80 overlap trials divided into four blocks. After a period of central fixation (1500–2,500 msec), the central target was extinguished 200 ms after peripheral target appearance at either 10° or 15° to the left or right from center. Subjects were instructed to not look to the peripheral target (antisaccade error) but instead immediately look to the mirror image location in the opposite hemi‐field. The percentage of trials with antisaccade errors was recorded indicating failed inhibitory control. The mean latency of correct antisaccades for each subject was also determined, which reflects time to inhibit an antisaccade error and implement of task‐appropriate correct goal‐directed voluntary behavior.

2.3. Resting state fMRI data acquisition

Brain scans were conducted using a GE Signa EXCITE 3.0 Tesla MR imaging system and an 8‐channel phased array head coil. Resting‐state MR scanning was performed while subjects fixated a central crosshair for the duration of a 5 min scan. Video monitoring of participants' eyes confirmed adherence to this instruction. Soft ear plugs were used to reduce scan noise, and head motion was minimized with head cushions. Echo‐planar imaging (repetition time = 1,775 ms, echo time = 27 ms, flip angle = 60°) was performed with slice thickness of 4 mm (1 mm gap), a matrix size of 64 × 64 and a field of view of 220 × 220 mm2, resulting in a voxel size for analysis of 3.44 × 3.44 × 5 mm. Each brain volume was comprised of 29 axial slices, and each functional run contained 210 image volumes.

3. RESTING STATE DATA ANALYSES

3.1. Amplitude of low frequency fluctuations (ALFF)

Preprocessing steps were performed using the Data Processing Assistant for Resting‐State fMRI (DPARSF) toolbox that runs on the REST software platform (http://resting-fmri.sourceforge.net). The first five scans were discarded to establish magnetization stabilization. For each subject, EPI images were slice‐timing corrected, and realigned to the middle slice. The Friston 24‐parameter model (Friston, Williams, Howard, Frackowiak, & Turner, 1996) was used to regress out head motion effects from the realigned data, which has demonstrated benefits in removing head motion effects in recent reports (Friston et al., 1996; Satterthwaite et al., 2013; Yan et al., 2013). Motion scrubbing was not applied to avoid the possibility of losing data leading to a biased estimate of further distant connectivity, specifically in visual and posterior parietal regions (Zeng et al., 2014). Next, functional images were spatially normalized to the Montreal Neurological Institute (MNI) EPI template in SPM8, resampled to 3 × 3 × 3 mm3, and smoothed with an 6 mm full‐width at half‐maximum (FWHM) Gaussian kernel. Measurement of the amplitude of low‐frequency fluctuations of the BOLD signal (ALFF), considered to be related to regional spontaneous neural activity, was used to identify differences between the patient group and controls in regional resting cerebral function (Cordes et al., 2001). After bandpass filtering (0.01–0.08 Hz), signals from white matter and cerebrospinal fluid were removed. Following linear detrending, the voxel‐wise time series were transformed to the frequency domain using fast Fourier transformation to obtain the power spectrum. The ALFF measure at each voxel represents the square root of the power across a low‐frequency range. The ALFF value of each voxel was divided by the global mean ALFF value of each individual subject to standardize data for comparisons across subjects.

3.2. Functional connectivity analysis

To investigate functional circuitry alterations, we first identified regional group differences of ALFF values. These were used to create seed regions for functional connectivity analyses by creating a spherical region of interest centered on the voxel with peak group difference in ALFF data that had a radius of 3 mm. Voxels in the region of interest sphere were averaged to create the seed for FC analysis between eye movement relevant regions with altered ALFF values and the remainder of the brain.

The first step in the analysis was to extract a reference time series by obtaining the average of the time series of voxels within each sphere as described above. Correlations were then computed between the time series of the seed reference and all brain voxels outside the seed region. Finally, these correlation coefficients were transformed by Fisher r‐to‐z transformation to increase their normality before averaging correlations across participants and testing for associations with antisaccade task performance.

3.3. Statistical analyses

Voxel‐based comparison of ALFF maps from patient and control groups was performed using pairwise t tests in SPM8 (http://www.fil.ion.ecl.ac.uk/spm). Age, sex, and race were used as covariates in group comparisons. Connectivity was examined from the selected seed areas using pairwise t tests comparing patients and controls. In all image analyses, a threshold of p = .05 after AlphaSim correction was used to correct for multiple comparisons corresponding to a minimum cluster size of 80 contiguous voxels significant individually at a threshold of p < .01. The AlphaSim calculation based on Monte Carlo simulations was conducted using REST software (http://restfmri.net/forum/index.php).

To determine relations between alterations of mean ALFF and FC values with antisaccade performance measures, we used stepwise backward regression analysis that allows for consideration of several independent variables simultaneously. This procedure yielded a regression model using imaging parameters to predict antisaccade performance with a significance threshold for entry of 0.05 and that for removal of 0.10. Standardized z scores for antisaccade error rate and latency, derived from a normative regression approach described previously (Reilly et al., 2014), were entered as dependent variables while mean values of peak coordinate‐defined spherical regions of altered ALFF and altered FC clusters in patients were used as independent variables in these analyses.

4. RESULTS

4.1. Behavioral findings

As expected following our previous studies including the full B‐SNIP sample (Reilly et al., 2014), antisaccade error rate was considerably increased in patients compared to controls in the sample available for this study (Table 1). Error rate did not differ between the three patient groups (F(2,82) = 2.04, p = .14, Supporting Information Table S1). Despite increased antisaccade errors in patients, latencies of correct antisaccade responses did not differ between patients and controls, indicating that patients planned and initiated correct antisaccades as quickly as controls.

4.2. Alterations of ALFF and FC in patients

ALFF was reduced in patients compared to controls in FEF regions bilaterally, SEF region, and thalamus bilaterally, and in left orbitofrontal gyrus and left superior temporal gyrus (Table 2). Given their adjacent midline location, left, and right SEF were considered as one SEF region. Based on the established roles in eye movement activity of FEF, SEF, and thalamus, these regions were selected as seed areas for FC analyses. Note that clusters of voxels in FC analyses with these seeds include areas defined previously in task‐based fMRI studies using antisaccade paradigms (Herweg et al., 2014; McDowell et al., 2002), but were in some instances larger.

Table 2.

Regions with altered ALFF values in whole brain analyses in patients with psychosis (N = 88) compared to healthy controls (N = 50)

MINI coordinates: Peak voxel Cluster size p values*
Location x y z
Patients < controls
FEF region, left −39 −24 54 717 <.001
FEF region, right 63 −12 36 339 <.001
SEF region −6 −12 45 152 .01
Thalamus, left −12 −12 3 101 .03
Thalamus, right 15 −12 9 155 .01
Orbitofrontal gyrus, left −15 36 −9 113 .02
Superior temporal gyrus, left −54 −36 21 107 .03
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Abbreviations: ALFF, Amplitude of low frequency fluctuations; MNI, Montreal Neurological Institute; SD, standard deviation; FEF, frontal eye fields; SEF, supplementary eye fields.

*

A threshold of p = .05 after AlphaSim correction was used to correct for multiple comparisons using a minimum cluster size of 80 contiguous voxels significant individually at a threshold of p < .01.

FC analyses revealed a pattern of both increased and decreased connectivity in patients compared to controls. Table 3 lists all FC that differed between patients and controls.

Table 3.

Altered functional connectivity (FC) within oculomotor fronto‐thalamo circuitry in patients with psychosis (N = 88) compared to healthy controls (N = 50)

MINI coordinates: Of peak voxel Cluster size p value
Seed area Clusters with altered FC x y z
Patients > controls
FEF region, left Caudate, bilateral 15 12 −3 4,450 <.001
Inferior parietal gyrus, left −57 −42 39 261 .007
Inferior parietal gyrus, right 60 −39 48 340 .003
Precuneus, right 9 −72 45 172 .022
Middle frontal gyrus, left −36 48 15 281 .005
FEF region, right Thalamus, bilateral 18 −24 6 1,310 <.001
Thalamus, left FEF region, bilateral −48 −6 21 3,721 <.001
Thalamus, right FEF region, bilateral −51 −9 24 5,574 <.001
Parahippocampus, right 27 −21 −30 218 .017
Cuneus, bilateral −12 −75 27 256 .011
SEF region Thalamus, bilateral 21 −24 3 1,212 <.001
Anterior cingulate, bilateral 9 36 21 368 .002
Patients < controls
FEF region, left Angular gyrus, left −48 −75 39 273 .006
SEF region −6 −39 75 1,362 <.001
Precuneus, bilateral −3 −48 9 473 .001
Fusiform gyrus, right 42 −18 −24 204 .014
Middle temporal gyrus, left −57 −27 −3 421 .001
FEF region, right Superior temporal gyrus, left −54 −12 6 350 .003
Thalamus, right Middle frontal gyrus, right 30 57 12 506 .001
Middle frontal gyrus, left −36 36 48 149 .043
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Abbreviations: MNI, Montreal Neurological Institute; FEF, frontal eye field; SEF, supplementary eye field.

5. ASSOCIATIONS WITH ANTISACCADE PERFORMANCE

5.1. Antisaccade error rate

In patients, ALFF power was not associated with antisaccade error rate in any oculomotor region, including FEF, SEF or thalamus (adjusted R 2 = .0, standard error (SE) = 1.34, F = 2.03.59, p > 0.05). However, higher antisaccade error rate was associated with lower FC from left FEF to left inferior parietal gyrus (IPG), bilateral precuneus (PCUN), left middle temporal gyrus (MTG), and right fusiform gyrus (FFG), and higher FC from left FEF to left angular gyrus (ANG). Higher FC from SEF to bilateral thalamus (THA) and lower FC from right thalamus to right parahippocampus (PHG) and left middle frontal gyrus (MFG) were also associated with higher antisaccade error rate in patients. Together these alterations in FC explained 34% of the variance in antisaccade error rate in patients (adjusted R 2 = .34, SE = 1.12, F = 6.59, p < .001, Table 4). Figure 1 illustrates the findings from regression analyses, note that FC in red indicate that stronger FC was associated with higher error rates while blue FC indicate weaker association with higher error rates.

Table 4.

Associations of resting state activity with antisaccade measures in patients with psychotic disorders (N = 88) and controls (N = 50)*

Antisaccade error rate
Patients Beta t p value
FC FEF region, left, to Inferior parietal gyrus, left −0.16 −1.68 .09
Angular gyrus, left 0.31 3.10 .003
Precuneus, bilateral −0.38 −3.54 .001
Gyrus fusiformis, right −0.23 −2.42 .02
Middle temporal gyrus, left −0.19 −1.93 .06
FC SEF to Thalamus, bilateral 0.27 2.90 .005
FC thalamus, right, to Parahippocampus, right −0.43 −4.41 <.001
Middle frontal gyrus, left −0.20 −2.12 .04
Controls
ALFF in FEF region, left −0.27 −2.03 .05
ALFF in thalamus, left 0.43 3.27 .002
Latency of correct antisaccades
Patients Beta t p value
FC thalamus, right, to Cuneus, bilateral −0.25 −2.44 .02
Middle frontal gyrus, left 0.18 1.74 .09
Controls
FC FEF region, left, to Precuneus, right −0.32 −2.54 .02
Middle frontal gyrus, left 0.38 2.58 .01
SEF −0.25 −1.78 .08
Fusiform gyrus, right 0.29 2.07 .05
FC SEF to Thalamus, bilateral 0.31 2.13 .04
Anterior cingulate, bilateral −0.50 −3.25 .002
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*

Table depicts results from backwards regression analyses using ALFF and FC values as independent variables to predict antisaccade error rate and latency of correct antisaccades, respectively. Only results from final models that together best predicted antisaccade measures are shown.

Figure 1.

Figure 1

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ALFF and FC measurements associated with antisaccade error rate in patients and controls. Dots identify the location of peak group differences within clusters (Table 3). FEF frontal eye field region, SEF supplementary eye field region, THA thalamus, MFG middle frontal gyrus; IPG inferior parietal gyrus; PCUN precuneus; ANG angular gyrus; MTG middle temporal gyrus; PHG parahippocampal gyrus; FFG fusiform gyrus; L left; R right. Red lines indicate that stronger FC was associated with higher error rates while blue lines indicate that weaker FC was associated with higher error rates [Color figure can be viewed at wileyonlinelibrary.com]

In controls, higher antisaccade error rate was predicted by higher ALFF in left thalamus (THA) and lower ALFF in left FEF (adjusted R 2 = .18, .E = 0.77, F = 6.45, p = .003), Figure 1. In contrast to patients, error rates in controls were not associated with any FC differences between patients and controls (adjusted R 2 = .0, SE = 0.85, F = 0.5, p > .05).

5.2. Latency of correct antisaccades

There was only a weak association between antisaccade latency and resting state physiology in patients, Figure 2, with higher FC from right thalamus to bilateral cuneus (CUN) and lower FC from right thalamus to left middle frontal gyrus (MFG) predicting slower antisaccade processing speed (adjusted R 2 = .09, SE = 1.2, F = 5.24, p = .007).

Figure 2.

Figure 2

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ALFF and FC measurements associated with latency of correct antisaccades in patients and controls. Dots identify the location of peak group differences within clusters (Table 3). FEF frontal eye field region, SEF supplementary eye field region, THA thalamus, MFG middle frontal gyrus; ACC, anterior cingulate gyrus; CUN cuneus; PCUN precuneus; FFG fusiform gyrus, L left; R right. Red lines indicate that stronger FC was associated with longer latency while blue lines indicate that weaker FC was associated with longer latency [Color figure can be viewed at wileyonlinelibrary.com]

In contrast, in controls, longer latency of correct antisaccades was predicted by lower FC from left FEF to right precuneus (PCUN) and to SEF and higher FC from left FEF to left MFG and left fusiform gyrus (FFG; Figure 2). Additionally, longer antisaccade latency was predicted by higher FC from SEF to thalamus (THA) bilaterally and lower FC to anterior cingulate (ACC) bilaterally. Together, these FC patterns accounted for 27% of the variance in antisaccade latency in controls (adjusted R 2 = .27, SE = 0.78, F = 3.95, p = .003, Table 4). Note that in Figure 2, FC in red indicate that stronger FC was associated with longer latency while blue FC indicate weaker FC associated with longer latency. Antisaccade latency was not associated with ALFF in any oculomotor region in controls (adjusted R 2 = .0, SE = 0.90, F = 1.3, p > .05).

5.3. Associations with clinical variables

Alterations in regional ALFF and FC measures, which were associated with error rate and antisaccade latency, were not correlated with antipsychotic medication dosage, that is, chlorpromazine equivalents. Associations with clinical ratings were modest, consistent with the prior literature (Lui et al., 2015). PANSS scores only correlated with FC between SEF and bilateral thalamus (PANSSTotal r = .39, p < .001, PANSSPositive r = .36, p = .001 PANSSNegative r = .26, p = .02) but not with any other clinical rating or ALFF measures.

6. DISCUSSION

There are two important aspects to our findings from this first study to investigate associations of alterations of resting state brain physiology in fronto‐thalamo‐parietal networks with the well‐established biomarker of increased antisaccade error rates in patients with psychosis. First, our results indicate a behavioral relevance of altered resting state brain function in psychotic disorders, specifically to deficits in cognitive control. Second, our findings demonstrate that increased inhibition error rates in patients with psychotic disorders are related to predominantly weaker functional connectivity in intrinsic fronto‐thalamo‐parietal brain systems rather than to altered local activity in oculomotor areas, such as FEF, SEF, or thalamus.

In secondary analyses, we studied the relation between intrinsic brain activity and processing speed required to generate correct antisaccade responses. Consistent with studies in nonhuman primates, volitional saccadic behavior in controls was supported by a widely distributed fronto‐thalamo‐parietal network that includes the anterior cingulate (Jamadar et al., 2016; Marek et al., 2015). In patients, processing speed was associated with fewer significant functional connections in this circuitry.

Together, our findings extend previous reports from patients with psychosis showing alterations in a fronto‐thalamo‐parietal network during active antisaccade performance in task‐based fMRI studies (Ettinger, Kumari, Chitnis, et al., 2004; Fukumoto‐Motoshita et al., 2009; McDowell et al., 2002; Raemaekers et al., 2002; Tu et al., 2010). Thus, our findings provide new understanding of the brain substrate of this familial abnormality associated with psychotic disorders by providing novel evidence that similar network alterations are seen in resting‐state studies and that connectivity alterations rather than altered regional activity are more related to this domain of behavioral deficit on a task where resting physiology prior to trial onset is known to be an important factor relevant to task performance (Koval et al., 2011).

Previous rs‐MRI studies from patients with schizophrenia, schizoaffective disorder, and psychotic bipolar disorder demonstrated disruptions of intrinsic cortical network organization and integration including the sensorimotor network guiding attention and action toward external sensory stimuli, the fronto‐parietal network subserving executive control, and the default mode network (Baker et al., 2014; Buckner, 2013; Tu, Hsieh, Li, Bai, & Su, 2012). The areas with reduced ALFF in patients that we used as seed areas form part of the fronto‐striatal sensorimotor network including FEF and SEF regions, and thalamus (Moussa, Steen, Laurienti, & Hayasaka, 2012). As expected, we found that ALFF in FEF and thalamus was directly related to antisaccade error rates in controls, a finding that is in line with results from large meta‐analyses that included the few rs‐MRI studies in addition to fMRI studies with active antisaccade performance (Cieslik et al., 2016). It suggests that high intrinsic activity in FEF and low activity in thalamus contribute to keeping saccade inhibition error rates low. These associations were not significant in patients. Instead, error rates were associated with several measures of functional connectivity, including connectivity from FEF to nodes in the default mode network comprising inferior parietal gyrus, the angular gyrus, precuneus, and middle temporal gyrus. Other inhibition error‐related connectivity alterations in patients involved connections between thalamus and both SEF and parahippocampus, as well as the fronto‐parietal connections including the DLPFC as part of the middle frontal gyrus (MFG) (Buckner, 2013; Moussa et al., 2012). To understand the relation between altered functional connectivity within these brain systems and disturbances of inhibitory reflexive control, it is important to consider the specific oculomotor functions of the relevant brain regions.

6.1.

6.1.1. Connectivity alterations between regions of specific oculomotor function

The FEF represents the core cortical hub related to saccade generation. It has connections throughout the brain where cortical saccade planning and generation of oculomotor commands are coded (Vernet, Quentin, Chanes, Mitsumasu, & Valero‐Cabre, 2014). Functional FEF alterations have previously been suggested in psychotic disorders by eye movement laboratory studies (Keedy et al., 2014; Reilly, Harris, Khine, Keshavan, & Sweeney, 2008; Sweeney et al., 1998) and task‐based saccade fMRI studies (Fukumoto‐Motoshita et al., 2009; Keedy, Ebens, Keshavan, & Sweeney, 2006). Second, the SEF is known to be important in higher order saccade control such as in visuomotor sequence learning and prediction (Heide et al., 2001; Schlag‐Rey, Amador, Sanchez, & Schlag, 1997; Simo, Krisky, & Sweeney, 2005). Third, the thalamus is known to play important roles in cortical connectivity generally, and fronto‐striatal systems in particular, and to be involved in visual information processing (Shook, Schlag‐Rey, & Schlag, 1991; Sommer & Wurtz, 2006). Together, the FEF, the SEF and the thalamus represent core areas of the oculomotor cortico‐striato‐thalamo‐cortical loop that is integrated with wider networks including the PPC, precuneus, cuneus, and angular gyrus, all of which are important for the spatial inversion of the visual target to its mirror position required for antisaccade generation (Alexander, Crutcher, & DeLong, 1990; Domagalik et al., 2012; Herweg et al., 2014). The DLPFC has been frequently implicated in antisaccade execution although its specific role is still not fully understood (Johnston, Koval, Lomber, & Everling, 2014; Sweeney et al., 1996). It shows strong functional connectivity within the fronto‐parietal network in resting state studies (Koval, Hutchison, Lomber, & Everling, 2014) projecting directly and indirectly to SC to mediate top‐down resting and excitatory control over SC neurons (Johnston et al., 2014). Others have shown that components of the default mode network including the middle temporal gyrus and the inferior parietal cortex, as well as the parahippocampus, are also involved in active antisaccade generation (Domagalik et al., 2012; Talanow et al., 2016).

6.1.2. Alterations in FEF connectivity related to inhibition error rate

Our findings of weaker intrinsic connectivity strength from FEF to inferior parietal gyrus, precuneus, and middle temporal gyrus predicting higher rates of antisaccade errors in patients suggest that increased inhibition errors are at least partly driven by alterations in intrinsic brain systems supporting visuospatial attention and their relation to sensorimotor transformation and volitional behavioral control (Brown, Vilis, & Everling, 2008; Talanow et al., 2016). Notably, higher functional connectivity between FEF and angular gyrus predicted higher inhibition errors in patients, implying a higher tone of sensorimotor integration that might reduce the ability to suppress saccade responses to visual target presentation (Seghier, 2013). Angular gyrus dysfunction has been shown to result in laterality discrimination difficulties that may impact generation of an antisaccade to the mirror position (Hirnstein, Bayer, Ellison, & Hausmann, 2011).

6.1.3. Alterations in thalamus connectivity related to inhibition error rate

We also observed stronger connectivity between thalamus and SEF, and weaker connectivity between thalamus and DLPFC in MFG at rest that predicted higher error rates in patients, Figure 1. This finding reflects an imbalance in the higher order control of sensorimotor systems, with more input from premotor cortex and less from prefrontal cortex. It is in line with a very recent study using rs‐MRI showing a pattern of increased connectivity between sensorimotor cortex, that is, SEF, and thalamus on the one hand and decreased connectivity between thalamus and prefrontal areas being related to proactive response inhibition (Wertz et al., 2018). Heightened thalamic interaction with SEF might also serve to increase resting tone in saccade generation systems, which would have the effect of reducing the ability to inhibit responses to sensory input. In the language of stop signal models, this could bias the system to respond to input which would reduce the ability of top down control to inhibit prosaccade responses, which might already be reduced by lowered DLPFC connectivity. Of note, the higher connectivity between SEF and thalamus was the only connection that correlated with higher symptom expression on PANSS scores indicating a possible association with illness severity, which itself has previously been linked to antisaccade performance (Harris et al., 2006).

6.1.4. Intrinsic brain systems for processing speed of correct antisaccades

Processing speed in controls was influenced by connectivity from FEF and SEF to cognitively relevant frontal regions including DLPFC in MFG and ACC, two regions highly implicated in antisaccade control and cognitive control generally. Stronger connectivity being related to longer processing time could reflect task specific involvement of the DLPFC for contextual processing (Hussein, Johnston, Belbeck, Lomber, & Everling, 2014) and evaluation of error responses for performance optimization in ACC (Polli et al., 2005). On the other hand, faster processing speed was related to stronger connectivity from FEF and SEF to thalamus and the precuneus which mediate visuospatial attention and remapping required for antisaccade performance.

6.1.5. Limitations

First, combining patients with psychotic disorders as implemented in the B‐SNIP study was planned based on previous laboratory antisaccade studies and resting state studies across disorders to increase the power of analyses. In the present sample, there was no difference between groups regarding antisaccade error rates supporting the hypothesis that increased error rates represent a more general biomarker across psychotic disorders (Reilly et al., 2014). However, future studies with larger samples are needed to reveal specific differences of alterations in fronto‐thalamo‐parietal networks associated with antisaccade error rate between psychotic disorders. Second, we found a relative independence of intrinsic brain activity from current medications and symptom expression. This may in part be due to the fact that by design, our patients were relatively clinically stable and on stable pharmacological treatment. Third, to control for possible confounds by head motion participants with head translation >1.5 mm and rotation >1.5° during fMRI scanning were excluded and head motion parameters were additionally used as covariates. However, possible effects of this confound cannot be fully excluded.

The findings from this study support a linkage between a well‐established biomarker for psychotic disorders and alterations in intrinsic brain systems subserving this behavior. Our findings provide novel evidence that disturbances in these brain systems at rest contribute to impaired antisaccade task performance. Future studies are needed to unravel how alterations in resting state activity contribute to other cognitive control deficits in psychotic disorders, and whether there are informative genetic associations with these alterations.

CONFLICT OF INTERESTS

All authors have declared no conflicts of interest in relation to the subject of this study.

AUTHOR CONTRIBUTION

The NIMH had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or the decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Carol A. Tamminga reports the following financial disclosures: American Psychiatric Association–Deputy Editor; Astellas–Ad Hoc Consultant; Autifony–Ad Hoc Consultant; The Brain and Behavior Foundation–Council Member; Eli Lilly Pharmaceuticals–Ad Hoc Consultant; Intracellular Therapies (ITI, Inc.)–Advisory Board, drug development; Institute of Medicine–Council Member; National Academy of Medicine–Council Member; Pfizer–Ad Hoc Consultant; Sunovion–Investigator Initiated grant funding.

Supporting information

Supporting Information Table S1 Characteristics in Patient Subsamples

Click here for additional data file. (72.5KB, doc)

ACKNOWLEDGMENTS

The authors thank the patients and their family members who contributed their time and effort to participate in this study. We also thank Gunvant Thaker for his many scientific contributions to the B‐SNIP consortium. This work was supported by the National Institute of Mental Health (C.A.T., MH077851), (M.S.K., MH078113), (G.D.P. MH077945), (G.K.T, MH077852), and (J.A.S., MH077862). This work was further supported by the National Natural Science Foundation of China (S.L., 81371527) and National Program for Support of Top‐notch Young Professionals of China, and the Alexander von Humboldt Foundation, Germany (to R.L. and J.S.).

Lencer R, Yao L, Reilly JL, et al. Alterations in intrinsic fronto‐thalamo‐parietal connectivity are associated with cognitive control deficits in psychotic disorders. Hum Brain Mapp. 2019;40:163–174. 10.1002/hbm.24362

Funding information Alexander von Humboldt‐Stiftung; National Institute of Mental Health, Grant/Award Numbers: MH077851MH077852 MH077862MH077945MH078113, MH077862, MH077852, MH077945, MH078113, MH077851; Alexander von Humboldt Foundation; National Program for Support of Top‐notch Young Professionals of China; National Natural Science Foundation of China, Grant/Award Number: 81371527

Rebekka Lencer and Li Yao contributed equally to this work.

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Supporting Information Table S1 Characteristics in Patient Subsamples

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