Schizophrenia Bulletin vol. 41 no. 6 pp. 1336–1348, 2015 doi:10.1093/schbul/sbv064 Advance Access publication May 26, 2015
Frequency-Specific Neural Signatures of Spontaneous Low-Frequency Resting State Fluctuations in Psychosis: Evidence From Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Consortium
Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT; 2Mental Health Institute of the Second Xiangya Hospital, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, Hunan, China; 3Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX; 4Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA; 5Department of Psychology, University of Georgia, Athens, GA; 6Department of Psychiatry, Johns Hopkins University, Baltimore, MD; 7The Mind Research Network, Albuquerque, NM; 8Department of Psychiatry, Yale University, New Haven, CT; 9Department of ECE, The University of New Mexico, Albuquerque, NM; 10Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; 11Department of Neurobiology, Yale University, New Haven, CT 1
*To whom correspondence should be addressed; 200 Retreat Avenue, Olin Neuropsychiatry Research Center, Hartford Hospital/IOL, Hartford, CT 06102, US; tel: 860-545-7483, fax: 860-545-7797, e-mail:
[email protected]
Background: We quantified frequency-specific, absolute, and fractional amplitude of low-frequency fluctuations (ALFF/fALFF) across the schizophrenia (SZ)-psychotic bipolar disorder (PBP) psychosis spectrum using resting functional magnetic resonance imaging data from the large BSNIP family study. Methods: We assessed 242 healthy controls (HC), 547 probands (180 PBP, 220 SZ, and 147 schizoaffective disorder—SAD), and 410 of their first-degree relatives (134 PBPR, 150SZR, and 126 SADR). Following standard preprocessing in statistical parametric mapping (SPM8), we computed absolute and fractional power (ALFF/fALFF) in 2 low-frequency bands: slow-5 (0.01–0.027 Hz) and slow-4 (0.027–0.073 Hz). We evaluated voxelwise post hoc differences across traditional Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition diagnostic categories. Results: Across ALFF/fALFF, in contrast to HC, BP/SAD showed hypoactivation in frontal/anterior brain regions in the slow-5 band and hypoactivation in posterior brain regions in the slow-4 band. SZ showed consistent hypoactivation in precuneus/ cuneus and posterior cingulate across both bands and indices. Increased ALFF/fALFF was noted predominantly in deep subcortical and temporal structures across probands in both bands and indices. Across probands, spatial ALFF/ fALFF differences in SAD resembled PBP more than SZ. None of these ALFF/fALFF differences were detected in relatives. Conclusions: Results suggest ALFF/fALFF is a putative biomarker rather than a familial endophenotype.
Overall sensitivity to discriminate proband brain alteration was stronger for fALFF than ALFF. Patterns of differences noted in SAD were more similar to those observed in PBP. Differential effects were noted across the 2 frequency bands, more prominently for BP/SAD compared with SZ, suggesting frequency-sensitive physiologic mechanisms for the former. Key words: fALFF/ALFF/bipolar/high risk/schizoaffective/relatives Introduction The traditional classification of major psychiatric disorders has been questioned recently because symptomatic boundaries not only overlap, but display rather limited biological validity.1 Schizophrenia (SZ) and psychotic bipolar disorder (PBP) share multiple characteristics, including risk genes and abnormalities in cognition, neural function, and brain structure.2–4 To overcome the traditional SZ/PBP dichotomy issue, “crosscutting” dimensional assessment of symptoms and clinical phenomena is advocated in the newly revised Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V).5 Lacking external validation criteria for classification, psychiatry has seen a shift toward utilization of objective biological markers that could ultimately be used to develop targeted treatments. Such biological
© The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email:
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Shashwath A. Meda*,1, Zheng Wang2, Elena I. Ivleva3, Gaurav Poudyal3, Matcheri S. Keshavan4, Carol A. Tamminga3, John A. Sweeney3, Brett A. Clementz5, David J. Schretlen6, Vincent D. Calhoun6–9, Su Lui10, Eswar Damaraju7, and Godfrey D. Pearlson1,8,11
Frequency-Specific Resting Neural Signatures in Psychosis
promising results. ALFF is defined as the total power within a specific frequency range and thus indexes the strength or intensity of LFO. fALFF, on the other hand, measures the power within a specified band normalized to the power in the entire detectable frequency range, thus representing the relative contribution of a specific LFO to the whole frequency range. fALFF is thought to be an improvement over ALFF; however, each index has its own pros and cons.16,17 For example, fALFF is previously reported to have higher specificity but lower reliability to gray matter signal, vs ALFF. Therefore, in order to maximize reliability across subjects while providing sufficient specificity to capture interindividual differences (as recommended by Zuo et al12), we report both metrics in the present study. Although the exact origins and interacting mechanisms behind ALFF/fALFF are unknown, neurophysiologically, these measures have been proposed to be local intensity estimates of spontaneous brain activity7,9,18 and have previously shown to be robustly sensitive to signals originating in gray matter.7,16 Recent studies analyzing concurrent electroencephalogram (EEG) and rs-fMRI have helped shed more light on the underlying neurophysiology of these LFOs in rs-fMRI. Rodent studies suggest that rs-LFOs are correlated with synchronized delta EEG oscillations.19 Similarly, another study using simultaneous EEG-fMRI reported LFOs in monkey visual cortex were correlated with local field gamma band power.20 In humans, increased alpha power has been linked to decreased rs-LFOs in multiple regions, including occipital, superior temporal, inferior frontal, and cingulate cortex.21 Additionally, several studies have shown taskrelated modulation of LFO amplitudes, especially in the realm of working memory and motor performance.18,22 Rs-LFOs have also been related to arousal level, by demonstrating that sleep produces stage-dependent alterations in amplitude patterns.23–25 Specific to SZ, a prior dual-band examination of ALFF/ fALFF showed differential patterns between the slow-5 and slow-4 bands mainly in basal ganglia, midbrain, and ventromedial prefrontal cortex, suggesting that the 2 different frequency bands might be differentially sensitive in SZ.26 Similar frequency-dependent ALFF changes have also been reported in elderly subjects with amnestic mild cognitive impairment.27 Most ALFF/fALFF studies have used the conventional (0.01–0.08) band, with variability mainly due to sample size, medication effects, analytic techniques, and thresholding. However, regions showing the most consistent decreases in ALFF/fALFF in SZ across studies are medial prefrontal cortex (MPFC), cuneus/precuneus, posterior cingulate cortex (PCC), and medial occipital gyrus. Similarly, regions most consistently showing increased ALFF/fALFF in SZ were hippocampal/parahippocampal cortex and other subcortical structures like amygdala, thalamus, putamen, and caudate. Studies evaluating ALFF/fALFF in bipolar subjects are 1337
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signatures could also be examined as candidate endophenotypes by testing these measures in unaffected relatives to ultimately improve understanding of underlying genetic/molecular risk mechanisms. In contrast to task-related functional magnetic resonance imaging (fMRI) experiments where low-frequency signals are filtered out, in resting-state fMRI (rs-fMRI), low-frequency oscillations (LFOs; typically in the 0.01– 0.08 Hz frequency band) are physiologically relevant and related to neuronal fluctuations in brain gray matter.6,7 Such LFOs in general may represent an integration of neuronal firing effects with longer delays and larger variability and that recruit larger brain areas, compared with high-frequency oscillations that are characteristically more localized and incorporate synaptic events from spatially close regions.8 Several neuropsychiatric studies have evaluated LFO connectivity or coherence and demonstrated their usefulness as biomarkers and/or endophenotypes.2,3,9,10 However, research on regional or local properties of the brain’s intrinsic functional dynamics is lacking. Conventionally, rs-LFOs have been examined in the 0.01–0.08 frequency band.6,7,9 However, seminal studies by Penttonen, Buzsaki, and Draguhn suggest that neuronal oscillation classes are arrayed linearly when plotted on a logarithmic scale. They concluded that this regularity (and empirical data collected at higher frequencies) suggests that independent frequency bands are generated by distinct oscillators, each with specific properties and unique physiological functions.8,11 They also asserted that neuronal oscillations in adjacent bands do not co-occur within a particular structure, by showing that major rest-/sleep-related oscillations (delta, alpha, etc.) are separated by active-state-related mechanisms. Zuo and colleagues were the first to extend this concept to rs-fMRI. They evaluated LFOs in 4 distinct bands— slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073), slow-3 (0.073–0.198 Hz), and slow-2 (0.198–0.25 Hz) and demonstrated differential and interactive amplitude effects within them in several brain regions.12 Recent reports have further confirmed that integration of brain function occurs within multiple frequency bands and might have different neural manifestations, thus urging the scientific community to examine frequency-specific effects.6,13,14 In the current study, we chose to adopt the Buzsaki framework, but only examine the slow-5 (0.01–0.027 Hz) and slow-4 (0.027–0.073) bands because these encompass most of the traditional 0.01–0.08 frequency spectrum and have minimal overlap with potential physiological noise frequency.7 Local properties of spontaneous activity can be probed using various methods including regional coherence Regional Homogeneity, power spectrum analysis (absolute and fractional amplitude of low-frequency fluctuations [ALFF/fALFF], fractal dimension), and temporo-spatial clustering.15 In this study, we evaluated ALFF and fALFF because they have yielded the most
S. A. Meda et al
Materials and Methods Study Sample Subjects passing all quality control (steps listed in methods) (N = 1199) and included in the final analysis comprised 242 healthy controls (HC), 547 probands (180 PBP, 220 SZ, and 147 schizoaffective disorder—SAD), and 410 first-degree relatives (134 PBPR, 150 SZR, and 126 SADR). All subjects were administered similar rs-fMRI protocol across 6 sites (Baltimore, Boston, Chicago, Dallas, Detroit, and Hartford). Probands/relatives Axis-I diagnoses were based on Structured Clinical Interview for DSM-IV TR Diagnosis (SCID-I/P).31 These diagnostic definitions (SZ, SAD, PBP) were established based on all available clinical information, followed by a formal diagnostic consensus discussion by at least 3 experienced clinicians (MD, PhD, or Master’s level), to improve interrater reliability of clinical diagnosis. As described previously, probands were stable, medicated outpatients.32 Relatives with lifetime psychiatric diagnoses were asymptomatic/mildly symptomatic during the imaging session.32 Relatives meeting criteria for Axis-I “proband-like” psychotic disorders (SZ, SAD, PBP) were regrouped into the corresponding proband category. The 1338
remaining biological relatives comprised SZR, SADR, and PBPR groups. We define “unaffected” status in relatives as absence of lifetime psychotic disorders. The study protocol was approved by each local site IRB. After complete description of the study to the participants, written informed consent was obtained. Complete demographic and clinical characteristics of the study sample are provided in table 1. Concomitant medications data are reported in supplementary table 1. Further, to evaluate current symptom severity, all probands were administered the Positive and Negative Syndrome Scale (PANSS).33 Data Acquisition Imaging Data. All subjects underwent a single 5-min run of resting state fMRI and a 3D T1-weighted structural scan on a 3T scanner at each site. Participants were instructed to keep their eyes open, focus on a crosshair displayed on a monitor, and remain still during the entire scan. In addition, head motion was restricted with a custom-built head-coil cushion. Alertness during the scan was confirmed immediately afterward. If necessary, the scan was repeated. Differences in scanning parameters and sites were handled appropriately during both preprocessing and statistical analysis (see supplementary table 2 for scanning parameters). Data Processing Preprocessing and ALFF/fALFF Computation. Prepro cessing of fMRI, T1-weighted and computation of ALFF/fALFF images were conducted using the REST toolbox.34 The initial 6 images, during which T2-effects stabilized, were discarded. Images were then realigned and corrected for slice timing differences, acquisition parameters such as repetition time, slice acquisition direction etc. The above pipeline was adapted individually for each site due to differences in scanning parameters. At this stage, subjects with excessive motion (>3 mm of motion and/or 3° rotation) were dropped from further analysis. Next, structural and functional images were co-registered to each other. Six motion parameters, signal from the cerebro spinal fluid (CSF), and white matter were used as nuisance covariates to reduce effects of head motion and non-neuronal BOLD fluctuations. Structural images were normalized using the DARTEL technique.35 Normalization was checked visually and any anomalous data were discarded. For the remaining subjects, normalization parameters obtained during the previous step were then applied to the functional images to bring them into a common DARTEL-MNI space. Also, linear detrending was performed before computation of ALFF/fALFF. Following the above preprocessing, ALFF images were computed by extracting power spectra via a Fast Fourier Transform and computing the sum of amplitudes in 2 separate low-frequency bands: slow-5 (0.01–0.027 Hz)
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few, with only 2 reporting ALFF (0.01–0.08) differences; these showed decreased ALFF in postcentral, parahippocampal, lingual gyri, and cerebellum. Increased ALFF in bipolars was noted mainly in the dorsolateral prefrontal cortex, middle frontal, insula, orbito-frontal cortex, and anterior cingulate cortix (ACC).28–30 As noted, only a few studies, in generally smallish samples, have evaluated this promising biological marker to compare SZ and PBP. More importantly, no study has yet evaluated ALFF/ fALFF across different frequency bands across a broad psychosis spectrum. The current study employed data from a large multisite study, Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP),31 established to test the manifestations and distribution of multiple putative intermediate phenotypes including rs-fMRI across DSM-IV psychosis-centered diagnostic categories. We sought to answer 2 primary questions (1) to evaluate shared and unique characteristics of ALFF/fALFF across psychosis spectrum diagnoses across different frequency bands and (2) to test ALFF/fALFF as putative disease biomarkers and/ or endophenotypes, evaluating these measures across psychosis probands and their biological relatives. Based on previous findings, we hypothesized (1) decreased ALFF/ fALFF power in cuneus/precuneus, posterior cingulate, lingual gyrus, and occipital areas and increased power in striatal and deep brain regions in psychosis probands and (2) similar, albeit diminished ALFF/fALFF traits in relatives of probands. Furthermore, we predicted differential effects in probands across different bands, especially in basal, midbrain, and medial prefrontal regions.
42.56 57.44
90.91 9.09
12.40 84.30 0.83 2.48
21.07 3.72 21.49 23.55 9.50 20.66
103 139
220 22
30 204 2 6
51 9 52 57 23 50
35 4 52 28 29 32
27 151 1 1
165 15
58 122
N
N/A N/A N/A
N/A N/A N/A
%
12.65
38.14
19.44 2.22 28.89 15.56 16.11 17.78
15.00 83.89 0.56 0.56
91.67 8.33
4.38 3.61 0.09
13.04
32.22 67.78
%
12.77 11.79 7.79
36.94
SD
Mean
SD
Mean
30 1 27 40 5 44
12 132 3 0
130 17
66 81
N
18.12 15.49 5.04
35.08
Mean
20.41 0.68 18.37 27.21 3.40 29.93
8.16 89.80 2.04 0.00
88.44 11.56
44.90 55.10
%
5.32 4.95 1.6
12.01
SD
Schizoaffective (N = 147)
71 5 33 34 27 50
28 186 3 3
202 18
145 75
N
16.91 16.27 1.35
35.15
Mean
32.27 2.27 15.00 15.45 12.27 22.73
12.73 84.55 1.36 1.36
91.82 8.18
65.91 34.09
%
5.42 5.93 1.24
12.31
SD
Schizophrenia (N = 220)
30 4 32 12 13 43
15 118 1 0
121 13
49 85
N
N/A N/A N/A
40.59
Mean
22.39 2.99 23.88 8.96 9.70 32.09
11.19 88.06 0.75 0.00
90.30 9.70
36.57 63.43
%
N/A N/A N/A
16.13
SD
Bipolar Relatives (N = 134)
24 3 16 29 4 50
14 106 4 2
120 6
40 86
N
N/A N/A N/A
41.01
Mean
19.05 2.38 12.70 23.02 3.17 39.68
11.11 84.13 3.17 1.59
95.24 4.76
31.75 68.25
%
N/A N/A N/A
16.14
SD
Schizoaffective Relatives (N = 126)
b
39 7 17 21 14 52
18 129 1 2
134 16
49 101
N
N/A N/A N/A
43.33
Mean
26.00 4.67 11.33 14.00 9.33 34.67
12.00 86.00 0.67 1.33
89.33 10.67
32.67 67.33
%
N/A N/A N/A
15.55
SD
Schizophrenia Relatives (N = 150)
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Note: aAge post hoc differences: SADR, SZR > HC; SZR, BPR, SADR > BP; HC, BPR, SADR, SZR > SZ; BPR, SADR, SZR > SAD. PANSS_Positive post hoc differences: SZ > BP; SAD > SZ, BP. c PANSS_Negative post hoc differences: SZ > BP, SAD; SAD > BP. d Schizo-Bipolar Scale post hoc differences: BP > SAD > SZ.
Sex Male Female Ethnicity Non-Hispanic Hispanic Handedness Left Right Ambidextrous Missing Sites Baltimore Boston Chicago Dallas Detroit Hartford
N
Age (years) Clinical Scores PANSS Positive PANSS Negative Schizo-Bipolar Scale
Demographics
Bipolar (N = 180)
Healthy Controls (N = 242)
Table 1. Detailed Demographics and Clinical Characteristics of the Study Population
0.460
