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Unstable wakefulness during resting-state fMRI and its associations with network connectivity and affective psychopathology in young adults
Journal of Affective Disorders 258 (2019) 125–132
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Unstable wakefulness during resting-state fMRI and its associations with network connectivity and affective psychopathology in young adults
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Adriane M. Soehner , Henry W. Chase, Michele A. Bertocci, Tsafrir Greenberg, Ricki Stiffler, Jeannette C. Lockovich, Haris A. Aslam, Simona Graur, Genna Bebko, Mary L. Phillips University of Pittsburgh, Department of Psychiatry, Loeffler Building, Room 304, 121 Meyran Ave, Pittsburgh, PA 15213, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Resting-state fMRI Sleep Affective disorders
Background: Drifts between wakefulness and sleep are common during resting state functional MRI (rsfMRI). Among healthy adults, within-scanner sleep can impact functional connectivity of default mode (DMN), taskpositive (TPN), and thalamo-cortical networks. Because dysfunctional arousal states (i.e., sleepiness, sleep disturbance) are common in affective disorders, individuals with affective psychopathology may be more prone to unstable wakefulness during rsfMRI, hampering the estimation of clinically meaningful functional connectivity biomarkers. Methods: A transdiagnostic sample of 150 young adults (68 psychologically distressed; 82 psychiatrically healthy) completed rsfMRI and reported whether they experienced within-scanner sleep. Symptom scales were reduced into depression/anxiety and mania proneness dimensions using principal component analysis. We evaluated associations between within-scanner sleep, clinical status, and functional connectivity of the DMN, TPN, and thalamus. Results: Within-scanner sleep during rsfMRI was reported by 44% of participants (n = 66) but was unrelated to psychiatric diagnoses or mood symptom severity (p-values > 0.05). Across all participants, self-reported withinscanner sleep was associated with connectivity signatures akin to objectively-assessed sleep, including lower within-DMN connectivity, lower DMN-TPN anti-correlation, and altered thalamo-cortical connectivity (p < 0.05, corrected). Among participants reporting sustained wakefulness (n = 84), depression/anxiety severity positively associated with averaged DMN-TPN connectivity and mania proneness negatively associated with averaged thalamus-DMN connectivity (p-values < 0.05). Both relationships were attenuated and became non-significant when participants reporting within-scanner sleep were included (p-values > 0.05). Limitations: Subjective report of within-scanner sleep. Conclusions: Findings implicate within-scanner sleep as a source of variance in network connectivity; careful monitoring and correction for within-scanner sleep may enhance our ability to characterize network signatures underlying affective psychopathology.
1. Introduction Resting state fMRI (rsfMRI) has become an increasingly popular tool for characterizing network connectivity biomarkers of affective disorders. While rsfMRI has fostered significant advances in the psychiatric neuroimaging field, since its inception researchers have informally observed that within-scanner sleep is a common occurrence, presumably due to the lack of stimulation and task demands (Fox and Raichle, 2007). Recent fMRI studies employing simultaneous EEG or eye-tracking have objectively corroborated observations of unstable wakefulness during fMRI sessions (Poudel et al., 2014; Tagliazucchi
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et al., 2012; Wang et al., 2016; Wong et al., 2016, 2013). One widelycited investigation reported that nearly half of their healthy young adult sample fell asleep during an rsfMRI scan (Tagliazucchi and Laufs, 2014). These findings may be particularly pertinent to biomarker discovery in psychiatric neuroscience. For example, affective disorders have been linked to dysfunctional arousal states, including sleepiness and sleep disturbances (Harvey et al., 2011; Hegerl and Hensch, 2014), which may affect maintenance of wakefulness during rsfMRI protocols. Understanding the extent to which clinical samples are more prone to unstable wakefulness during rsfMRI and the impact this has on the detection of clinically meaningful brain-behavior relationships
Corresponding author. E-mail address: [email protected] (A.M. Soehner).
https://doi.org/10.1016/j.jad.2019.07.066 Received 13 March 2019; Received in revised form 19 July 2019; Accepted 29 July 2019 Available online 30 July 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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we explored the extent to which within-scanner sleep modulates relationships between connectivity in arousal-related networks and affective psychopathology. We hypothesized that including participants reporting within-scanner sleep would attenuate relationships between network connectivity and affective symptom severity (depression/anxiety, mania proneness).
(Deco and Kringelbach, 2014) has the potential to improve biomarker sensitivity and replicability. Inter-individual differences in arousal, including unintentional sleep, are an increasingly well-characterized source of variability in functional connectivity in waking-state functional neuroimaging studies of healthy adults (Picchioni et al., 2013; Poudel et al., 2018; Tagliazucchi and Laufs, 2014). Changes in connectivity of the default mode network (DMN), task positive network (TPN), and thalamus are often detected in rsfMRI studies of low arousal (Kaufmann et al., 2006; Larson-Prior et al., 2011; Yeo et al., 2015), eye-closures (Poudel et al., 2014, 2018; Wang et al., 2016), and light sleep (Larson-Prior et al., 2011; Picchioni et al., 2014; Samann et al., 2011; Spoormaker et al., 2010). In particular, within-DMN connectivity and DMN-TPN anticorrelation decrease in low arousal relative to alert states (Horovitz et al., 2009; Larson-Prior et al., 2011; Samann et al., 2011; Wang et al., 2016; Yeo et al., 2015), which may reflect a breakdown of internally and externally focused cognition. Concomitant reduction in thalamocortical connectivity, in turn, blocks sensory inputs from conscious awareness (Picchioni et al., 2013) and may contribute to reduced integration of large-scale cortical networks (Hwang et al., 2017; Spoormaker et al., 2010). These neural correlates of arousal have been uncovered predominantly through studies experimentally manipulating arousal state (i.e., sleep deprivation, caffeine) or utilizing objective arousal measures concurrent with fMRI (i.e., eye-tracking, EEG). Because self-report of within-scanner sleep is more common and simpler to obtain, it is important to understand whether network signatures of objectively low arousal can be replicated using self-report of withinscanner sleep. In addition to arousal-related changes in DMN, TPN, and thalamocortical functional connectivity, these networks have been implicated in the neural basis of affective disorders (e.g., Anticevic et al., 2014; Cui et al., 2016; Whitfield-Gabrieli and Ford, 2012). Although connectivity abnormalities among these networks vary by disorder, some researchers have proposed that unstable wakefulness during fMRI scanning may impact the measurement and interpretation of brain-behavior relationships in psychiatric samples (Tagliazucchi and van Someren, 2017). One possibility is that unstable wakefulness during rsfMRI is more common in psychiatric cohorts than controls due to documented deficits in arousal and sleep (Harvey et al., 2011; Hegerl and Hensch, 2014). This could indicate an overlapping neural basis of low arousal and psychopathology. Another possibility is that withinscanner wakefulness and psychiatric status are unrelated. If so, network signatures of unstable wakefulness could either induce or obscure links between functional connectivity and psychiatric symptomatology. As a step toward understanding the complex tripartite relationships among large-scale brain networks, arousal, and psychopathology, we investigated the extent to which self-reports of within-scanner sleep attenuated or strengthened relationships between network connectivity and affective psychopathology measures. Our goal was to evaluate rates of self-reported within-scanner sleep and its association with arousal-related network connectivity and affective psychopathology in a transdiagnostic sample of psychiatrically healthy and psychologically distressed young adults. Participants retrospectively reported the presence of unintentional sleep during the rsfMRI scan session. Our first aim was to ascertain clinical, demographic, and scan features that may predispose to unstable wakefulness during rsfMRI, and to assess these factors in relation to the presence or absence of within-scanner sleep. We predicted that rates of affective diagnoses and symptom severity would be greater among participants reporting within-scanner sleep. We next evaluated associations between self-reported within-scanner sleep and DMN, TPN, and thalamo-cortical functional connectivity. Consistent with work incorporating objective arousal markers during rsfMRI, we hypothesized that lower withinDMN connectivity, lower DMN-TPN anti-correlation, and lower thalamocortical connectivity would be observed among participants reporting within-scanner sleep relative to sustained wakefulness. Finally,
2. Methods 2.1. Participants Two groups of young adults (163 individuals, 18–25 years old) were recruited: 72 seeking help from mental health professionals at counseling or psychiatric services for psychological distress (including depressive and anxiety symptoms, and other behavioral and emotional problems such as failing to cope with everyday stressors and interpersonal relationships), irrespective of presence or absence of psychiatric diagnosis, and 91 healthy individuals not seeking help from such services, and with no previous personal or family history of psychiatric illness in first-degree relatives. Participants were recruited via community advertisement, student counseling services, and a participant registry. The University of Pittsburgh Human Research Protection Office approved the study; all participants provided written informed consent. Exclusion criteria for the study were: history of head injury, neurological, pervasive developmental disorder or systemic medical disease, cognitive impairment (Mini-Mental State Examination1 score < 24), and premorbid North American Adult Reading Test (NAART) IQ estimate < 85, visual disturbance (<20/40 Snellen visual acuity), left or mixed handedness (Annett, 1970), alcohol/substance use disorder and/or illicit substance use (except cannabis) over the last 3 months determined by the Structured Clinical Interview for DSM-5 (First et al., 2015); and visibly observed intoxication on the day of the neuroimaging assessment confirmed with urine drug and/or saliva alcohol tests on the scan day. Additional exclusion criteria at the MRI visit included positive pregnancy test/report for female participants and any current psychotropic medication use for greater than 2 weeks in distressed individuals. While current psychotropic medication use <2 weeks was allowed, the subset of participants included at the time of scan were medication-free at the time of the scan for at least 3 months (historic psychotropic medication use was not exclusionary). Participants were excluded for the following reasons: (a) excluded for excessive motion (>2 mm), signal loss, and/or severe artifacts in their imaging data (N = 12) and (b) incomplete clinical data (n = 1). The final sample included n = 150 (68 distressed and 82 healthy). 2.2. Psychopathology measures 2.2.1. DSM-5 disorders A trained clinician assessed psychiatric and sleep disorders with the Structured Clinical Interview for the DSM-5, Research Version (SCIDRV; First et al., 2015). 2.2.2. Affective psychopathology Participants were assessed on the following clinician-rated and selfreport measures of affective pathology: clinician-administered Hamilton Anxiety Rating Scale (HAM-A; Hamilton, 1959), Hamilton Rating Scale for Depression (HAM-D; Hamilton, 1960), and Young Mania Rating Scale (YMRS; Young et al., 1978); and self-rated Spielberger State-Trait Anxiety Inventory (STAI-T and STAI-S; Spielberger, 1983), Mood and Anxiety Symptom Questionnaire (MASQ; Clark and Watson, 1991); and the Mood Spectrum Measure (MOODSSR; Dell'Osso et al., 2002). Total scores were calculated for the HAM-A, HAM-D, YMRS, STAI-T, and STAI-S. MASQ subscales included the High Positive Affect, Anhedonia, Loss of Interest, Anxious Arousal, General Distress-Anxiety, General Distress-Depression, and General Distress126
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were engaging in a variety of mental activities (e.g., thinking about the fixation crosshair or events that happened that day, meditating, sleeping, counting, etc.). Because sleep perception can be variable in light sleep (Bonnet and Moore, 1982), the presence or lack of unintentional sleep was used to categorize participants rather than the minutes of time reported asleep. A total of 66 participants reported falling asleep during the rsfMRI scan (Sleepy) and 84 reported sustained wakefulness (Alert).
Mixed. MOODS-SR subscales included Depressed Mood, Manic Mood, Depressed Energy, Manic Energy, Depressed Cognition, Manic Cognition, and Rhythmicity. 2.2.3. Data reduction for psychopathology measures A principal component analysis (PCA) was applied to psychopathology measure total scores (HAM-A, HAM-D, YMRS, STAI-T, STAIS) and subscale scores for the MASQ and MOODS-SR psychopathology measures using standard techniques (see Supplemental Methods). Two factors were derived (see Table S1 for loadings). Factor 1 reflected Depression/Anxiety (HAM-A, HAM-D, STAI-T, STAI-S and the MASQ Anhedonia, Loss of Interest, General Depressive Distress subscales). Factor 2 reflected Mania Proneness (MOODS-SR Manic Mood, Manic Energy, and Manic Cognition).
2.4.2. Functional connectivity metrics Connectivity analyses focused on interactions among the Default Mode Network (DMN), Task-Positive Network (TPN), and thalamus. Cortical regions were selected from among 100 regions-of-interest (ROIs) extracted from a 7-network cortical parcellation estimated in 1000 young adults (Schaefer et al., 2017; Yeo et al., 2011). From this parcellation, a subset of DMN and TPN nodes specifically known to be affected by sleep/sleepiness (Larson-Prior et al., 2011; Samann et al., 2011) were utilized. Eight DMN regions included bilateral medial prefrontal cortex (mPFC), posterior cingulate (PCC) and retrosplenial cortices (RSP), and posterior inferior parietal lobules (IPLdmn) (LarsonPrior et al., 2011; Samann et al., 2011). Nine TPN regions included bilateral insula, lateral prefrontal cortices (lPFC) and anterior inferior parietal lobules (IPLtp), and mid-cingulate cortex (mCC) (Samann et al., 2011). DMN and TPN regions are shown in Fig. 1a. Bilateral thalamus ROIs were derived from the Automated Anatomical Labeling Atlas (Maldjian et al., 2003). The Data Processing Assistant for Resting-State fMRI (DPARSF) toolbox (Chao-Gan and Yu-Feng, 2010) extracted nuisance-regressed signal time series within each of the 19 ROIs, and constructed z-scored pair-wise correlation matrices amongst the ROIs. Main analyses used pair-wise z-scored correlations and averaged network connectivity measures. Averaged network connectivity measures were derived by averaging across pair-wise z-scored correlations reflecting Within-DMN, Within-TPN, DMN-TPN, Thalamus-DMN and Thalamus-TPN functional connectivity (Ong et al., 2015) .
2.3. Neuroimaging measures 2.3.1. Paradigm and data acquisition Participants completed a 6-min eyes-open resting-state fMRI paradigm with a fixation cross. Functional neuroimaging data were collected in n = 134 using a 3.0 Tesla Siemens Trio 2 MRI scanner with a 32-channel head coil and in n = 29 using a 3.0 Tesla Prisma Siemens MRI scanner with a 64-channel head coil (Siemens Medical Solutions, Erlangen, Germany) in the Magnetic Resonance Research Center (MRRC) at the University of Pittsburgh Medical Center. Structural 3D axial MPRAGE images (TR = 1500 ms, TE = 3.19 ms, Flip Angle 8°, FOV = 256 × 256 mm, 1 mm isotropic voxels, 176 continuous slices) and fieldmaps (TR = 500 ms, TE1 = 4.92 ms, TE2 = 7.38 ms, FOV = 220 × 220 mm, matrix = 96 × 96, Flip Angle = 45°, Bandwidth = 1302 Hz/Px) were acquired. For the resting-state scan, blood-oxygen-level-dependent (BOLD) images were acquired with a simultaneous multi-slice (SMS) gradient echo EPI sequence and an oblique axial angle (18 slices acquired with SMS factor = 3 for a total of 54 slices, TR = 1500 ms, TE = 30 ms, Field of View (FOV) = 220 × 220 mm, matrix = 96 × 96, slice thickness = 2.3 mm, Flip Angle = 55°, Bandwidth = 1860 Hz/Px).
2.4.3. Effects of within-scanner sleep on functional connectivity An analysis of covariance (ANCOVA) model examined the effect of within-scanner sleep (Sleepy vs. Alert) on pair-wise and averaged network functional connectivity among all participants (N = 150), adjusting for age, sex, IQ, cohort, and MRI scanner. For averaged network measures, a p < 0.05 threshold was for all ANCOVA analyses (Ong et al., 2015). For pair-wise connectivity among the 19 ROIs, the Network-Based Statistic (NBS) Matlab toolbox was used to correct for
2.3.2. Preprocessing Data were preprocessed using a combination of software packages (SPM, FSL, AFNI) implemented in Nipype (Gorgolewski et al., 2011). Preprocessing included realignment, coregistration, distortion correction, normalization, despiking, and smoothing (see Supplemental Methods for details). Physiological fluctuation and spurious variance were removed by including head motion (6 parameters), white matter signal, cerebrospinal fluid signal, and grey matter signal along with their temporal derivatives as nuisance covariates. Images were then band-pass filtered (0.009–0.9Hz). Average framewise displacement (FD) was calculated for each participant from their 6 head-motion parameters. Though we recognize that there is much debate regarding the use of global signal (GS) regression (i.e., including mean grey matter signal as a nuisance regressor) (Murphy and Fox, 2017), we used this approach for comparability with majority of other studies of arousal state and functional connectivity (Larson-Prior et al., 2011; Ong et al., 2015; Patanaik et al., 2018; Picchioni et al., 2014; Samann et al., 2011; Wang et al., 2016). Because low arousal state has been linked to increased GS (McAvoy et al., 2018; Wong et al., 2016, 2013), we conducted exploratory analyses pertaining to GS and present main functional connectivity analyses without GS regression in the Supplement.
Fig. 1. Averaged network connectivity patterns of the thalamus, default mode network, and task-positive network based on self-report of within-scanner sleep. (a) Map of task-positive (TPN; orange) and default mode network regions (DMN; blue) comprised of 17 ROIs derived from a resting-state network parcellation (Schaefer et al., 2017; Yeo et al., 2011); (b) Average functional connectivity strength within and between the DMN, TPN, and thalamus plotted by self-reported within-scanner sleep (Alert [N = 84] vs. Sleepy [N = 68]), adjusting for age, sex, IQ, cohort, and scanner type; *p < 0.05. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
2.4. Main analyses 2.4.1. Categorization of participants by within-scanner sleep Participants completed a questionnaire asking them to classify their thought and behavioral content during the resting-state scan immediately post-scan session (Andrews-Hanna et al., 2010). Participants quantified the proportion of time during rsfMRI scan in which they 127
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3.2. Effects of within-scanner sleep on functional connectivity
multiple comparisons (Zalesky et al., 2010). The NBS is a graph analog of cluster-level thresholding and controls for family-wise error rate. An NBS threshold of p < 0.05 was used for the group-level ANOVA model. A similar approach was previously used to detect effects of sleep deprivation on large-scale network connectivity (Yeo et al., 2015).
We next evaluated whether DMN, TPN, and thalamus functional connectivity patterns differed as a function of within-scanner sleep using averaged and pair-wise network connectivity metrics. Averaged network connectivity metrics plotted by within-scanner sleep (Sleepy vs. Alert) are displayed in Fig. 1b. In support of our hypothesis, the Sleepy group displayed lower within-DMN connectivity (F1,144 = 10.83, p = 0.001) and lower anti-correlation between the DMN and TPN (F1,144 = 14.17, p < 0.001). Within-TPN connectivity was lower in the Sleepy group, relative to the Alert group, though only at a trend level (F1,144 = 3.77, p = 0.054). Contrary to our hypothesis, thalamus connectivity with the DMN (F1,144 = 0.06, p = 0.815) and TPN (F1,144 = 0.63, p = 0.428) did not differ by within-scanner sleep. Pair-wise connectivity patterns were evaluated to explore more nuanced connectivity changes associated with within-scanner sleep. Pair-wise connectivity patterns among our 19 ROIs in our Alert and Sleepy groups both have high within-network correlations (blue) and low between-network and thalamocortical correlations (red-black). Pair-wise connectivity differences based on within-scanner sleep are depicted in Fig. 2b. Consistent with our hypothesis, relative to the Alert group, the Sleepy group displayed lower connectivity among DMN ROI pairs (17 of 28 ROI pairs; p < 0.05, corrected). In addition, several TPN ROI pairs demonstrated lower connectivity (15 of 37 ROI pairs; p < 0.05, corrected) in the Sleepy relative to the Alert group. The most prominent difference between the two groups was a reduced anti-correlation between DMN and TPN ROIs (46 of 54 ROI pairs, p < 0.05, corrected) in the Sleepy group relative to the Alert group, consistent with our hypothesis. In partial support of our hypothesis, among DMN ROIs, the Sleepy group had relatively lower thalamus-PCC connectivity. However, higher thalamus connectivity was also observed with mPFC, IPLdmn, bilateral lPFC, and left insula ROIs in the Sleepy versus Alert group (all p < 0.05, corrected). Finally, higher intra-thalamic connectivity was observed in the Sleepy group (p < 0.05, corrected).
2.4.4. Exploratory analysis of network connectivity, within-scanner sleep, and current psychopathology We conducted a series of regression of analyses to explore the hypothesis that within-scanner sleep may attenuate brain-behavior relationships. First, among participants reporting sustained wakefulness during their rsfMRI scan (Alert; N = 84), we examined associations between our five network connectivity measures and symptom severity factor scores (Depression/Anxiety, Mania Proneness) using regression, adjusting for age, sex, IQ, and MRI scanner. To evaluate whether including participants reporting within-scanner sleep attenuated associations between symptom severity and network connectivity, we conducted a sensitivity analysis in the whole sample (N = 150) using a parallel set of regression models. Given the exploratory nature of these analyses, significance was set at p < 0.05. 3. Result 3.1. Demographic and clinical differences based on within-scanner sleep Our sample of 150 participants was grouped into those who reported within-scanner sleep (Sleepy; N = 66) and those reporting sustained wakefulness (Alert; N = 84); 44.0% of participants reported falling asleep during the rsfMRI scan. The majority of demographic, clinical, and scan day variables did not differ based on within-scanner sleep group (Table 1). Average framewise displacement was greater among participants reporting sleep (p = 0.001). IQ differed at a level of p < 0.05 (Alert > Sleepy), though this did not survive multiple-comparison correction.
Table 1 Demographic and clinical differences among participants reporting within-scanner sleep (Sleepy) versus sustained wakefulness (Alert) during eyes-open resting-state fMRI.
Demographics Age (years) Sex (% Female) Education (years) IQ (NAART score) Cohort (% Distressed) DSM-5 Psychiatric Disorders Major Depression Anxiety Disorder Post-Traumatic Stress Disorder Behavior Disorder Eating Disorder Any DSM-5 Psychiatric Disorder Psychiatric Symptoms Anxiety/Depression Factor Mania Proneness Factor Sleep/Alertness DSM-5 Insomnia DSM-5 Hypersomnia Any DSM-5 Sleep Disorder Alertness (PANAS item score) Scan Features Scan Start Time (HH:MM) Scanner (% Trio) Average FD (mm)
All (N = 150) Mean (SD) or N (%)
Alert (N = 84) Mean (SD) or N (%)
Sleepy (N = 66) Mean (SD) or N (%)
Test Statistic
p-value
21.70(2.01) 101(67.3%) 5.34(1.09) 108.03(6.87) 68(45.3%)
21.78 (1.79) 59(70.2%) 5.35(1.02) 109.12(6.72) 38(45.2%)
21.59(2.27) 42(63.6%) 5.33(1.17) 106.66(6.85) 30(45.5%)
t148 = 0.56 X2 = 0.73 t148 = 0.07 t148 = 2.21 X2 = 0.001
0.579 0.392 0.947 0.029 0.979
23(15.3%) 32(21.3%) 6(4.0%) 10(6.7%) 1(0.7%) 43(28.7%)
12(14.3%) 19(22.6%) 3(3.6%) 6(7.1%) 0(0.0%) 23(27.4%)
11(16.7%) 13(19.7%) 3(4.5%) 4(6.1%) 1(1.5%) 20(30.3%)
X2 X2 X2 X2 X2 X2
0.688 0.665 0.763 0.792 0.258 0.694
0.00(1.0) 0.00(1.0)
−0.06(0.99) 0.08(1.08)
0.07 1.01) −0.10(0.88)
t148 = −0.80 t148 = 1.05
0.426 0.298
9(6.0%) 5(3.3%) 14(9.3%) 2.89(1.07)
5(6.0%) 3(3.6%) 8(9.5%) 3(1.0)
4(6.1%) 2(3.0%) 6(9.1%) 2.76(1.14%)
X2 = 0.001 X2 = 0.03 X2 = 0.01 t148 = 1.38
0.978 0.855 0.928 0.169
14:37 (1:47) 123(82.0%) 0.10(0.04)
14:32 (1:49) 69(82.1%) 0.09(0.03)
14:41(1:44) 54(81.8%) 0.11(0.04)
t148 = −0.48 X2 = 0.003 t148 = −3.47
0.635 0.959 0.001*
= = = = = =
0.16 0.19 0.09 0.07 1.28 0.15
Note: IQ = Intelligence Quotient; NAART = North American Adult Reading Test; DSM-5 = Diagnostic and Statistical Manual of Mental Disorders – 5th edition; PANAS = Positive and Negative Affect Scales; FD=frame-wise displacement. ⁎ Significant at Bonferroni-corrected p < 0.05 (p = 0.0025). 128
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Fig. 2. Functional connectivity patterns of the thalamus, default mode network, and task-positive network based on self-report of within-scanner sleep (Alert [N = 84] vs. Sleepy [N = 68]). (a) Z-transformed functional connectivity matrix of the 19 ROIs reflecting the DMN, TPN, and bilateral thalamus in participants who were Alert (yellow, upper triangular) or Sleepy (green; lower triangular). Hot colors reflect lower correlation and cool colors reflect positive correlation among ROI pairs; (b) Functional connectivity differences between Sleepy vs. Alert participants (Sleepy-Alert). ROI pairs significant at p < 0.05 NBS-corrected threshold are marked with a white asterisk (*). Hot colors reflect lower connectivity among Sleepy versus Alert participants, and cool colors reflect higher connectivity among Sleepy versus Alert participants. Analyses adjusted for age, sex, IQ, cohort, and scanner type. Abbreviations: DMN = Default Mode Network; TPN = Task-Positive Network; mPFC = medial prefrontal cortex, RSP = retrosplenial cortex, PCC = posterior cingulate cortex, IPL = inferior parietal lobule, lPFC = lateral prefrontal cortex, dmCC = dorsal mid-cingulate cortex, Thal = thalamus. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3.4. Exploratory global signal analyses
3.3. Effects of within-scanner sleep on network connectivity-mood symptom associations
Exploratory analyses of GS in relation to self-report within-scanner sleep showed patterns largely consistent observed in studies using objective arousal or sleep markers and sleep deprivation (see Supplement, Fig. S1). GS variability was greater among Sleepy versus Alert participants (p < 0.05). When fMRI analyses were performed without GS regression, within-DMN, within-TPN, and DMN-TPN connectivity differences based on within-scanner sleep were similar to the main analyses, though of smaller magnitude (Fig. S1b and c). A decrease in thalamo-cortical connectivity among Sleepy relative to Alert participants was the most prominent difference from the main analysis (Fig. S1b and c), consistent with other work (Yeo et al., 2015).
To explore the effects of within-scanner sleep on brain-behavior relationships, we evaluated associations between mood symptom severity (Depression/Anxiety; Mania Proneness) and averaged network connectivity when including (All Participants; N = 150) or excluding participants reporting within-scanner sleep (Alert Only; N = 84). There was a significant association between Depression/Anxiety and averaged DMN-TPN connectivity in participants reporting sustained wakefulness (BALERT = 0.27, p = 0.017; Fig. 3a). However, this effect was attenuated to a trend when participants reporting within-scanner sleep were included (BALL = 0.15, p = 0.071; Fig. 3b). Regardless of withinscanner sleep, depression/anxiety severity was not significantly associated with other connectivity patterns, including within-DMN (BALL = −0.01, p = 0.141; BALERT = −0.09, p = 0.467), within-TPN (BALL = −0.07, p = 0.424; BALERT = −0.13, p = 0.252), ThalamusDMN (BALL = −0.02, p = 0.780; BALERT = −0.03, p = 0.796), or Thalamus-TPN connectivity (BALL = −0.05, p = 0.582; BALERT = −0.10, p = 0.388). Mania Proneness was significantly associated with Thalamus-DMN connectivity in participants reporting sustain wakefulness (BALERT = −0.25, p = 0.030; Fig. 3c), but this effect became non-significant in the whole sample (BALL = −0.13, p = 0.115; Fig. 3d). Regardless of within-scanner sleep, Mania Proneness severity was not significantly associated with within-DMN (BALL = −0.06, p = 0.501; BALERT = −0.12, p = 0.320), within-TPN (BALL = −0.15, p = 0.068; BALERT = −0.19, p = 0.089), DMN-TPN (BALL = 0.16, p = 0.058; BALERT = 0.21, p = 0.066), or Thalamus-TPN connectivity (BALL = 0.15, p = 0.069; BALERT = −0.17, p = 0.146).
4. Discussion Our goal was to examine effects of self-reported within-scanner sleep during rsfMRI on arousal-related functional connectivity and their relationship with affective psychopathology in a transdiagnostic sample of young adults. Among participants who reported within-scanner sleep versus sustained wakefulness, differences in demographic, clinical features, network connectivity were examined. We also explored whether within-scanner sleep modulated associations between arousal-related connectivity patterns and affective psychopathology measures. Withinscanner sleep was not associated with affective diagnoses or symptom severities, however we observed connectivity signatures of self-reported within-scanner sleep that overlapped with those of objectively-assessed arousal. Exploratory analyses also suggest that within-scanner sleep may attenuate associations between DMN connectivity patterns and mood symptom severities. Consistent with fMRI studies using concurrent EEG to objectively 129
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Fig. 3. Associations between network connectivity and mood symptom severities including (All Participants; N = 150) and excluding participants reporting within-scanner sleep (Alert Only; N = 84). (a) Depression/Anxiety factor score plotted versus averaged DMN-TPN connectivity in Alert participants and (b) including participants reporting within-scanner sleep. (c) Mania proneness factor score plotted versus averaged Thalamus-DMN connectivity in Alert participants and (d) including participants reporting within-scanner sleep. All analyses adjusted for age, sex, IQ, and scanner type. Abbreviations: CTL = healthy control participants; DS = distressed participants; DMN=default mode network; TPN=Task-Positive Network.
attention/salience network regions (lateral PFC, insula) during low arousal states. Because we could not pinpoint the timing of withinscanner sleep in our study, thalamo-cortical connectivity patterns observed here may reflect a combination low arousal and compensatory neural signatures supporting resumption or maintenance of wakefulness, though more nuanced analyses are needed to support this speculation. Nonetheless, our results indicate some overlap in the neural signatures of self-report within-scanner sleep and those of objective sleep or low arousal states. The final aim examined whether including participants reporting within-scanner sleep would alter associations between arousal-related network connectivity measures and mood symptom severity measures. Among alert participants, our exploratory analyses showed a positive association between Depression/Anxiety Severity and DMN-TPN connectivity, along with a negative association between Mania Proneness and Thalamus-DMN connectivity. Both effects were attenuated in the whole sample, when participants reporting within-scanner sleep were included in the analysis. While these exploratory analyses were subtle and should be interpreted as preliminary given the lack of multiple comparison correction, they provide some of the first empirical support for long-held concerns that within-scanner sleep has the potential to subtly attenuate relationships between network connectivity and psychiatric symptoms. Findings also suggest that connectivity of the DMN may be particularly vulnerable to such effects. This is consistent with tight links observed between DMN function and both sleepiness and affective disorders (Tagliazucchi and van Someren, 2017; WhitfieldGabrieli and Ford, 2012). The exact mechanism through which withinscanner sleep may attenuate brain-behavior relationships here is unclear; while sleep has effects on functional connectivity, it may also
detect sleep (Tagliazucchi and Laufs, 2014), 44% of our participants reported falling asleep during their scan. Our hypothesis that self-report of within-scanner sleep would be more common among participants with affective (mood, anxiety) diagnoses or greater mood symptom severity was not supported. Consistent with behavioral studies of sleepiness (van den Berg, 2006), average head-motion was greater in participants reporting within-scanner sleep. However, post-hoc exploratory analyses showed that including average framewise displacement as a covariate did not alter results of the main analyses. Withinscanner sleep was unrelated to other demographic, clinical, or scan measures. Overall, our data do not indicate greater propensity for reports of within-scanner sleep among participants with affective or other psychiatric diagnoses. Self-reports of within-scanner sleep were associated with several of the connectivity patterns observed in studies of light sleep, sleep deprivation, and lower waking arousal (Tagliazucchi and van Someren, 2017). Consistent with our prediction, we detected lower within-DMN connectivity and lower DMN-TPN anticorrelation among participants reporting sleep versus those who did not. Predictions about lower thalamo-cortical connectivity were partially supported. While thalamus-PCC connectivity was lower among participants reporting sleep, higher thalamus connectivity was observed with other regions (medial PFC, lateral PFC, left insula). Lower thalamus-PCC connectivity has been hypothesized to underlie reduced or impaired consciousness (He et al., 2015), consistent with our pattern of findings. The observation of greater thalamus connectivity among participants reporting within-scanner sleep is more consistent with effects of fluctuating arousal during rsfMRI (Poudel et al., 2018; Wang et al., 2016); these studies also report greater thalamus connectivity with ventral 130
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contribute to noise in the BOLD signal via respiratory or cardiac fluctuations (Tagliazucchi and van Someren, 2017). Though our self-report measure of within-scanner sleep was not associated with psychiatric disorder or symptoms, effects of within-scanner sleep on brain-behavior relationships may be magnified in more severe or chronic psychiatric samples, among whom sleep deprivation, insomnia or hypersomnia, or sedating psychotropic medication use is more common (Soehner et al., 2013). To avoid sleep confounds, it has been recommended that rsfMRI be combined with an objective measure of wakefulness (Tagliazucchi and van Someren, 2017), such as EEG or eye-tracking, particularly in samples where vigilance may be compromised. Alternatively, some have opted to apply machine classifiers to detect 30second epochs of sleep using rsfMRI time series data (Haimovici et al., 2017) and include only periods of sustained wakefulness in analyses (Kaufmann et al., 2016). A combination of these approaches would be beneficial in future psychiatric neuroimaging studies to mitigate arousal-related confounds in rsfMRI. Several strengths and limitations of the present analysis bear consideration. Key strengths include a large, unmedicated transdiagnostic sample consisting of healthy and psychologically distressed young adults; use of both regional and network connectivity methods; and comprehensive assessment of psychiatric symptomatology. On the other hand, results should be considered preliminary and cautiously interpreted due to our reliance on a subjective sleep measure. Sleep misperception, or perception of sleep as a waking state, is more common among those diagnosed with psychiatric disorders, and thus our estimates of sleep during the scan may be imprecise (Rezaie et al., 2018). As noted above, future rsfMRI studies in psychiatric samples would benefit from incorporating objective markers of sleep or arousal. Our study also did not include assessment of recent sleep patterns (Poudel et al., 2014) and caffeine use (Wong et al., 2016), which have strong effects on scan-day sleep propensity. This may have also impacted our ability to detect relationships between within-scanner sleep and psychiatric status. Exploratory analyses revealed links between selfreported within-scanner sleep and variability in the global signal, which tends to track strongly with fluctuating vigilance (see Supplement) (Samann et al., 2011; Wong et al., 2013; Yeo et al., 2015). While we opted to employ global signal regression for comparability with recent studies (Larson-Prior et al., 2011; Ong et al., 2015; Patanaik et al., 2018; Picchioni et al., 2014; Samann et al., 2011; Wang et al., 2016), signal processing methods may affect the connectivity signatures associated with sleep and their links with psychopathology. Our analysis emphasized a low arousal component of unstable wakefulness, restlessness, state anxiety, or agitation can also contribute to interindividual differences resting state connectivity, for example in sensorimotor or limbic networks (Bauer, 2013; Soros et al., 2019). The present analysis is not intended as a comprehensive assay of psychopathology-related brain networks associated with arousal state, simply a subset of networks previously shown to be modulated by low withinscanner arousal. The present analyses indicate that self-reported within-scanner sleep may be a common phenomenon during rsfMRI, regardless of psychiatric status. Moreover, self-reported sleep is associated with several of the DMN and thalamo-cortical connectivity patterns observed in objectively-measured light sleep. Finally, our analyses support conventional wisdom that sleep during fMRI scanning may affect the accurate characterization of brain-behavior relationships; herein, withinscanner sleep attenuated associations between mood symptoms (depression/anxiety, mania) and DMN functional connectivity. Withinscanner sleep during rsfMRI has the potential to act as a source of interindividual variance in network connectivity measures pertinent to affective disorders. Careful monitoring and correction for within-scanner sleep could improve the accurate detection of network signatures underlying affective psychopathology.
Funding This study was supported by funding from the National Institute of Mental Health grants R01MH100041 (M.L.P.) and K01MH111953 (A.M.S.). The funding source had no role in the analysis or manuscript preparation. CRediT authorship contribution statement Adriane M. Soehner: Conceptualization, Data curation, Formal analysis, Writing - original draft. Henry W. Chase: Conceptualization, Data curation, Formal analysis, Project administration, Writing - review & editing. Michele A. Bertocci: Conceptualization, Data curation, Formal analysis, Writing - review & editing. Tsafrir Greenberg: Project administration, Writing - review & editing. Ricki Stiffler: Project administration, Writing - review & editing. Jeannette C. Lockovich: Project administration, Writing - review & editing. Haris A. Aslam: . Simona Graur: Project administration, Writing - review & editing. Genna Bebko: Project administration, Writing - review & editing. Mary L. Phillips: Formal analysis, Funding acquisition, Writing - review & editing, Project administration. Declaration of Competing Interest None. Acknowledgments We would like to Erin Rodgers, B.S., for her assistance with manuscript preparation. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.07.066. References Andrews-Hanna, J.R., Reidler, J.S., Huang, C., Buckner, R.L., 2010. Evidence for the default network's role in spontaneous cognition. J. Neurophysiol. 104, 322–335. Annett, M., 1970. A classification of hand preference by association analysis. Br. J. Psychol. 61, 303–321. Anticevic, A., Cole, M.W., Repovs, G., Murray, J.D., Brumbaugh, M.S., Winkler, A.M., Savic, A., Krystal, J.H., Pearlson, G.D., Glahn, D.C., 2014. Characterizing thalamocortical disturbances in schizophrenia and bipolar illness. Cereb. Cortex 24, 3116–3130. Bauer, A.M., 2013. Review: collaborative care improves depression and anxiety symptoms in adults. Evid. Based Ment. Health 16, 40. Bonnet, M.H., Moore, S.E., 1982. The threshold of sleep: perception of sleep as a function of time asleep and auditory threshold. Sleep 5, 267–276. Chao-Gan, Y., Yu-Feng, Z., 2010. DPARSF: a MATLAB toolbox for "Pipeline" data analysis of resting-state fMRI. Front. Syst. Neurosci. 4, 13. Clark, L.A., Watson, D., 1991. Tripartite model of anxiety and depression: psychometric evidence and taxonomic implications. J. Abnorm. Psychol. 100, 316–336. Cui, H., Zhang, J., Liu, Y., Li, Q., Li, H., Zhang, L., Hu, Q., Cheng, W., Luo, Q., Li, J., Li, W., Wang, J., Feng, J., Li, C., Northoff, G., 2016. Differential alterations of resting-state functional connectivity in generalized anxiety disorder and panic disorder. Hum. Brain Mapp. 37, 1459–1473. Deco, G., Kringelbach, M.L., 2014. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 84, 892–905. Dell'Osso, L., Armani, A., Rucci, P., Frank, E., Fagiolini, A., Corretti, G., Shear, M.K., Grochocinski, V.J., Maser, J.D., Endicott, J., Cassano, G.B., 2002. Measuring mood spectrum: comparison of interview (SCI-MOODS) and self-report (MOODS-SR) instruments. Compr. Psychiatry 43, 69–73. First, M.B., Williams, J.B.W., Karg, R.S., R.L., S., 2015. Structured Clinical Interview For DSM-5—Research Version (SCID-5 For DSM-5, Research Version; SCID-5-RV). American Psychiatric Association, Arlington, VA. Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711. Gorgolewski, K., Burns, C.D., Madison, C., Clark, D., Halchenko, Y.O., Waskom, M.L., Ghosh, S.S., 2011. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform. 5, 13. Haimovici, A., Tagliazucchi, E., Balenzuela, P., Laufs, H., 2017. On wakefulness fluctuations as a source of BOLD functional connectivity dynamics. Sci. Rep. 7, 5908.
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Unstable wakefulness during resting-state fMRI and its associations with network connectivity and affective psychopathology in young adults
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Adriane M. Soehner , Henry W. Chase, Michele A. Bertocci, Tsafrir Greenberg, Ricki Stiffler, Jeannette C. Lockovich, Haris A. Aslam, Simona Graur, Genna Bebko, Mary L. Phillips University of Pittsburgh, Department of Psychiatry, Loeffler Building, Room 304, 121 Meyran Ave, Pittsburgh, PA 15213, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Resting-state fMRI Sleep Affective disorders
Background: Drifts between wakefulness and sleep are common during resting state functional MRI (rsfMRI). Among healthy adults, within-scanner sleep can impact functional connectivity of default mode (DMN), taskpositive (TPN), and thalamo-cortical networks. Because dysfunctional arousal states (i.e., sleepiness, sleep disturbance) are common in affective disorders, individuals with affective psychopathology may be more prone to unstable wakefulness during rsfMRI, hampering the estimation of clinically meaningful functional connectivity biomarkers. Methods: A transdiagnostic sample of 150 young adults (68 psychologically distressed; 82 psychiatrically healthy) completed rsfMRI and reported whether they experienced within-scanner sleep. Symptom scales were reduced into depression/anxiety and mania proneness dimensions using principal component analysis. We evaluated associations between within-scanner sleep, clinical status, and functional connectivity of the DMN, TPN, and thalamus. Results: Within-scanner sleep during rsfMRI was reported by 44% of participants (n = 66) but was unrelated to psychiatric diagnoses or mood symptom severity (p-values > 0.05). Across all participants, self-reported withinscanner sleep was associated with connectivity signatures akin to objectively-assessed sleep, including lower within-DMN connectivity, lower DMN-TPN anti-correlation, and altered thalamo-cortical connectivity (p < 0.05, corrected). Among participants reporting sustained wakefulness (n = 84), depression/anxiety severity positively associated with averaged DMN-TPN connectivity and mania proneness negatively associated with averaged thalamus-DMN connectivity (p-values < 0.05). Both relationships were attenuated and became non-significant when participants reporting within-scanner sleep were included (p-values > 0.05). Limitations: Subjective report of within-scanner sleep. Conclusions: Findings implicate within-scanner sleep as a source of variance in network connectivity; careful monitoring and correction for within-scanner sleep may enhance our ability to characterize network signatures underlying affective psychopathology.
1. Introduction Resting state fMRI (rsfMRI) has become an increasingly popular tool for characterizing network connectivity biomarkers of affective disorders. While rsfMRI has fostered significant advances in the psychiatric neuroimaging field, since its inception researchers have informally observed that within-scanner sleep is a common occurrence, presumably due to the lack of stimulation and task demands (Fox and Raichle, 2007). Recent fMRI studies employing simultaneous EEG or eye-tracking have objectively corroborated observations of unstable wakefulness during fMRI sessions (Poudel et al., 2014; Tagliazucchi
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et al., 2012; Wang et al., 2016; Wong et al., 2016, 2013). One widelycited investigation reported that nearly half of their healthy young adult sample fell asleep during an rsfMRI scan (Tagliazucchi and Laufs, 2014). These findings may be particularly pertinent to biomarker discovery in psychiatric neuroscience. For example, affective disorders have been linked to dysfunctional arousal states, including sleepiness and sleep disturbances (Harvey et al., 2011; Hegerl and Hensch, 2014), which may affect maintenance of wakefulness during rsfMRI protocols. Understanding the extent to which clinical samples are more prone to unstable wakefulness during rsfMRI and the impact this has on the detection of clinically meaningful brain-behavior relationships
Corresponding author. E-mail address: [email protected] (A.M. Soehner).
https://doi.org/10.1016/j.jad.2019.07.066 Received 13 March 2019; Received in revised form 19 July 2019; Accepted 29 July 2019 Available online 30 July 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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we explored the extent to which within-scanner sleep modulates relationships between connectivity in arousal-related networks and affective psychopathology. We hypothesized that including participants reporting within-scanner sleep would attenuate relationships between network connectivity and affective symptom severity (depression/anxiety, mania proneness).
(Deco and Kringelbach, 2014) has the potential to improve biomarker sensitivity and replicability. Inter-individual differences in arousal, including unintentional sleep, are an increasingly well-characterized source of variability in functional connectivity in waking-state functional neuroimaging studies of healthy adults (Picchioni et al., 2013; Poudel et al., 2018; Tagliazucchi and Laufs, 2014). Changes in connectivity of the default mode network (DMN), task positive network (TPN), and thalamus are often detected in rsfMRI studies of low arousal (Kaufmann et al., 2006; Larson-Prior et al., 2011; Yeo et al., 2015), eye-closures (Poudel et al., 2014, 2018; Wang et al., 2016), and light sleep (Larson-Prior et al., 2011; Picchioni et al., 2014; Samann et al., 2011; Spoormaker et al., 2010). In particular, within-DMN connectivity and DMN-TPN anticorrelation decrease in low arousal relative to alert states (Horovitz et al., 2009; Larson-Prior et al., 2011; Samann et al., 2011; Wang et al., 2016; Yeo et al., 2015), which may reflect a breakdown of internally and externally focused cognition. Concomitant reduction in thalamocortical connectivity, in turn, blocks sensory inputs from conscious awareness (Picchioni et al., 2013) and may contribute to reduced integration of large-scale cortical networks (Hwang et al., 2017; Spoormaker et al., 2010). These neural correlates of arousal have been uncovered predominantly through studies experimentally manipulating arousal state (i.e., sleep deprivation, caffeine) or utilizing objective arousal measures concurrent with fMRI (i.e., eye-tracking, EEG). Because self-report of within-scanner sleep is more common and simpler to obtain, it is important to understand whether network signatures of objectively low arousal can be replicated using self-report of withinscanner sleep. In addition to arousal-related changes in DMN, TPN, and thalamocortical functional connectivity, these networks have been implicated in the neural basis of affective disorders (e.g., Anticevic et al., 2014; Cui et al., 2016; Whitfield-Gabrieli and Ford, 2012). Although connectivity abnormalities among these networks vary by disorder, some researchers have proposed that unstable wakefulness during fMRI scanning may impact the measurement and interpretation of brain-behavior relationships in psychiatric samples (Tagliazucchi and van Someren, 2017). One possibility is that unstable wakefulness during rsfMRI is more common in psychiatric cohorts than controls due to documented deficits in arousal and sleep (Harvey et al., 2011; Hegerl and Hensch, 2014). This could indicate an overlapping neural basis of low arousal and psychopathology. Another possibility is that withinscanner wakefulness and psychiatric status are unrelated. If so, network signatures of unstable wakefulness could either induce or obscure links between functional connectivity and psychiatric symptomatology. As a step toward understanding the complex tripartite relationships among large-scale brain networks, arousal, and psychopathology, we investigated the extent to which self-reports of within-scanner sleep attenuated or strengthened relationships between network connectivity and affective psychopathology measures. Our goal was to evaluate rates of self-reported within-scanner sleep and its association with arousal-related network connectivity and affective psychopathology in a transdiagnostic sample of psychiatrically healthy and psychologically distressed young adults. Participants retrospectively reported the presence of unintentional sleep during the rsfMRI scan session. Our first aim was to ascertain clinical, demographic, and scan features that may predispose to unstable wakefulness during rsfMRI, and to assess these factors in relation to the presence or absence of within-scanner sleep. We predicted that rates of affective diagnoses and symptom severity would be greater among participants reporting within-scanner sleep. We next evaluated associations between self-reported within-scanner sleep and DMN, TPN, and thalamo-cortical functional connectivity. Consistent with work incorporating objective arousal markers during rsfMRI, we hypothesized that lower withinDMN connectivity, lower DMN-TPN anti-correlation, and lower thalamocortical connectivity would be observed among participants reporting within-scanner sleep relative to sustained wakefulness. Finally,
2. Methods 2.1. Participants Two groups of young adults (163 individuals, 18–25 years old) were recruited: 72 seeking help from mental health professionals at counseling or psychiatric services for psychological distress (including depressive and anxiety symptoms, and other behavioral and emotional problems such as failing to cope with everyday stressors and interpersonal relationships), irrespective of presence or absence of psychiatric diagnosis, and 91 healthy individuals not seeking help from such services, and with no previous personal or family history of psychiatric illness in first-degree relatives. Participants were recruited via community advertisement, student counseling services, and a participant registry. The University of Pittsburgh Human Research Protection Office approved the study; all participants provided written informed consent. Exclusion criteria for the study were: history of head injury, neurological, pervasive developmental disorder or systemic medical disease, cognitive impairment (Mini-Mental State Examination1 score < 24), and premorbid North American Adult Reading Test (NAART) IQ estimate < 85, visual disturbance (<20/40 Snellen visual acuity), left or mixed handedness (Annett, 1970), alcohol/substance use disorder and/or illicit substance use (except cannabis) over the last 3 months determined by the Structured Clinical Interview for DSM-5 (First et al., 2015); and visibly observed intoxication on the day of the neuroimaging assessment confirmed with urine drug and/or saliva alcohol tests on the scan day. Additional exclusion criteria at the MRI visit included positive pregnancy test/report for female participants and any current psychotropic medication use for greater than 2 weeks in distressed individuals. While current psychotropic medication use <2 weeks was allowed, the subset of participants included at the time of scan were medication-free at the time of the scan for at least 3 months (historic psychotropic medication use was not exclusionary). Participants were excluded for the following reasons: (a) excluded for excessive motion (>2 mm), signal loss, and/or severe artifacts in their imaging data (N = 12) and (b) incomplete clinical data (n = 1). The final sample included n = 150 (68 distressed and 82 healthy). 2.2. Psychopathology measures 2.2.1. DSM-5 disorders A trained clinician assessed psychiatric and sleep disorders with the Structured Clinical Interview for the DSM-5, Research Version (SCIDRV; First et al., 2015). 2.2.2. Affective psychopathology Participants were assessed on the following clinician-rated and selfreport measures of affective pathology: clinician-administered Hamilton Anxiety Rating Scale (HAM-A; Hamilton, 1959), Hamilton Rating Scale for Depression (HAM-D; Hamilton, 1960), and Young Mania Rating Scale (YMRS; Young et al., 1978); and self-rated Spielberger State-Trait Anxiety Inventory (STAI-T and STAI-S; Spielberger, 1983), Mood and Anxiety Symptom Questionnaire (MASQ; Clark and Watson, 1991); and the Mood Spectrum Measure (MOODSSR; Dell'Osso et al., 2002). Total scores were calculated for the HAM-A, HAM-D, YMRS, STAI-T, and STAI-S. MASQ subscales included the High Positive Affect, Anhedonia, Loss of Interest, Anxious Arousal, General Distress-Anxiety, General Distress-Depression, and General Distress126
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were engaging in a variety of mental activities (e.g., thinking about the fixation crosshair or events that happened that day, meditating, sleeping, counting, etc.). Because sleep perception can be variable in light sleep (Bonnet and Moore, 1982), the presence or lack of unintentional sleep was used to categorize participants rather than the minutes of time reported asleep. A total of 66 participants reported falling asleep during the rsfMRI scan (Sleepy) and 84 reported sustained wakefulness (Alert).
Mixed. MOODS-SR subscales included Depressed Mood, Manic Mood, Depressed Energy, Manic Energy, Depressed Cognition, Manic Cognition, and Rhythmicity. 2.2.3. Data reduction for psychopathology measures A principal component analysis (PCA) was applied to psychopathology measure total scores (HAM-A, HAM-D, YMRS, STAI-T, STAIS) and subscale scores for the MASQ and MOODS-SR psychopathology measures using standard techniques (see Supplemental Methods). Two factors were derived (see Table S1 for loadings). Factor 1 reflected Depression/Anxiety (HAM-A, HAM-D, STAI-T, STAI-S and the MASQ Anhedonia, Loss of Interest, General Depressive Distress subscales). Factor 2 reflected Mania Proneness (MOODS-SR Manic Mood, Manic Energy, and Manic Cognition).
2.4.2. Functional connectivity metrics Connectivity analyses focused on interactions among the Default Mode Network (DMN), Task-Positive Network (TPN), and thalamus. Cortical regions were selected from among 100 regions-of-interest (ROIs) extracted from a 7-network cortical parcellation estimated in 1000 young adults (Schaefer et al., 2017; Yeo et al., 2011). From this parcellation, a subset of DMN and TPN nodes specifically known to be affected by sleep/sleepiness (Larson-Prior et al., 2011; Samann et al., 2011) were utilized. Eight DMN regions included bilateral medial prefrontal cortex (mPFC), posterior cingulate (PCC) and retrosplenial cortices (RSP), and posterior inferior parietal lobules (IPLdmn) (LarsonPrior et al., 2011; Samann et al., 2011). Nine TPN regions included bilateral insula, lateral prefrontal cortices (lPFC) and anterior inferior parietal lobules (IPLtp), and mid-cingulate cortex (mCC) (Samann et al., 2011). DMN and TPN regions are shown in Fig. 1a. Bilateral thalamus ROIs were derived from the Automated Anatomical Labeling Atlas (Maldjian et al., 2003). The Data Processing Assistant for Resting-State fMRI (DPARSF) toolbox (Chao-Gan and Yu-Feng, 2010) extracted nuisance-regressed signal time series within each of the 19 ROIs, and constructed z-scored pair-wise correlation matrices amongst the ROIs. Main analyses used pair-wise z-scored correlations and averaged network connectivity measures. Averaged network connectivity measures were derived by averaging across pair-wise z-scored correlations reflecting Within-DMN, Within-TPN, DMN-TPN, Thalamus-DMN and Thalamus-TPN functional connectivity (Ong et al., 2015) .
2.3. Neuroimaging measures 2.3.1. Paradigm and data acquisition Participants completed a 6-min eyes-open resting-state fMRI paradigm with a fixation cross. Functional neuroimaging data were collected in n = 134 using a 3.0 Tesla Siemens Trio 2 MRI scanner with a 32-channel head coil and in n = 29 using a 3.0 Tesla Prisma Siemens MRI scanner with a 64-channel head coil (Siemens Medical Solutions, Erlangen, Germany) in the Magnetic Resonance Research Center (MRRC) at the University of Pittsburgh Medical Center. Structural 3D axial MPRAGE images (TR = 1500 ms, TE = 3.19 ms, Flip Angle 8°, FOV = 256 × 256 mm, 1 mm isotropic voxels, 176 continuous slices) and fieldmaps (TR = 500 ms, TE1 = 4.92 ms, TE2 = 7.38 ms, FOV = 220 × 220 mm, matrix = 96 × 96, Flip Angle = 45°, Bandwidth = 1302 Hz/Px) were acquired. For the resting-state scan, blood-oxygen-level-dependent (BOLD) images were acquired with a simultaneous multi-slice (SMS) gradient echo EPI sequence and an oblique axial angle (18 slices acquired with SMS factor = 3 for a total of 54 slices, TR = 1500 ms, TE = 30 ms, Field of View (FOV) = 220 × 220 mm, matrix = 96 × 96, slice thickness = 2.3 mm, Flip Angle = 55°, Bandwidth = 1860 Hz/Px).
2.4.3. Effects of within-scanner sleep on functional connectivity An analysis of covariance (ANCOVA) model examined the effect of within-scanner sleep (Sleepy vs. Alert) on pair-wise and averaged network functional connectivity among all participants (N = 150), adjusting for age, sex, IQ, cohort, and MRI scanner. For averaged network measures, a p < 0.05 threshold was for all ANCOVA analyses (Ong et al., 2015). For pair-wise connectivity among the 19 ROIs, the Network-Based Statistic (NBS) Matlab toolbox was used to correct for
2.3.2. Preprocessing Data were preprocessed using a combination of software packages (SPM, FSL, AFNI) implemented in Nipype (Gorgolewski et al., 2011). Preprocessing included realignment, coregistration, distortion correction, normalization, despiking, and smoothing (see Supplemental Methods for details). Physiological fluctuation and spurious variance were removed by including head motion (6 parameters), white matter signal, cerebrospinal fluid signal, and grey matter signal along with their temporal derivatives as nuisance covariates. Images were then band-pass filtered (0.009–0.9Hz). Average framewise displacement (FD) was calculated for each participant from their 6 head-motion parameters. Though we recognize that there is much debate regarding the use of global signal (GS) regression (i.e., including mean grey matter signal as a nuisance regressor) (Murphy and Fox, 2017), we used this approach for comparability with majority of other studies of arousal state and functional connectivity (Larson-Prior et al., 2011; Ong et al., 2015; Patanaik et al., 2018; Picchioni et al., 2014; Samann et al., 2011; Wang et al., 2016). Because low arousal state has been linked to increased GS (McAvoy et al., 2018; Wong et al., 2016, 2013), we conducted exploratory analyses pertaining to GS and present main functional connectivity analyses without GS regression in the Supplement.
Fig. 1. Averaged network connectivity patterns of the thalamus, default mode network, and task-positive network based on self-report of within-scanner sleep. (a) Map of task-positive (TPN; orange) and default mode network regions (DMN; blue) comprised of 17 ROIs derived from a resting-state network parcellation (Schaefer et al., 2017; Yeo et al., 2011); (b) Average functional connectivity strength within and between the DMN, TPN, and thalamus plotted by self-reported within-scanner sleep (Alert [N = 84] vs. Sleepy [N = 68]), adjusting for age, sex, IQ, cohort, and scanner type; *p < 0.05. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
2.4. Main analyses 2.4.1. Categorization of participants by within-scanner sleep Participants completed a questionnaire asking them to classify their thought and behavioral content during the resting-state scan immediately post-scan session (Andrews-Hanna et al., 2010). Participants quantified the proportion of time during rsfMRI scan in which they 127
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3.2. Effects of within-scanner sleep on functional connectivity
multiple comparisons (Zalesky et al., 2010). The NBS is a graph analog of cluster-level thresholding and controls for family-wise error rate. An NBS threshold of p < 0.05 was used for the group-level ANOVA model. A similar approach was previously used to detect effects of sleep deprivation on large-scale network connectivity (Yeo et al., 2015).
We next evaluated whether DMN, TPN, and thalamus functional connectivity patterns differed as a function of within-scanner sleep using averaged and pair-wise network connectivity metrics. Averaged network connectivity metrics plotted by within-scanner sleep (Sleepy vs. Alert) are displayed in Fig. 1b. In support of our hypothesis, the Sleepy group displayed lower within-DMN connectivity (F1,144 = 10.83, p = 0.001) and lower anti-correlation between the DMN and TPN (F1,144 = 14.17, p < 0.001). Within-TPN connectivity was lower in the Sleepy group, relative to the Alert group, though only at a trend level (F1,144 = 3.77, p = 0.054). Contrary to our hypothesis, thalamus connectivity with the DMN (F1,144 = 0.06, p = 0.815) and TPN (F1,144 = 0.63, p = 0.428) did not differ by within-scanner sleep. Pair-wise connectivity patterns were evaluated to explore more nuanced connectivity changes associated with within-scanner sleep. Pair-wise connectivity patterns among our 19 ROIs in our Alert and Sleepy groups both have high within-network correlations (blue) and low between-network and thalamocortical correlations (red-black). Pair-wise connectivity differences based on within-scanner sleep are depicted in Fig. 2b. Consistent with our hypothesis, relative to the Alert group, the Sleepy group displayed lower connectivity among DMN ROI pairs (17 of 28 ROI pairs; p < 0.05, corrected). In addition, several TPN ROI pairs demonstrated lower connectivity (15 of 37 ROI pairs; p < 0.05, corrected) in the Sleepy relative to the Alert group. The most prominent difference between the two groups was a reduced anti-correlation between DMN and TPN ROIs (46 of 54 ROI pairs, p < 0.05, corrected) in the Sleepy group relative to the Alert group, consistent with our hypothesis. In partial support of our hypothesis, among DMN ROIs, the Sleepy group had relatively lower thalamus-PCC connectivity. However, higher thalamus connectivity was also observed with mPFC, IPLdmn, bilateral lPFC, and left insula ROIs in the Sleepy versus Alert group (all p < 0.05, corrected). Finally, higher intra-thalamic connectivity was observed in the Sleepy group (p < 0.05, corrected).
2.4.4. Exploratory analysis of network connectivity, within-scanner sleep, and current psychopathology We conducted a series of regression of analyses to explore the hypothesis that within-scanner sleep may attenuate brain-behavior relationships. First, among participants reporting sustained wakefulness during their rsfMRI scan (Alert; N = 84), we examined associations between our five network connectivity measures and symptom severity factor scores (Depression/Anxiety, Mania Proneness) using regression, adjusting for age, sex, IQ, and MRI scanner. To evaluate whether including participants reporting within-scanner sleep attenuated associations between symptom severity and network connectivity, we conducted a sensitivity analysis in the whole sample (N = 150) using a parallel set of regression models. Given the exploratory nature of these analyses, significance was set at p < 0.05. 3. Result 3.1. Demographic and clinical differences based on within-scanner sleep Our sample of 150 participants was grouped into those who reported within-scanner sleep (Sleepy; N = 66) and those reporting sustained wakefulness (Alert; N = 84); 44.0% of participants reported falling asleep during the rsfMRI scan. The majority of demographic, clinical, and scan day variables did not differ based on within-scanner sleep group (Table 1). Average framewise displacement was greater among participants reporting sleep (p = 0.001). IQ differed at a level of p < 0.05 (Alert > Sleepy), though this did not survive multiple-comparison correction.
Table 1 Demographic and clinical differences among participants reporting within-scanner sleep (Sleepy) versus sustained wakefulness (Alert) during eyes-open resting-state fMRI.
Demographics Age (years) Sex (% Female) Education (years) IQ (NAART score) Cohort (% Distressed) DSM-5 Psychiatric Disorders Major Depression Anxiety Disorder Post-Traumatic Stress Disorder Behavior Disorder Eating Disorder Any DSM-5 Psychiatric Disorder Psychiatric Symptoms Anxiety/Depression Factor Mania Proneness Factor Sleep/Alertness DSM-5 Insomnia DSM-5 Hypersomnia Any DSM-5 Sleep Disorder Alertness (PANAS item score) Scan Features Scan Start Time (HH:MM) Scanner (% Trio) Average FD (mm)
All (N = 150) Mean (SD) or N (%)
Alert (N = 84) Mean (SD) or N (%)
Sleepy (N = 66) Mean (SD) or N (%)
Test Statistic
p-value
21.70(2.01) 101(67.3%) 5.34(1.09) 108.03(6.87) 68(45.3%)
21.78 (1.79) 59(70.2%) 5.35(1.02) 109.12(6.72) 38(45.2%)
21.59(2.27) 42(63.6%) 5.33(1.17) 106.66(6.85) 30(45.5%)
t148 = 0.56 X2 = 0.73 t148 = 0.07 t148 = 2.21 X2 = 0.001
0.579 0.392 0.947 0.029 0.979
23(15.3%) 32(21.3%) 6(4.0%) 10(6.7%) 1(0.7%) 43(28.7%)
12(14.3%) 19(22.6%) 3(3.6%) 6(7.1%) 0(0.0%) 23(27.4%)
11(16.7%) 13(19.7%) 3(4.5%) 4(6.1%) 1(1.5%) 20(30.3%)
X2 X2 X2 X2 X2 X2
0.688 0.665 0.763 0.792 0.258 0.694
0.00(1.0) 0.00(1.0)
−0.06(0.99) 0.08(1.08)
0.07 1.01) −0.10(0.88)
t148 = −0.80 t148 = 1.05
0.426 0.298
9(6.0%) 5(3.3%) 14(9.3%) 2.89(1.07)
5(6.0%) 3(3.6%) 8(9.5%) 3(1.0)
4(6.1%) 2(3.0%) 6(9.1%) 2.76(1.14%)
X2 = 0.001 X2 = 0.03 X2 = 0.01 t148 = 1.38
0.978 0.855 0.928 0.169
14:37 (1:47) 123(82.0%) 0.10(0.04)
14:32 (1:49) 69(82.1%) 0.09(0.03)
14:41(1:44) 54(81.8%) 0.11(0.04)
t148 = −0.48 X2 = 0.003 t148 = −3.47
0.635 0.959 0.001*
= = = = = =
0.16 0.19 0.09 0.07 1.28 0.15
Note: IQ = Intelligence Quotient; NAART = North American Adult Reading Test; DSM-5 = Diagnostic and Statistical Manual of Mental Disorders – 5th edition; PANAS = Positive and Negative Affect Scales; FD=frame-wise displacement. ⁎ Significant at Bonferroni-corrected p < 0.05 (p = 0.0025). 128
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Fig. 2. Functional connectivity patterns of the thalamus, default mode network, and task-positive network based on self-report of within-scanner sleep (Alert [N = 84] vs. Sleepy [N = 68]). (a) Z-transformed functional connectivity matrix of the 19 ROIs reflecting the DMN, TPN, and bilateral thalamus in participants who were Alert (yellow, upper triangular) or Sleepy (green; lower triangular). Hot colors reflect lower correlation and cool colors reflect positive correlation among ROI pairs; (b) Functional connectivity differences between Sleepy vs. Alert participants (Sleepy-Alert). ROI pairs significant at p < 0.05 NBS-corrected threshold are marked with a white asterisk (*). Hot colors reflect lower connectivity among Sleepy versus Alert participants, and cool colors reflect higher connectivity among Sleepy versus Alert participants. Analyses adjusted for age, sex, IQ, cohort, and scanner type. Abbreviations: DMN = Default Mode Network; TPN = Task-Positive Network; mPFC = medial prefrontal cortex, RSP = retrosplenial cortex, PCC = posterior cingulate cortex, IPL = inferior parietal lobule, lPFC = lateral prefrontal cortex, dmCC = dorsal mid-cingulate cortex, Thal = thalamus. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3.4. Exploratory global signal analyses
3.3. Effects of within-scanner sleep on network connectivity-mood symptom associations
Exploratory analyses of GS in relation to self-report within-scanner sleep showed patterns largely consistent observed in studies using objective arousal or sleep markers and sleep deprivation (see Supplement, Fig. S1). GS variability was greater among Sleepy versus Alert participants (p < 0.05). When fMRI analyses were performed without GS regression, within-DMN, within-TPN, and DMN-TPN connectivity differences based on within-scanner sleep were similar to the main analyses, though of smaller magnitude (Fig. S1b and c). A decrease in thalamo-cortical connectivity among Sleepy relative to Alert participants was the most prominent difference from the main analysis (Fig. S1b and c), consistent with other work (Yeo et al., 2015).
To explore the effects of within-scanner sleep on brain-behavior relationships, we evaluated associations between mood symptom severity (Depression/Anxiety; Mania Proneness) and averaged network connectivity when including (All Participants; N = 150) or excluding participants reporting within-scanner sleep (Alert Only; N = 84). There was a significant association between Depression/Anxiety and averaged DMN-TPN connectivity in participants reporting sustained wakefulness (BALERT = 0.27, p = 0.017; Fig. 3a). However, this effect was attenuated to a trend when participants reporting within-scanner sleep were included (BALL = 0.15, p = 0.071; Fig. 3b). Regardless of withinscanner sleep, depression/anxiety severity was not significantly associated with other connectivity patterns, including within-DMN (BALL = −0.01, p = 0.141; BALERT = −0.09, p = 0.467), within-TPN (BALL = −0.07, p = 0.424; BALERT = −0.13, p = 0.252), ThalamusDMN (BALL = −0.02, p = 0.780; BALERT = −0.03, p = 0.796), or Thalamus-TPN connectivity (BALL = −0.05, p = 0.582; BALERT = −0.10, p = 0.388). Mania Proneness was significantly associated with Thalamus-DMN connectivity in participants reporting sustain wakefulness (BALERT = −0.25, p = 0.030; Fig. 3c), but this effect became non-significant in the whole sample (BALL = −0.13, p = 0.115; Fig. 3d). Regardless of within-scanner sleep, Mania Proneness severity was not significantly associated with within-DMN (BALL = −0.06, p = 0.501; BALERT = −0.12, p = 0.320), within-TPN (BALL = −0.15, p = 0.068; BALERT = −0.19, p = 0.089), DMN-TPN (BALL = 0.16, p = 0.058; BALERT = 0.21, p = 0.066), or Thalamus-TPN connectivity (BALL = 0.15, p = 0.069; BALERT = −0.17, p = 0.146).
4. Discussion Our goal was to examine effects of self-reported within-scanner sleep during rsfMRI on arousal-related functional connectivity and their relationship with affective psychopathology in a transdiagnostic sample of young adults. Among participants who reported within-scanner sleep versus sustained wakefulness, differences in demographic, clinical features, network connectivity were examined. We also explored whether within-scanner sleep modulated associations between arousal-related connectivity patterns and affective psychopathology measures. Withinscanner sleep was not associated with affective diagnoses or symptom severities, however we observed connectivity signatures of self-reported within-scanner sleep that overlapped with those of objectively-assessed arousal. Exploratory analyses also suggest that within-scanner sleep may attenuate associations between DMN connectivity patterns and mood symptom severities. Consistent with fMRI studies using concurrent EEG to objectively 129
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Fig. 3. Associations between network connectivity and mood symptom severities including (All Participants; N = 150) and excluding participants reporting within-scanner sleep (Alert Only; N = 84). (a) Depression/Anxiety factor score plotted versus averaged DMN-TPN connectivity in Alert participants and (b) including participants reporting within-scanner sleep. (c) Mania proneness factor score plotted versus averaged Thalamus-DMN connectivity in Alert participants and (d) including participants reporting within-scanner sleep. All analyses adjusted for age, sex, IQ, and scanner type. Abbreviations: CTL = healthy control participants; DS = distressed participants; DMN=default mode network; TPN=Task-Positive Network.
attention/salience network regions (lateral PFC, insula) during low arousal states. Because we could not pinpoint the timing of withinscanner sleep in our study, thalamo-cortical connectivity patterns observed here may reflect a combination low arousal and compensatory neural signatures supporting resumption or maintenance of wakefulness, though more nuanced analyses are needed to support this speculation. Nonetheless, our results indicate some overlap in the neural signatures of self-report within-scanner sleep and those of objective sleep or low arousal states. The final aim examined whether including participants reporting within-scanner sleep would alter associations between arousal-related network connectivity measures and mood symptom severity measures. Among alert participants, our exploratory analyses showed a positive association between Depression/Anxiety Severity and DMN-TPN connectivity, along with a negative association between Mania Proneness and Thalamus-DMN connectivity. Both effects were attenuated in the whole sample, when participants reporting within-scanner sleep were included in the analysis. While these exploratory analyses were subtle and should be interpreted as preliminary given the lack of multiple comparison correction, they provide some of the first empirical support for long-held concerns that within-scanner sleep has the potential to subtly attenuate relationships between network connectivity and psychiatric symptoms. Findings also suggest that connectivity of the DMN may be particularly vulnerable to such effects. This is consistent with tight links observed between DMN function and both sleepiness and affective disorders (Tagliazucchi and van Someren, 2017; WhitfieldGabrieli and Ford, 2012). The exact mechanism through which withinscanner sleep may attenuate brain-behavior relationships here is unclear; while sleep has effects on functional connectivity, it may also
detect sleep (Tagliazucchi and Laufs, 2014), 44% of our participants reported falling asleep during their scan. Our hypothesis that self-report of within-scanner sleep would be more common among participants with affective (mood, anxiety) diagnoses or greater mood symptom severity was not supported. Consistent with behavioral studies of sleepiness (van den Berg, 2006), average head-motion was greater in participants reporting within-scanner sleep. However, post-hoc exploratory analyses showed that including average framewise displacement as a covariate did not alter results of the main analyses. Withinscanner sleep was unrelated to other demographic, clinical, or scan measures. Overall, our data do not indicate greater propensity for reports of within-scanner sleep among participants with affective or other psychiatric diagnoses. Self-reports of within-scanner sleep were associated with several of the connectivity patterns observed in studies of light sleep, sleep deprivation, and lower waking arousal (Tagliazucchi and van Someren, 2017). Consistent with our prediction, we detected lower within-DMN connectivity and lower DMN-TPN anticorrelation among participants reporting sleep versus those who did not. Predictions about lower thalamo-cortical connectivity were partially supported. While thalamus-PCC connectivity was lower among participants reporting sleep, higher thalamus connectivity was observed with other regions (medial PFC, lateral PFC, left insula). Lower thalamus-PCC connectivity has been hypothesized to underlie reduced or impaired consciousness (He et al., 2015), consistent with our pattern of findings. The observation of greater thalamus connectivity among participants reporting within-scanner sleep is more consistent with effects of fluctuating arousal during rsfMRI (Poudel et al., 2018; Wang et al., 2016); these studies also report greater thalamus connectivity with ventral 130
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contribute to noise in the BOLD signal via respiratory or cardiac fluctuations (Tagliazucchi and van Someren, 2017). Though our self-report measure of within-scanner sleep was not associated with psychiatric disorder or symptoms, effects of within-scanner sleep on brain-behavior relationships may be magnified in more severe or chronic psychiatric samples, among whom sleep deprivation, insomnia or hypersomnia, or sedating psychotropic medication use is more common (Soehner et al., 2013). To avoid sleep confounds, it has been recommended that rsfMRI be combined with an objective measure of wakefulness (Tagliazucchi and van Someren, 2017), such as EEG or eye-tracking, particularly in samples where vigilance may be compromised. Alternatively, some have opted to apply machine classifiers to detect 30second epochs of sleep using rsfMRI time series data (Haimovici et al., 2017) and include only periods of sustained wakefulness in analyses (Kaufmann et al., 2016). A combination of these approaches would be beneficial in future psychiatric neuroimaging studies to mitigate arousal-related confounds in rsfMRI. Several strengths and limitations of the present analysis bear consideration. Key strengths include a large, unmedicated transdiagnostic sample consisting of healthy and psychologically distressed young adults; use of both regional and network connectivity methods; and comprehensive assessment of psychiatric symptomatology. On the other hand, results should be considered preliminary and cautiously interpreted due to our reliance on a subjective sleep measure. Sleep misperception, or perception of sleep as a waking state, is more common among those diagnosed with psychiatric disorders, and thus our estimates of sleep during the scan may be imprecise (Rezaie et al., 2018). As noted above, future rsfMRI studies in psychiatric samples would benefit from incorporating objective markers of sleep or arousal. Our study also did not include assessment of recent sleep patterns (Poudel et al., 2014) and caffeine use (Wong et al., 2016), which have strong effects on scan-day sleep propensity. This may have also impacted our ability to detect relationships between within-scanner sleep and psychiatric status. Exploratory analyses revealed links between selfreported within-scanner sleep and variability in the global signal, which tends to track strongly with fluctuating vigilance (see Supplement) (Samann et al., 2011; Wong et al., 2013; Yeo et al., 2015). While we opted to employ global signal regression for comparability with recent studies (Larson-Prior et al., 2011; Ong et al., 2015; Patanaik et al., 2018; Picchioni et al., 2014; Samann et al., 2011; Wang et al., 2016), signal processing methods may affect the connectivity signatures associated with sleep and their links with psychopathology. Our analysis emphasized a low arousal component of unstable wakefulness, restlessness, state anxiety, or agitation can also contribute to interindividual differences resting state connectivity, for example in sensorimotor or limbic networks (Bauer, 2013; Soros et al., 2019). The present analysis is not intended as a comprehensive assay of psychopathology-related brain networks associated with arousal state, simply a subset of networks previously shown to be modulated by low withinscanner arousal. The present analyses indicate that self-reported within-scanner sleep may be a common phenomenon during rsfMRI, regardless of psychiatric status. Moreover, self-reported sleep is associated with several of the DMN and thalamo-cortical connectivity patterns observed in objectively-measured light sleep. Finally, our analyses support conventional wisdom that sleep during fMRI scanning may affect the accurate characterization of brain-behavior relationships; herein, withinscanner sleep attenuated associations between mood symptoms (depression/anxiety, mania) and DMN functional connectivity. Withinscanner sleep during rsfMRI has the potential to act as a source of interindividual variance in network connectivity measures pertinent to affective disorders. Careful monitoring and correction for within-scanner sleep could improve the accurate detection of network signatures underlying affective psychopathology.
Funding This study was supported by funding from the National Institute of Mental Health grants R01MH100041 (M.L.P.) and K01MH111953 (A.M.S.). The funding source had no role in the analysis or manuscript preparation. CRediT authorship contribution statement Adriane M. Soehner: Conceptualization, Data curation, Formal analysis, Writing - original draft. Henry W. Chase: Conceptualization, Data curation, Formal analysis, Project administration, Writing - review & editing. Michele A. Bertocci: Conceptualization, Data curation, Formal analysis, Writing - review & editing. Tsafrir Greenberg: Project administration, Writing - review & editing. Ricki Stiffler: Project administration, Writing - review & editing. Jeannette C. Lockovich: Project administration, Writing - review & editing. Haris A. Aslam: . Simona Graur: Project administration, Writing - review & editing. Genna Bebko: Project administration, Writing - review & editing. Mary L. Phillips: Formal analysis, Funding acquisition, Writing - review & editing, Project administration. Declaration of Competing Interest None. Acknowledgments We would like to Erin Rodgers, B.S., for her assistance with manuscript preparation. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.07.066. References Andrews-Hanna, J.R., Reidler, J.S., Huang, C., Buckner, R.L., 2010. Evidence for the default network's role in spontaneous cognition. J. Neurophysiol. 104, 322–335. Annett, M., 1970. A classification of hand preference by association analysis. Br. J. Psychol. 61, 303–321. Anticevic, A., Cole, M.W., Repovs, G., Murray, J.D., Brumbaugh, M.S., Winkler, A.M., Savic, A., Krystal, J.H., Pearlson, G.D., Glahn, D.C., 2014. Characterizing thalamocortical disturbances in schizophrenia and bipolar illness. Cereb. Cortex 24, 3116–3130. Bauer, A.M., 2013. Review: collaborative care improves depression and anxiety symptoms in adults. Evid. Based Ment. Health 16, 40. Bonnet, M.H., Moore, S.E., 1982. The threshold of sleep: perception of sleep as a function of time asleep and auditory threshold. Sleep 5, 267–276. Chao-Gan, Y., Yu-Feng, Z., 2010. DPARSF: a MATLAB toolbox for "Pipeline" data analysis of resting-state fMRI. Front. Syst. Neurosci. 4, 13. Clark, L.A., Watson, D., 1991. Tripartite model of anxiety and depression: psychometric evidence and taxonomic implications. J. Abnorm. Psychol. 100, 316–336. Cui, H., Zhang, J., Liu, Y., Li, Q., Li, H., Zhang, L., Hu, Q., Cheng, W., Luo, Q., Li, J., Li, W., Wang, J., Feng, J., Li, C., Northoff, G., 2016. Differential alterations of resting-state functional connectivity in generalized anxiety disorder and panic disorder. Hum. Brain Mapp. 37, 1459–1473. Deco, G., Kringelbach, M.L., 2014. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 84, 892–905. Dell'Osso, L., Armani, A., Rucci, P., Frank, E., Fagiolini, A., Corretti, G., Shear, M.K., Grochocinski, V.J., Maser, J.D., Endicott, J., Cassano, G.B., 2002. Measuring mood spectrum: comparison of interview (SCI-MOODS) and self-report (MOODS-SR) instruments. Compr. Psychiatry 43, 69–73. First, M.B., Williams, J.B.W., Karg, R.S., R.L., S., 2015. Structured Clinical Interview For DSM-5—Research Version (SCID-5 For DSM-5, Research Version; SCID-5-RV). American Psychiatric Association, Arlington, VA. Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711. Gorgolewski, K., Burns, C.D., Madison, C., Clark, D., Halchenko, Y.O., Waskom, M.L., Ghosh, S.S., 2011. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform. 5, 13. Haimovici, A., Tagliazucchi, E., Balenzuela, P., Laufs, H., 2017. On wakefulness fluctuations as a source of BOLD functional connectivity dynamics. Sci. Rep. 7, 5908.
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