A mega-analysis of low frequency resting-state measures in mood and psychosis-spectrum disorders

A mega-analysis of low frequency resting-state measures in mood and psychosis-spectrum disorders | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search A mega-analysis of low frequency resting-state measures in mood and psychosis-spectrum disorders View ORCID Profile Maya L. Foster , View ORCID Profile Milana Khaitova , View ORCID Profile Saloni Mehta , Jean Ye , View ORCID Profile Dustin Scheinost doi: https://doi.org/10.1101/2025.08.15.25332894 Maya L. Foster 1 Department of Biomedical Engineering, Yale University M.S., M.Phil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maya L. Foster For correspondence: maya.foster{at}yale.edu Milana Khaitova 2 Department of Radiology & Biomedical Imaging, Yale School of Medicine B.A. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Milana Khaitova Saloni Mehta 2 Department of Radiology & Biomedical Imaging, Yale School of Medicine M.B.B.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Saloni Mehta Jean Ye 3 Interdepartmental Neuroscience Program, Yale School of Medicine M.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dustin Scheinost 1 Department of Biomedical Engineering, Yale University 2 Department of Radiology & Biomedical Imaging, Yale School of Medicine 3 Interdepartmental Neuroscience Program, Yale School of Medicine 4 Department of Statistics & Data Science, Yale University 5 Yale Child Study Center, Yale School of Medicine 6 Yale Biomedical Imaging Institute, Yale School of Medicine Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dustin Scheinost Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Objective Conduct a mega-analysis of two complementary measures of resting-state functional magnetic resonance imaging (rsfMRI) dynamics—amplitude of low-frequency fluctuation (ALFF) and low-frequency spectral entropy (lfSE)—in mood and psychosis-spectrum disorders to evaluate group differences and clinical symptom associations. Design ALFF and lfSE were calculated at the node-level by filtering data from 0.01 Hz to 0.08 Hz, regressing demographic variables, and harmonizing sites. Group differences were assessed using the Wilcoxon signed test. Symptom associations were evaluated with Spearman’s rho. Analyses were conducted at both whole-brain and network levels, with sensitivity analyses to evaluate the impact of frequency brands. Setting Four independent open-source case-control datasets with resting-state functional magnetic resonance imaging were used: the Center for Biomedical Research Excellence, the Human Connectome Project for Early Psychosis, the Strategic Research Program for Brain Sciences, and the UCLA Consortium for Neuropsychiatric Phenomics. Participants Included participants had a mood disorder (bipolar, dysthymia, or major depressive disorder, n=228, aged 38.31 ± 12.56 years), a psychosis-spectrum disorder (early psychosis, schizophrenia spectrum disorder, or mood disorder with psychotic symptoms, n=318, aged 29.8 ± 13.21 years), or a healthy control (n=535, aged 39.89 ± 15.3 years). Main outcomes and Measures To identify group differences and symptom associations in mood and psychosis-spectrum disorders using ALFF and lfSE. Results ALFF in psychosis-spectrum was significantly lower than mood disorders and controls (q’s<0.001) at the whole-brain and network levels. lfSE in controls was significantly lower than both psychosis-spectrum and mood disorders at the whole-brain and network levels (q’s<0.001). Whole-brain ALFF is positively associated with mood symptoms (rho=0.27, p<0.05). Whole-brain lfSE is negatively associated with positive (rho=-0.13, p<0.05) and mood (rho=-0.38, p<0.01) symptoms. A greater sensitivity of group differences and symptom associations to frequency ranges was observed in mood disorders. ALFF is sensitive to medication. Conclusions and Relevance Widespread, global differences in ALFF and lfSE underly psychosis-spectrum and mood disorders. lfSE may be applicable for wider use in fMRI. Differences in spectral measures of brain dynamics may represent shared and distinct markers of mental health. Question How do the amplitude and complexity of low-frequency oscillations in fMRI signals associate with psychosis-spectrum and mood disorders? Findings Findings suggest that the amplitude and complexity of low-frequency oscillation are associated with mood and psychosis-spectrum disorders in a wide-spread and global manner. Complexity, which is well-studied in EEG but not fMRI— emerged as a promising measure for further research. Our results also support a frequency-based behavioral encoding system operating in the BOLD signal. Meaning Our findings indicate that mood and psychosis-spectrum disorders differ from healthy controls in a mechanistically distinct way, as well as from one another across the whole brain. They also suggest that differences in low-frequency oscillations may represent shared and distinct markers of mental health and may help in developing treatment strategies that target these disruptions. INTRODUCTION Mood (e.g., major depressive disorder and bipolar disorder) and psychosis-spectrum (e.g., first-episode psychosis and schizophrenia) disorders cause significant disability globally 1 – 4 and current treatment outcomes remain suboptimal. These disorders also have shared and unique genetic risks 5 – 11 . Thus, continuing to advance knowledge of their shared and unique mechanisms will be important for improving differential diagnosis 11 , 12 and symptom management for affected individuals. Widely associated with cognition and mental health 13 – 16 , changes in brain dynamics may be linked to the pathophysiology of mood and psychosis-spectrum disorders. However, while applications of brain dynamics methods are becoming increasingly popular, there is a scarcity of work exploring their simultaneous application to mood and psychosis-spectrum disorders, raising questions of whether linked dynamics signatures are shared, distinct, or both. Spectral measures succinctly characterize brain dynamics across frequency bands 17 – 19 . One widely used metric is the amplitude of low-frequency fluctuations (ALFF), which quantifies the signal strength in the 0.01-0.08 Hz range and is well-established in functional neuroimaging 20 – 24 . A comparable measure is low-frequency spectral entropy (lfSE)—a frequency-bounded derivative of spectral entropy that quantifies the complexity or unpredictability of fMRI signal fluctuations in the 0.01–0.08 Hz range. Spectral entropy has been broadly used in EEG 24 , but not in fMRI studies. ALFF and lfSE provide distinct, but complementary insights (amplitude versus complexity) into intrinsic brain dynamics that may help biologically differentiate disorders and mediate individual differences in symptom severity. Prior work has identified ALFF differences across major functional networks in mood 25 – 27 and psychosis-spectrum 28 – 30 , 27 disorders. In schizophrenia, studies found reduced ALFF in the default mode (DMN), salience (SAL), sensorimotor, parietal, and visual networks compared to healthy controls 31 . Early-stage schizophrenia has been marked by increased ALFF in subcortical and visual networks and decreased ALFF in the DMN and parietal networks 31 . In contrast, chronic schizophrenia includes widespread increases in ALFF across frontal, SAL, temporal, and limbic networks, alongside reductions in sensorimotor, parietal, and occipital areas 31 . In depression, studies report increased ALFF in several regions—including lateralized subcortical areas, occipital lobe, limbic areas, frontoparietal network (FPN), and the DMN— compared to matched healthy controls 26 , 32 – 35 . Decreased ALFF is also observed in somatosensory cortex 25 , 36 , 32 , limbic areas, and cerebellum 37 , 38 for major depressive disorder (MDD) compared to controls. In bipolar disorder, increased ALFF in the right caudate and putamen 39 , bilateral insula, medial prefrontal cortex, and decreased ALFF in the left cerebellum 40 have been observed. In contrast, lfSE has not been studied in mood and psychosis-spectrum disorders using fMRI. Moreover, to our knowledge no previous work has comprehensively contrasted ALFF with lfSE in mood and psychosis-spectrum disorders in one study. Mega-analyses (e.g., a method that pools together the raw, individual level data from multiple studies), are a promising approach for finding shared and distinct biological indices across disorders. By pooling data from several smaller studies, mega-analyses maximize population variance, increase generalizability, and improve statistical power 41 , 42 . ALFF and lfSE are ideal for these analyses because they are computationally tractable in large samples, easy to pool across diverse datasets due to their conceptual simplicity, and neurobiologically interpretable at different spatial scales (i.e., regional, network, or whole brain levels). By better accounting for psychiatric heterogeneity, mega-analyses analyses can support the development of treatments that target shared and distinct clinical mechanisms 43 – 45 . Here, we perform a mega-analysis in over 1000 individuals to compare ALFF and lfSE in individuals with mood disorders, individuals with psychosis-spectrum disorders and healthy controls. A secondary objective was to assess the utility of lfSE compared to ALFF. Our study expands on previous empirical findings by adding a complexity measure (i.e., lfSE) 46 , using a large-scale sample size (n=1081), and comparing measures across a wider range of symptoms. We also assess their association with relevant symptom measures. Based on findings from previous works, we hypothesized that ALFF and lfSE case-control group differences will be in the FPNl 47 , 48 , SAL 47 , and DMN 47 , 49 . METHODS Participants We used four publicly available resting-state fMRI datasets (see Table S1 for participant demographics), the Center for Biomedical Research Excellence 50 (COBRE, n=99), the Human Connectome Project Early Psychosis 51 (HCP-EP, n=169), the Strategic Research Program for Brain Sciences 52 (SRPBS, n=609) Multi-disorder Connectivity Dataset, and the University of California Los Angeles (UCLA) Consortium for Neuropsychiatric Phenomics 53 (CNP, n=204). The sample included 535 healthy controls (HCs), 228 mood disorder (major depressive disorder, bipolar disorder, and dysthymia), and 318 psychosis-spectrum disorder (early psychosis and schizophrenia spectrum disorder) participants. All patients were diagnosed according to the DSM-5 54 . Demographics, symptom scores, medication information, and diagnosis breakdowns are summarized in the Supplement methods and tables S1, S5, and S6. Symptom Measures Psychosis symptoms were assessed with the Positive and Negative Syndrome Scale 55 (PANSS), which measures the presence and severity of positive, negative, and general psychopathology for an individual within the past week. The scale is widely used in clinical psychosis studies 56 and has demonstrated reliability in assessing psychopathology of schizophrenia populations 57 . 230 participants had PANSS scores (HCP-EP=107, SRPBS=123). Mood symptoms in individuals (n=62) with a mood disorder (SRPBS=39) and health controls (SRPBS=23) were assessed with the Beck’s Depression Inventory 58 (BDI-II). The BDI-II measures hallmark symptoms of depression and has high internal consistency 58 , 59 . Data Preprocessing The acquisition and imaging parameters for the datasets are detailed elsewhere 50 , 51 , 60 , 52 . However, an abridged version is available in the Supplement (see Table S2). Images were motion and slice time corrected using SPM12 61 . BioImage Suite was used to perform mean white matter regression, cerebral spinal fluid regression, and gray matter time, removing linear trends, and low-pass filtering 62 . The Shen-268 atlas 61 was warped from Montreal Neurological Institute (MNI) space into single-subject space. Next, the mean-time course of each node was calculated by averaging its constituent voxels’ time-series data. Quality Control Participants were excluded if they had an average frame-to-frame displacement exceeding 0.2 mm ( n=28), insufficient quality of linear or nonlinear registration (n=16), had >100 missing nodes (n=10) or had less than 75% coverage of relevant nodes for a given network or whole-brain analysis (n=15, see Supplement for more details on data preservation process). This left a final sample of 1081 subjects for further analysis: 823 for whole-brain analyses (averaging across all nodes) and varying sample sizes for network analyses (Table S3-4). ALFF Calculation and Analysis First, the power spectral density of each node’s time series was calculated using periodogram function from the SciPy 63 signal processing API in Python 3.9. This calculation entailed converting each participant’s node time-series into the frequency domain via the fast Fourier transform (FFT). Then, the power spectral density was integrated over the low-frequency range (0.01 Hz ≤ frequency ≤ 0.08 Hz) using SciPy’s trapezoid integration method to calculate total power. Next, we took the square root of that operation to quantify ALFF. ALFF in this study is considered a proxy for the intensity of neural-associated brain activity or the energy content of the low-frequency band. A high ALFF signifies greater oscillation intensity while a low ALFF signifies lower oscillation intensity in the low-frequency range. ALFF was assessed group-wise at the whole-brain by averaging all nodes and network-levels using 10 canonical networks from the Shen 268 atlas. Low-frequency Spectral Entropy Calculation and Analysis lfSE was also calculated at the node-level. Individual node time-series underwent a band-pass filter in the low-frequency range (0.01 Hz ≤ frequency ≤ 0.08 Hz) using SciPy’s butter and filtfilt functions (order = 2) 21 . The signal was transformed into the frequency domain using FFT, followed by power spectral density calculation and normalization to a probability density function. Then, lfSE was calculated as the Shannon entropy of the power spectral density was calculated using AntroPy 64 package in Python 3. As a complexity measure, high lfSE (flatter power spectral density spectrum) represents low complexity while low lfSE (power spectral density distribution with more peaks) represents high complexity. lfSE was assessed group-wise at the whole-brain by averaging all nodes and network-levels using 10 canonical networks from the Shen 268 atlas. Statistical Analyses Statistical analyses were constrained to the whole-brain and network levels to capture macroscale brain dynamics. Broader scale such as these have greater power than granular levels of inference 65 . Group comparisons of each low-frequency brain measure were evaluated at p<0.05 using the Wilcoxon two-sided rank sum test while correcting for multiple comparisons with false discovery rate (FDR). We controlled for self-reported age, motion, and sex as variables of non-interest. First, these factors were regressed 66 using a generalized linear model on ALFF and lfSE values at the node level. Second, we applied the neuroComba t 67 R package to harmonize data across sites. For empirical ranges, please refer to table S5). Where relevant, we report corrected (q) and uncorrected (p) results for transparency but only interpret significant corrected results (q<0.05). Secondary analyses We correlated symptoms scores (PANSS and BDI-II) and covariate-regressed ALFF and lfSE values using Spearman’s correlation at the whole-brain and network levels. Participants for the PANSS analyses came from multiple sites and underwent site harmonization using neuroCombat 67 . Participants for the BDI-II analyses came from one site, so harmonization was not applied. Network-level associations were considered statistically significant at q<0.05 after FDR correction. Additionally, we evaluated similarity between the ALFF and lfSE by correlating these values across participants (p<0.05) using Spearman’s rho at the whole-brain and network levels. We also calculated these similarities independently for each group. These correlations were Fisher transformed and compared across groups using a two-sample z-test. Sensitivity Analysis We used different frequency cutoffs (0.01-0.045 Hz and 0.035-0.09 Hz) to assess if finer spectral divisions were driving the identified trends and associations 68 . We also examined the effect of medication exposure by including current medication status (binary: 1=on, 0=off) as an additional covariate. RESULTS Demographic differences between clinical groups Significant group differences were found for self-reported sex (χ²=18.081, df = 2, p<0.001), age (W=75.48, df=2, p<0.001) and average frame displacement (W= 8.599, df=2, p=0.014). Participants with psychosis-spectrum (29.8 ± 13.21 years) were significantly younger than mood disorders (38.31 ± 12.56 years) and controls (39.89 ± 15.3 years). Participants with mood disorders also had significantly higher motion frame displacement (0.087 ± 0.045 mm) compared to matched HCs (0.083 ± 0.046 mm) and those with psychosis-spectrum disorder (0.075 ± 0.037 mm). Group differences in ALFF At the whole-brain level, mood disorders exhibited the greatest ALFF, followed by controls and then psychosis-spectrum ( Figure 2A , Table 1 ). Psychosis-spectrum ALFF values were significantly lower than both mood disorders and controls (p’s<0.001). No differences in whole-brain ALFF were detected between mood disorders and controls. Similar differences were observed at the network level, where all networks showed significant (q’s<0.05, FDR-corrected) differences when comparing psychosis-spectrum to mood disorders or health controls ( Table 1 ). Results were similar when potential outliers were removed (Table S8) and when removing psychosis-spectrum patients with known affective disorders (Table S9). Download figure Open in new tab Figure 1. Schematic of analysis pipeline at node and network levels. This figure illustrates the study design and measurement calculation process. Four large-scale datasets were used in this study. A) First, the power spectrums were taken from preprocessed node-wise data, and the two low-frequency measures were calculated. Then, covariate regression of nuisance variables such as age, mean frame displacement, and sex were regressed using a generalized linear model. Where relevant, harmonization was performed to account for site effects. Then, the appropriate means were calculated for whole-brain (average over all nodes) and network (average over all member network nodes were taken for each of the 10 functional networks) analyses. B) Finally, group differences and symptom associations at whole-brain and network levels were calculated. FFT=Fast Fourier transform, ALFF=Amplitude of Low-Frequency Fluctuation, lfSE=low-frequency spectral entropy. x =mean. Download figure Open in new tab Figure 2. Group Differences at Whole-brain Level and Symptom Associations. Panels A and B feature a violin plot of the results for ALFF and lfSE respectively. Dots represent the participants (n=823; controls=366, mood=139, psychosis=318), colors represent the groups (controls, mood, psychosis). Lines of fit are based on linear model (for visualization). Correlations are reported as Spearman’s ρ. N.S. = not significant. ***=p<0.001. Panels C-E depict significant ALFF and lfSE symptom associations. Dots represent participants with a symptom score. A) Psychosis disorders had significantly lower ALFF than controls and mood disorders. B) Controls had significantly lower lfSE than mood and psychosis disorders. C) Significant BDI-II symptom associations with whole-brain ALFF (n=62). D) Significant positive PANSS symptom associations with whole-brain lfSE (n=230). E) Significant BDI-II symptom associations with whole-brain lfSE (n=62). View this table: View inline View popup Download powerpoint Table 1. Amplitude of low-frequency (ALFF, 0.01-0.08 Hz) value distributions by whole-brain, network and clinical group. Reported values have had covariates linearly regressed and undergone site harmonization. n=sample size. Bolded table entries represent significant comparisons after FDR correction. Group differences in lfSE At the whole-brain level, psychosis-spectrum had the highest lfSE, followed by mood disorders and then HCs ( Figure 2B ). Controls were significantly lower than both psychosis-spectrum and mood disorders at the whole-brain (p’s<0.001). No differences in whole-brain lfSE were detected between psychosis-spectrum and mood disorders. At the network level, all networks exhibited significant differences between health controls and psychosis-spectrum (q’s<0.05, FDR-corrected). The medial frontal, FPN, motor, subcortical, and cerebellar networks exhibited significant differences between HCs and mood disorders (q’s<0.05, FDR-corrected). While no differences between psychosis-spectrum and mood disorders existed at the whole-brain level, every network (other than the cerebellar network) exhibited significant between-group differences (q’s<0.05, FDR-corrected), likely due to the increases sample size used in the network analyses ( Table 2 ). Results were similar when removing psychosis-spectrum patients with affective disorders (Table S10). View this table: View inline View popup Download powerpoint Table 2. Low-frequency spectral entropy (lfSE, 0.01-0.08 Hz) value distributions by whole-brain, network and clinical group. Reported values have had covariates linearly regressed and undergone site harmonization. n=sample size. Bolded table entries represent significant comparisons after FDR correction. The psychosis-spectrum lfSE distribution was bimodal. Since the second (higher) peak in the distribution may influence the results, we repeated the analyses after removing these high-leverage points (see Supplemental Methods, Figure S4). Results were similar (Table S11); however, mood disorders now showed the highest lfSE, and only visual networks remained significantly different between psychosis-spectrum and mood disorders (q’s<0.05, FDR-corrected). Associations with psychosis and mood symptoms Whole-brain ALFF is positively associated with BDI-II scores (rho=0.36, p= 0.0046; Figure 2C , Table S12) but had no association with PANSS scores (Table S13). Additionally, whole-brain lfSE is negatively associated with positive PANSS (rho=-0.13, p=0.0418, Table S14) and BDI-II scores (rho=-0.38, p=0.002; see Figure 2D-E , Table S15). No other significant associations with ALFF or lfSE at the whole-brain or network-level were observed (Tables S12-15). Correlation of ALFF and lfSE At all scales of analysis, ALFF and lfSE were negatively correlated, such that individuals with higher ALFF values had lower lfSE values (rho’s = -0.39 to -0.22, Table S16-17). This relationship was also observed in our group comparison, where ALFF was consistently greater in mood disorder compared to psychosis-spectrum across scales and lfSE was consistently greater in psychosis-spectrum compared to mood disorder across scales. Sensitivity analyses of finer-scale frequency ranges Results at the finer-scale frequency ranges were broadly similar to the main results, with wide-spread differences observed between groups (Tables S18-25). However, results involving the mood disorder group exhibited the most changes in finer-scale frequency ranges. For example, the lfSE of the DMN was significantly different between controls and mood disorders at 0.035-0.090 Hz, but not 0.01-0.045 Hz or the full frequency range. Additionally, whole-brain ALFF was positively associated with BDI-II scores at 0.01-0.045 Hz but negatively associated at 0.035-0.09 Hz (Table S22). Medication differentially impacts ALFF and lfSE Results after controlling for medication were broadly similar for lfSE, but not ALFF ( Table 3 , Table S26). Only comparisons involving the mood disorder group were significant for ALFF. The ALFF comparisons between controls and psychosis spectrum were not significant. View this table: View inline View popup Download powerpoint Table 3. Effect of Medication Exposure on ALFF Group Differences. A subsample of the patient group had known exposure to a psychiatric medication and a sensitivity analysiswas performed on this subsample. Mood-psychosis effects remained consistent with analyses that did not consider medication exposure. However, case-control differences for mood disorders became significant, while those for psychosis-spectrum disorders became insignificant. Motor and visual I networks were analyzed using the mean only harmonization algorithm (see Supplementary methods). DISCUSSION In this study, we conducted a mega-analysis using two spectral measures— oscillation intensity (ALFF) and complexity (lfSE) —to compare mood and psychosis-spectrum disorders. Our results highlight extensive differences between these two clinical conditions that span multiple dimensions of brain dynamics. Notably, ALFF and lfSE uniquely differentiated clinical groups, providing distinct insights into the biological underpinnings of mood and psychosis-spectrum disorders. Psychosis-spectrum participants exhibited larger, widespread differences in lfSE compared to mood disorder participants and controls, while mood disorder participants exhibited more focal differences in lfSE compared to controls. Also, in line with several studies 25 , 26 , 32 – 38 , we observed significant differences between mood disorder participants and controls in ALFF after controlling for medication. In some cases, ALFF and lfSE results even converged. For example, despite our initial hypotheses, there is widespread overlap in the functional networks that had significant group differences between mood and psychosis-spectrum disorders. Our sensitivity analyses revealed that ALFF results in mood disorders vary by frequency band. In the narrower range of 0.035 to 0.090 Hz, larger group differences and significant associations with mood symptoms were discovered. Moreover, there was a reversal in the sign of significant mood symptom associations in the two finer-scale frequency bands (0.01-0.045 Hz and 0.035-0.090 Hz). Aligned with these findings, a previous study showed that associations between depressive symptoms and spectral measures in the subgenual gyrus differ between frequency bands 69 . These diverging associations are evidence of the functional specificity of frequency bands in the blood-oxygen level dependent (BOLD) signal 70 – 72 , a phenomena that has largely been attributed to EEG studies of mental health 73 . As such, the notion that different frequency bands reflect physiology occurring at a range of intrinsic time scales 73 – 76 is likely a universal principle of brain function. Under this consideration, the finer bands represented in our work comprise multiple physiological processes that contribute to mood disorder pathology. If too broad a frequency range is adopted, interactive effects 69 can dominate and obscure significant results in a competitive or mutually constructive way 77 . Continued investigations of the functional relevance of specific frequency bands in mood disorders and other psychiatric conditions are needed in fMRI research. Out of the two measures, lfSE performed the best in detecting significant group differences and symptom associations. It was also robust to common sources of variance, which is crucial for mega-analyses, as they often encompass real-world conditions (e.g., use of different scanners, participants with varying medication histories). lfSE may more effectively capture changes in brain dynamics because as a measure of complexity and irregularity 78 , it considers the entire energy distribution across a frequency band. Previous work employing similar methods in other imaging modalities have identified signal irregularity and high signal variance as key features of the psychosis-spectrum 79 – 82 . Our results, alongside broader literature findings, position lfSE—a standard measure in the EEG literature 24 and related modalities 78 , 83 – 85 —as a valuable index for increased application in fMRI. Future research should apply the lfSE metric under different conditions and test its robustness against different covariates to fully assess its mechanistic insights and resilience to non-interest variance sources. In contrast, we observed a change in the significance of two group difference pairs in ALFF analyses with and without medication ( Table 3 ). This inconsistency may indicate that ALFF is more susceptible to medication effects, or more specifically, medication effects in mood and psychosis-spectrum disorders. Future studies should further probe ALFF’s sensitivity to medication in other psychiatric populations to confirm. This study has several strengths. We combined distinct diagnoses into broader groupings (e.g., mood and psychosis-spectrum disorders), which may better reflect the dimensional nature of disorders and allow datasets with different diagnostic criteria to be combined in a mega-analysis. We also used two spectral measures, including lfSE (which is uncommon in fMRI analysis). It also has several limitations. First, participants were assumed to exclusively have mood or a psychosis-spectrum disorder. However, comorbidities are common, and individuals could belong in multiple groupings 86 . Second, we focus on measures that compress brain dynamics into a single number. More complex summaries of dynamic measures 87 may prove to be more sensitive and is a natural next step. Third, although we identify associations with symptoms scores, causality cannot be established. Through a mega-analysis, we investigated group differences using spectral measures of brain dynamics in mood and psychosis-spectrum disorders. Our approach revealed distinct brain signal properties in addition to shared characteristics between the two patient groups. Future studies should continue to elucidate common denominators as well as diagnosis-, symptom-, and individual-specific brain deviations that build on top of disorder commonalities. Better characterization of shared brain features, and disorder specific deviations could pave the way towards plausible prevention and tailored treatment efforts. Data Availability All data produced are available online. https://openneuro.org/datasets/ds000030/versions/00016 https://www.humanconnectome.org/study/human-connectome-project-for-early-psychosis http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html https://bicr-resource.atr.jp/srpbsopen/ Data and Code Availability Data are publicly available through the Center for Biomedical Research Excellence Phase I grant 50 , the Human Connectome Project Early Psychosis 51 , the Strategic Research Program for Brain Sciences 52 and the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study 88 . ( https://openneuro.org/datasets/ds000030/versions/00016 ). Preprocessing scripts are available at https://www.nitrc.org/projects/bioimagesuite . Funding Support This publication was made possible by CTSA Grant Number UL1 TR001863 from the National Center for Advancing Translational Science (NCATS), a component of the National Institutes of Health (NIH). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIH. This work was also supported by NIH grants R01MH126133-03 (to MF), F31AA032179 (to JY), R01MH121095 (to DS and SM), and 5R01DK136623-02 (to DS and MK). Author Contributions and Assistive Grammar Tools Acknowledgement MF had full access to the data and takes on full responsibility for accuracy of the data analysis. MF conceived and designed the study. MF participated in the acquisition, preprocessing, analysis, and interpretation of data. MF and DS participated in drafting the manuscript. MF and DS performed statistical analyses. SM and JY provided critical review of the content and provided clinical interpretations of the analyses. MK participated in quality control of data. JY participated in acquisition and preprocessing of some of the data. Portions of the writing in this manuscript were edited with artificial intelligence (AI)-based tools to improve clarity and grammar. The authors critically reviewed and edited all AI-assisted content to ensure accuracy and originality. No AI tools were used in the generation of scientific content, data analysis, or interpretation. All intellectual content, interpretations, and conclusions are our own. Acknowledgements We are grateful to the participants who dedicated their time and energy to participate in these large-scale data consortiums as well as the study team who collected and collated the data. 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