7 tesla multiscale entropy analysis reveals increased resting-state complexity in key regions for fear and anxiety in spider-fearful individuals.

7 tesla multiscale entropy analysis reveals increased resting-state complexity in key regions for fear and anxiety in spider-fearful individuals - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Neuroimage . Author manuscript; available in PMC: 2026 May 31. Published in final edited form as: Neuroimage. 2025 Jul 13;318:121371. doi: 10.1016/j.neuroimage.2025.121371 7 tesla multiscale entropy analysis reveals increased resting-state complexity in key regions for fear and anxiety in spider-fearful individuals Matthias Grieder a, * , Niklaus Denier a , Kay Jann b , Werner Strik a , Leila M Soravia a , Kristina Adorjan a , Marcel Meyer a , Elisabeth Jehli c Author information Article notes Copyright and License information a Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland b Laboratory of FMRI Technology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA c University Hospital of Zurich, Department of Neurosurgery, Zurich, Switzerland * Corresponding author at: University Hospital of Psychiatry and Psychotherapy, University of Bern, Bolligenstrasse 111, 3000 Bern 60, Switzerland. matthias.grieder@unibe.ch (M. Grieder). Issue date 2025 Sep. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). PMC Copyright notice PMCID: PMC13221786  NIHMSID: NIHMS2170182  PMID: 40664296 The publisher's version of this article is available at Neuroimage Abstract Resting-state functional connectivity is limited in assessing the temporal dynamics of brain networks and, due to an insufficient signal-to-noise ratio, in detecting subtle changes at 3T MRI. Nonetheless, measures of complexity, which capitalize on temporal dynamics, have revealed alterations for some affective disorders at this field strength. Anxiety disorders have received only scant attention in this regard, despite indications of altered functional brain architecture in spider-fearful participants (SP). To address this gap, we probed resting-state complexity using 7T MRI, comparing 28 adults with SP with 45 healthy controls (HC). We computed multi-scale entropy (MSE) on ten scales (1 – 0.1 Hz) for brain regions of the fear and anxiety networks in HC and SP. The MSE scales interacted with group (HC, SP) and brain area, revealing MSE increments in limbic regions in SP (versus HC). Whilst most MSE changes related to SP ranged between 1 and 0.33 Hz, the MSE of the bed nucleus of the stria terminalis (BNST), a nucleus involved in anxiety and the hormonal system, exhibited increases on all scales bar two for SP (i.e., for 0.5 – 0.125, and 0.1 Hz). MSE was also positively associated with SP severity (but not trait anxiety) in the BNST. Altogether, 7T fMRI detected elevated MSE in SP, indicating excessive intra-regional processing in brain regions key to fear and anxiety. The most pronounced effects were found in the BNST, corroborating its central role in the anxiety circuit. Keywords: Multiscale entropy, Complexity, Resting state, FMRI, Spider phobia, 7 Tesla 1. Introduction The human resting state arises from an intrinsic brain activation architecture, ofttimes measured with functional magnetic resonance imaging (fMRI). Resting-state networks (RSNs) denote recurring mental states, which, in turn, emerge from synchronized activity – the correlation of neural activation fluctuations between segregated brain regions over time ( Biswal et al., 1995 ; Raichle et al., 2001 ). RSNs have been linked to multiple primary cognitive functions and mirror overlapping patterns of functional connectivity corresponding to task-related activation topography (e.g., attention, Damoiseaux et al., 2006 ; Jann et al., 2010 ). The introduction of functional connectivity (FC) studies has generated substantial insights into diseased brain networks and consolidated markers for cognitive symptomatology. However, the FC measure yields only one correlative metric for a given scan, thereby failing to characterize the underlying temporal dynamics of complex brain systems. This major shortcoming might account for inconsistent findings across studies ( Calhoun and Adali, 2012 ). Resting-state fMRI complexity may help resolve such discrepancies. An apt metric of the complexity of fMRI signals is sample entropy – a statistical measure quantifying the complexity or irregularity of a timeseries signal. This method samples a limited number of data points with a predefined pattern length ( m ) and threshold ( r ) ( Smith et al., 2014 ; Wang et al., 2014 ). Sample entropy reflects the rate of information generation or processing in complex systems. It aids in differentiating complex signals from random noise, thus improving the understanding of the dynamics in physiological systems and their links to pathology ( Pincus, 1991 ; Yang et al., 2018 ). In the context of functional brain networks, entropy could indicate the transition or exploration between different states of segregated brain networks, resulting in a greater propensity for information processing ( McDonough and Nashiro, 2014 ; Wang et al., 2018 ). When sample entropy is applied to a set of coarse-grained time series by averaging the original time series over a range of time scales, this is termed multiscale entropy (MSE). MSE detects the self-similarity of complex signals across multiple time scales in a random noise environment ( Costa et al., 2002 ). Many complex systems in nature follow a 1/ f power law (frequency [ f ]), with scale-invariant features ( He et al., 2010 ). This also applies to the brain, with data from EEG, intracranial recordings ( Medel et al., 2023 ) and fMRI mapping onto the 1/ f power law ( He, 2011 ). The main advantage of MSE is that it can assess alterations and interactions of neural circuits on spatial and temporal scales. Increasing temporal scales in the context of MSE reflect signal fluctuations at different frequencies, i.e. with increasing scale, the frequency declines with 1/(scale * TR). Accordingly, in contrast to sample entropy, the multiscale dimension of MSE allows capturing the degrees of complexity across different frequencies, in which segregated brain regions communicate locally or distally. For instance, previous work has suggested that higher frequencies mirror aspects of intra-regional information processing, whereas lower frequencies are supposedly linked to inter-regional information transfer ( McDonough and Nashiro, 2014 ; Vakorin et al., 2011 ; Wang et al., 2018 ). MSE analyses of resting-state fMRI signals have recently begun to burgeon in basic and clinical neuroscience, showcasing that brain entropy is a measure of adaptability of a complex system and higher-order cognition, or, owing to its capability in capturing spatiotemporal dynamics, a candidate entity for a “common currency” that integrates neural and mental levels of the brain and the self ( Bassett et al., 2006 ; Northoff et al., 2020 ; Omidvarnia et al., 2021 ). This research illustrates that MSE may add profound insights based on the temporal dynamics over and above the FC components. In some studies, FC and MSE show converging results ( McDonough and Nashiro 2014 ); yet, our work indicates that MSE might be more sensitive than FC ( Grieder et al., 2018 ). Such discrepancies may be explained by the dependence of both FC and MSE on adequate temporal and spatial signal-to-noise ratios (SNR). With advances in processing algorithms, and especially the availability of ultra-high field 7T MRI, images with increased SNR permit detecting more subtle effects ( Dumoulin et al., 2018 ; Pohmann et al., 2016 ). The relative gain in sensitivity and spatial specificity stems from increased tissue-specific functional responses. These responses are related to a shorter venous T 2 * (signal decay time constant), leading to higher extravascular yet lower intravascular signals – compared to 3T imaging ( Uludağ et al., 2009 ). MSE capitalizes on these strengths but has not been explicitly probed with resting-state fMRI at 7T. Addressing this gap, we extracted MSE using 7T MRI. To this end, fMRI data from a sample of spider-fearful participants (SP) and healthy controls (HC) was re-utilized ( Jehli et al., 2024 ). This study found both increased and decreased FC related to SP, albeit less so at the group level. Following on from this, we examined whether MSE at 7T is sufficiently sensitive in detecting group differences without task-induced spider cues – characteristic of processing biases in SP. Complexity analyses are still sparse for psychiatric disorders in the neuroimaging domain. Nonetheless, they hold promise for clinical cohorts – e.g., depression, attention deficit hyperactivity disorder, and schizophrenia – yielding effects on symptom severity or improvement ( Denier et al., 2024 ; Fernández et al., 2013 ; Zhang et al., 2024 ). Thus far, the field of complexity has largely been neglected in anxiety disorders ( Takahashi, 2013 ). Acute threat elicits fear instantaneously, guiding an adaptive response to escape, avoid, sit out, or confront the endangering source. Healthy fear response declines once the threat has disappeared; however, in individuals with a specific phobia, the fear response is maladaptive and characterized by an exaggerated, persistent fear of a phobic object or situation ( LeBeau et al., 2010 ). Fear memories associated with phobic stimuli are also retrieved excessively, notably leading to sustained anxiety, marked by threat anticipation and distress ( de Quervain and Margraf, 2008 ). One common form of specific phobia is spider phobia, typified by an inadequate fear response towards spiders – the phobic object when encountered ( Schweckendiek et al., 2011 ). In such situations, individuals with spider phobia show a hyperactive cerebral fear network, involving regions responsible for fear response and processing ( Leehr et al., 2024 ). Spider phobia is related to hyperactivation in the amygdala, anterior cingulate cortex (ACC), insula, and thalamus and to reduced fear-controlling activation of the prefrontal cortex during confrontation with spider stimuli ( Leehr et al., 2024 ). Predictably, most studies investigating functional brain activity of spider phobia applied a task-based approach, involving confrontation with phobic cues. By contrast, resting-state data on the functional architecture of spider phobia, i.e., data without cue-confrontation, is sparse ( Leehr et al., 2024 ). Notably, spider phobia is an enduring condition, and evidence of the sustained anxiety of individuals with spider phobia (i.e., constant threat anticipation) emphasizes the importance of investigating alterations in both the fear and anxiety networks at rest ( Münsterkötter et al., 2015 ). In addition to the fear response, individuals with spider phobia experience phobic anxiety, that is, a persisting anxious anticipation of a potential threat ( Münsterkötter et al., 2015 ). Given that both fear and anxiety RSNs appear to be altered in spider phobia, it is imperative that this study examines the brain circuits involved in phasic fear as well as sustained anxiety ( Davis et al., 2010 ; Jehli et al., 2024 ). Prior findings of FC or brain activation related to spider phobia are only partially informative for work on resting-state complexity. This is because resting-state complexity measures signal self-similarity and information production rates. These are entities of system complexity and, thus, constitute a different modality from FC or brain activation. In view of the paucity of research on complexity in SP and the probable neural involvement of the fear and anxiety circuits, we focused on assessing region-specific MSE alterations in SP ( Lebow and Chen, 2016 ). Based on a functional view of SP, several fear- and anxiety-related brain regions are likely altered compared to HC. As a control measure, we compared the mean MSE of whole-brain grey matter, where group differences were not expected. Our regions of interest (ROIs) were defined based on research demonstrating their involvement in fear and anxiety networks. The fear response and processing network involves the amygdala, ACC, and insula ( Schweckendiek et al., 2011 ). The fusiform gyrus, parahippocampal gyrus, and thalamus support object recognition and have been shown to interact with the amygdala, making them relevant for phobic fear ( Nakataki et al., 2017 ; Soravia et al., 2016 ). Owing to the crucial role of fear memory in the apprehensive state of individuals with SP ( de Quervain and Margraf, 2008 ), several studies have included the hippocampus in the fear network (for an overview, see Duval et al., 2015 ). Moreover, defensive and fear-controlling processes have been linked with the medial prefrontal (MPFC) and orbitofrontal cortices (OFC, Leehr et al., 2024 ; Schweckendiek et al., 2011 ). Collectively, the fear network comprises regions of the limbic and limbic-related system (amygdala, hippocampus, parahippocampal gyrus, thalamus, insula) along with the prefrontal (MPFC, OFC, ACC) and visual association cortices (fusiform gyrus). Conversely, the anxiety circuit, according to Avery et al. (2016) , includes areas such as the hypothalamus, the bed nucleus of the stria terminalis (BNST), the hippocampus, the amygdala, and the insula. Their meta-analysis revealed that the caudate nucleus might play a role in threat anticipation. Hence, the caudate nucleus, as part of the dorsal striatum, also merits inclusion in the current complexity analysis, not, however, the ventral striatum, which has typically been linked to social anxiety rather than spider phobia ( Boehme et al., 2013 ). Altogether then, the anxiety circuit also includes regions of the limbic and limbic-related system (hypothalamus, the BNST, the hippocampus, the amygdala, and the insula) along with regions of the basal ganglia system (caudate nucleus). In neuroimaging fMRI methodology, subcortical brain structures (e. g., thalamus, BNST, caudate nucleus) are inherently difficult to image, even at 3T, due to their small size and their location proximate to the liquor-filled ventricles. While 7T theoretically improves tSNR for cortical and subcortical areas, there are known effects of increased susceptibility at higher field strengths, specifically in subcortical structures with higher iron contents, which causes faster T2* decay. Additionally, subthalamic areas are located farther from the receive elements of standard head coils and thus underlie g-factor penalty as the sensitivity of individual coils is relatively homogeneous in the center of the brain. Thus, acceleration techniques such as GRAPPA and SENSE, exploiting different receive sensitivities, are not providing the same benefit as for cortical areas. Nevertheless, the increased spatial resolution and potential gain of tSNR at 7T presumably prove advantageous. Corroborating evidence stems from direct comparisons of 3T versus 7T: it has repeatedly been demonstrated that 7T specifically improves the tSNR for subcortical areas, thus supporting enhanced reliability of MSE computation over 3T in those areas ( Colizoli et al., 2020 ; Lucas et al., 2023 ). Recent data further attests to the advantages of 7T for imaging cortical and subcortical regions. Using ultra-high field imaging, we revealed that brain regions of the anxiety circuit, particularly those partly overlapping with the fear network, could also be altered in the functional architecture of SP and, possibly, its complexity ( Jehli et al., 2024 ) – a finding not uncovered in the 3T literature so far. Note that the fear and anxiety circuits have not only been linked to specific phobias but also, at 3T, to depression, generalized and psychotic anxiety, and paranoia in schizophrenia ( Buff et al., 2017 ; Goltermann et al., 2023 ; Strik et al., 2017 ). Research elucidating the complexity of SP is wanting, however, particularly at ultra-high field imaging. This study, therefore, set out to, for the first time, investigate resting-state complexity in SP. Given the novelty of this work, the analysis must be considered exploratory rather than confirmatory. Regional MSE was used as the complexity metric, including mean MSE within each ROI of the fear and anxiety circuit as described above. We were interested in the interaction of the factor group (SP and HC) and the MSE of a set of frequency scales as a function of ROI (for details, see Section 2.4 ). To assess convergent validity, we expected a relationship between MSE and the severity of fear of spiders. Conversely, to verify discriminant validity, we correlated MSE with trait anxiety – here, we did not expect any significant associations. Taken together, this study aimed to introduce resting-state complexity measures to 7T fMRI as well as to anxiety disorders. 2. Methods 2.1. Study participants and procedures The dataset of this study was drawn from Jehli et al. (2024) . Ninety-four adults were screened for eligibility in this observational and open crossover study. Recruitment was accomplished via local newspapers (print and online) and social media advertisements. There were two versions of advertisements for recruitment that reached out either to individuals perceiving themselves as spider-phobic or to healthy adults. For instance, the headline of the advertisement for individuals with SP was «Does the idea of seeing a spider or looking at a picture of a spider trigger panic in you (sweating, shortness of breath, trembling, racing heart, crying, etc.)?» (translated from German). The attention of the healthy control group was drawn to the perceptual aspects of the study more broadly («Magnetic resonance imaging study about perception» .). In an initial phone call, potential participants were assigned to either the SP or HC group according to their own perception of being spider-phobic. To definitively subdivide the participants into groups with SP and without SP, the Fear of Spider Questionnaire (FSQ; Szymanski and O’Donohue, 1995 ) was performed during the testing session to assess SP severity. Inclusion criteria for the study were Swiss German or German mother tongue and an age range between 18 and 50 years. Individuals with a history of neurological or any other psychiatric disorders, current psychoactive drug use or drug abuse, sensory impairments, magnetic objects in or on the body (implants, pacemaker, etc.), claustrophobia, or pregnancy were excluded from the study – note: women underwent a pregnancy test prior to the MRI scan. In accordance with these criteria, 75 participants were included. Informed consent was obtained from all participants for being included in the study. This study received ethical approval from the cantonal ethics committee of Bern (KEK-Nr. 2020–01, 259). Those subjects meeting the inclusion criteria went on to complete a battery of questionnaires to conclude the screening. The Brief Symptom Checklist (BSCL; Franke, 2017 ) was used to assess psychological distress. General trait anxiety was measured using the State-Trait Anxiety Inventory (STAI; Spielberger, 1989 ). The study was also registered in the German registry of clinical trials (Deutsches Register Klinischer Studien, DRKS00023089). 2.2. MRI acquisition and preprocessing MRI data was recorded using a 32-channel head/neck coil (Nova Medical, Wilmington, MA) in a Siemens Magnetom Terra 7T scanner (Siemens Healthcare, Erlangen, Germany) at the Translational Imaging Center, located at the perimeter of the University Hospital Bern in Switzerland. High-resolution T1-weighted anatomical images were collected with an MP2RAGE protocol using the following settings: number of sagittal slices = 256, voxel size = 0.63 × 0.63 × 0.63 mm, field of view = 240 × 240 mm, matrix size = 384 × 384, TR = 6000 ms, TE = 2.06 ms, TI 1 = 800 ms, TI 2 = 2700 ms, flip angle 1 = 4°, flip angle 2 = 5°, TA = 460 s. Resting-state fMRI scans were acquired with a multiband echo-planar protocol comprised of: number of measurements = 360, number of slices = 60, voxel size = 2 × 2 × 2 mm, field of view = 204 × 204 mm, matrix size = 102 × 102, TR = 1000 ms, and TE = 25 ms, TA = 360 s. Image preprocessing was performed using the CONN toolbox ( Nieto-Castanon, 2021 ) and included motion realignment, slice-time correction, coregistration, normalization to MNI space, spatial smoothing with a Gaussian kernel (3 mm full-width half-maximum), high-pass detrending (0.006 Hz), and denoising ( Behzadi et al., 2007 ). Mean framewise displacement (mean FD) was computed to compare the magnitude of change in head position over the resting-state scanning time ( Power et al., 2012 ). 2.3. Subdivision of fear and anxiety brain regions into functional units As outlined in the introduction, the ROIs comprised brain regions of the fear and anxiety networks. In view of the substantial overlap of the fear and anxiety circuits, with either circuit appearing to play a key role in SP (see introduction ), no strict segregation of the two networks was attempted in this study. Furthermore, this study did not assess the specific underlying functions of each ROI. Instead, using the literature as a guide, the convoluted set of ROIs was subdivided into functional units, rendering the findings more structured and meaningful relative to a single ROI pool. The statistics, however, were performed on the ROIs as one set. This is because such functional units add clarity and might be assembled differently. Fig. 1 illustrates these functional units of the fear and anxiety networks. Fig. 1. Open in a new tab Fear and Anxiety network. Conjunct human fear and anxiety network brain regions, subdivided into functional units. ACC, anterior cingulate cortex; Amy, amygdala; BNST, bed nucleus of the stria terminalis; Cau, caudate nucleus; FFG, fusiform gyrus; Hip, hippocampus; Hy, hypothalamus; MPFC, medial prefrontal cortex; OFC, orbitofrontal cortex; PG, parahippocampal gyrus; Tha, thalamus. 2.4. Complexity analysis The preprocessed resting-state fMRI time series of each participant was subjected to the LOFT Complexity Toolbox ( Jann, 2021 ) for voxel-wise MSE computation with a pattern matching threshold of r = 0.2, a pattern length of m = 2, and a time length scale of a = (1,2,3 … 10) with corresponding frequencies (1 Hz, 0.5 Hz, 0.33 Hz, 0.25 Hz, 0.2 Hz, 0.17 Hz, 0.14, 0.125 Hz, 0.111 Hz, 0.1 Hz) ( Li et al., 2018 ; Smith et al., 2014 ; Sokunbi, 2014 ). All images were masked with a mean grey matter mask composed of all subjects. To extract individual ROI-specific mean MSE, 10 of the 12 ROI masks were generated using the automated anatomical labeling (AAL 1) in the WFU Pickatlas tool (v 3.0.5b, Maldjian et al., 2004 , 2003 ; Tzourio-Mazoyer et al., 2002 ). As for the remaining two ROI masks, the BNST mask was adopted from Avery et al. (2014) , and the hypothalamus mask, in turn, was adopted from the Talairach Daemon in the WFU Pickatlas tool ( Lancaster et al., 2000 ). Altogether, the 12 ROIs consisted of bilateral masks of the ACC, amygdala, BNST, caudate nucleus, fusiform gyrus, hippocampus, hypothalamus, insula, MPFC, OFC, parahippocampal gyrus, and thalamus. The mean sample entropy value for each ROI, scale, and participant was extracted using a custom-written MATLAB script (R2022a, Mathworks, Natick, MA). To assess whether the MSE of SP and HC differed between the ROIs and the scales, a mixed 2 × 12 × 10 ANCOVA was conducted with group (HC/SP) as the between-subjects factor and ROI (12 ROIs) and scale (scales 1 – 10) as the within-subjects factors, respectively. Age and sex served as covariates, and Mauchly’s test for sphericity was used to indicate whether the resulting p-values needed Greenhouse-Geisser correction. Significant interactions were disentangled by performing independent t -tests investigating group differences of MSE within each ROI and scale. The MSE analyses were performed using the rstatix package (v 0.7.2) for the ANCOVA and the ggpubr package (v 0.6.0) for the t -tests in R (v 4.4.1). The post-hoc p-values were corrected for multiple testing using false discovery rate (FDR). 2.5. Relationship between complexity and fear To assess whether complexity alterations are linked to trait anxiety or are specific to fear of spiders severity, a non-parametric Spearman correlation was run to correlate FSQ (fear of spiders) and STAI-trait (trait anxiety) scores with the mean MSE values across scales 1 to 10 of each ROI on all subjects from either group (corrplot package v 0.94). The p-values of the 24 correlations were FDR corrected. For clarity purposes, the complete correlation matrices between the MSE of the 12 ROIs and the FSQ and STAItrait were additionally computed for each scale and separated by group (pheatmap package v 1.0.13). 2.6. 7T MSE validation and comparability As the introduction outlines, the higher field strength of 7T fMRI leads to a higher tSNR than 3T. At the same time, the higher magnetic field strength induced a higher susceptibility to physiological artifacts and signal distortions. Small subcortical brain structures, such as the BNST or nuclei with high levels of iron, might add to the difficulty of acquiring a valid signal ( de Hollander et al., 2017 ). Therefore, it is crucial to examine the validity of the fMRI timeseries signal in the ROIs of this study. For this purpose, the voxel-wise (VW) tSNR of the smoothed data were calculated by dividing mean intensity of the 360 datapoints of the resting-state fMRI time series by the standard deviation of the intensity: t S N R V W = M e a n signal intensity S D signal intensity . The tSNR VW was averaged over all voxels within each participant’s ROI. To explore whether regional MSE alterations of SP derived from 7T data are as sensitive as a functional connectivity measure, we performed an intrinsic connectivity (IC) analysis ( Denier et al., 2024 ; Martuzzi et al., 2011 ). IC was defined as the mean square of correlation coefficients ( r ) between each single grey matter voxel ( x ) and all other grey matter voxels ( y ) within the brain ( B ): IC ( x ) = ∫ y B r 2 ( x , y ) d y 1 / 2 This measure includes the number of connections and the connectivity strengths. All IC values of the voxels within each of the 12 ROIs were averaged, and analogous to the MSE ANCOVA, a 2 × 12 ANCOVA with group (HC/SP) as the between-subject factor and ROI (12 ROIs) as the within-subject factor, respectively, was conducted. Age and sex served as covariates, and Mauchly’s test for sphericity was used to indicate whether the resulting p-values needed Greenhouse-Geisser correction. 3. Results 3.1. Demographics and fear metrics The study sample characteristics and statistics are depicted in Table 1 . Datasets of two participants from the SP group were discarded due to widespread and partly exceptionally low MSE values. Supplementary Figure S1 highlights the two outliers relative to the remaining sample. These two subjects showed MSE values lower than those in previous studies with comparable methods ( Smith et al., 2014 ). The group comparisons confirm higher fear of spiders and trait anxiety scores in the SP compared to the HC group. Besides, the sex ratio differed between the groups, with more women and fewer men in the SP than in the HC group. This observation reflects the higher prevalence of specific phobias in women and underpins the necessity to include the sex variable as a covariate in the analyses ( Bourdon et al., 1988 ). With a p-threshold exceeding 0.05, the remaining variables yielded no substantial group differences. There was no group difference in global MSE, thus confirming our assumption. Lastly, comparable mean FD values demonstrated that the MSE group effects were not confounded by subject motion during data acquisition. Table 1. Study sample demographics and statistics. HC ( N = 45) SP ( N = 28) HC-SP Mean (SD) Mean (SD) statistics p Age, years 26.3 (6.0) 27.8 (4.6) 475.0 ( U ) 0.08 Sex, f/m 17/28 24/4 16.1 ( X 2 ) 5.98e-05 Education, years 16.1 (2.8) 15.9 (2.2) 0.37 ( T ) 0.71 FSQ, score 24.8 (9.6) 85.7 (13.9) 2.0 ( U ) 7.81e-13 STAItrait, score 55.5 (8.7) 60.2 (9.4) −2.2 ( T ) 0.03 BSCL, score 10.6 (6.8) 16.0 (14.1) 530.5 ( U ) 0.26 Global MSE 1.64 (0.13) 1.57 (0.27) 513.0 ( U ) 0.18 Mean FD, mm 0.09 (0.03) 0.09 (0.02) 589.0 ( U ) 0.64 Open in a new tab Note: HC: healthy controls, SP: spider-fearful participants, SD: standard deviation, FSQ: Fear of Spiders Questionnaire, BSCL: Brief Symptom Checklist, STAI-trait: State-Trait Anxiety Inventory, FD: Framewise Displacement. p < 0.05 is highlighted in bold. 3.2. Complexity analysis Mauchly’s test for sphericity was significant for all investigated main effects and interactions ( Supplementary Table S1 ). Therefore, only Greenhouse-Geisser corrected statistics are reported. The mean MSE ANCOVA yielded a significant three-way interaction of group × ROI × scale (F 99 = 1.71, p = 1.71e-05, η 2 = 0.024). Besides, there was a two-way interaction of ROI × Scale (F 99 = 7.86, p = 4.22e-99, η 2 = 0.102) and a main effect of scale (F 1.3 = 21.50, p = 1.66e-06, η 2 = 0.238). None of the remaining main effects or interactions were significant ( Supplementary Table S2 for the complete statistics). The covariates age and sex did not alter the findings significantly, as merely sex interacted with ROI × Scale (F 99 = 1.50, p = 0.001, η 2 = 0.021). However, as reported above, this interaction remained significant even after correcting for the covariates. The three-way interaction indicates that MSE between SP and HC, depending on the brain region and the frequency scales, is altered. Figs. 2 – 5 illustrate the MSE for each ROI, scale, and group sorted by the predefined functional units to disentangle the three-way interaction. In the “Sensory processing of threat cue” unit ( Fig. 2 ), increased MSE in SP compared to HC could be observed in the parahippocampal gyrus (scale 1) and the thalamus (scale 2) but not the fusiform gyrus. In the “Fear processing” unit ( Fig. 3 ), higher MSE in SP was found in the amygdala (scale 1) but not the ACC or insula. In the “Contextual / top-down regulation” unit ( Fig. 4 ), increased MSE in SP could be identified in the hippocampus (scales 1 and 2) but not the OFC or the MPFC. Finally, in the “Fear / anxiety monitoring / behavior” unit ( Fig. 5 ), elevated MSE in SP was observed in the BNST (scales 2 – 8, and 10) and the caudate nucleus (scales 1 and 2) but not the hypothalamus. Fig. 2. Open in a new tab Group comparison of scale-wise regional MSE for the “Sensory processing of threat cue” unit. Box and violin plots of each panel illustrate the regional mean MSE stratified by group and scale (1 – 10). Black dots represent group means. For each scale, significant MSE group differences are indicated using asterisks at the top of the plots and p-values of the post-hoc t -tests at the bottom of the plots. Fig. 5. Open in a new tab Group comparison of scale-wise regional MSE for the “fear / anxiety monitoring / behavior” unit. Box and violin plots of each panel illustrate the regional mean MSE stratified by group and scale (1 – 10). Black dots represent group means. For each scale, significant MSE group differences are indicated using asterisks at the top of the plots and p-values of the post-hoc t -tests at the bottom of the plots. Fig. 3. Open in a new tab Group comparison of scale-wise regional MSE for the “Fear processing” unit. Box and violin plots of each panel illustrate the regional mean MSE stratified by group and scale (1 – 10). Black dots represent group means. For each scale, significant MSE group differences are indicated using asterisks at the top of the plots and p-values of the post-hoc t -tests at the bottom of the plots. Fig. 4. Open in a new tab Group comparison of scale-wise regional MSE for the “contextual / top-down regulation” unit. Box and violin plots of each panel illustrate the regional mean MSE stratified by group and scale (1 – 10). Black dots represent group means. For each scale, significant MSE group differences are indicated using asterisks at the top of the plots and p-values of the post-hoc t -tests at the bottom of the plots. 3.3. Relationship between complexity and fear and anxiety The Spearman correlation analyses assessed whether regional complexity was related to spider-phobic fear or trait anxiety. Spider-phobic fear (as indexed by a high FSQ score) was positively associated with MSE in all ROIs, yet only significantly in the BNST. Spearman’s rho values between regional MSE and trait anxiety (i.e., STAI-trait) were lower than for the FSQ, with none reaching significance ( Fig. 6 ). For completeness, in Supplementary Figure S2 , the full correlation matrices between regional MSE and FSQ/STAItrait for each scale and group are illustrated. However, as discussed in the limitations section, this study did not have sufficient statistical power to test for significant group effects in the 480 correlations. Fig. 6. Open in a new tab Relationship between regional MSE and spider phobic fear and trait anxiety. Spearman correlations including all study participants (SP and HC). The significant correlation is marked with an asterisk (p-values: * < 0.05). ACC: anterior cingulate cortex; BNST: bed nucleus of the stria terminalis; FSQ: Fear of Spiders Questionnaire; MPFC: medial prefrontal cortex; OFC: orbitofrontal cortex; STAItrait: State-Trait Anxiety Inventory. 3.4. 7T MSE validation and comparability Supplementary Table S3 lists the tSNR for each ROI. Generally, all ROIs apart from the hypothalamus exceeded a tSNR of 50 and appeared comparable with those of Yoo et al. (2018) , based on the same TR and a similar smoothing Kernel. The hypothalamus exhibited the lowest tSNR amongst the ROIs with 33.63, which is higher than those reported in an earlier 3T study ( Manuel et al., 2023 ). The ROI with the highest expected iron levels, the caudate nucleus, exhibited a tSNR of 69.22. The validation analysis confirmed that the fMRI timeseries used for the MSE analysis showed a sufficiently high tSNR. The ANCOVA investigating regional IC differences between HC and SP yielded main effects of sex (F 69 = 6.767, p = 0.011, η 2 = 0.089) and ROI (F 370.5 = 6.032, p = 1.26e-05, η 2 = 0.080), but not of group, and no interactions whatsoever (see Supplementary Tables S4 and S5 ). Hence, no post-hoc tests were performed to investigate ROI-specific group effects. For the sake of clarity, group-specific IC for each ROI can be viewed in Supplementary Figure S3 . 4. Discussion 4.1. Main findings This proof-of-concept study is the first to introduce the domain of resting-state fMRI complexity to ultra-high field imaging with 7T in SP. Key insights are yielded that complement previous analyses using FC measures by exploring altered complex neural systems, underlying anxiety disorders in particular (e.g., Jehli et al., 2024 ). The current study revealed elevated MSE complexity in SP, compared to HC, in key regions for fear and anxiety. The regional MSE augmentation varied across scales, and this frequency-specific alteration was dissimilar among some of the ROIs. After correcting for multiple testing, the BNST proved to be the only ROI where the MSE in SP was elevated in all scales, except scales 1 and 9. Additionally, lower frequency MSE (scales 4 to 10, except 9) was increased in SP but only in the BNST. Most ROIs with a group effect showed the MSE increase mainly in the lower scales 1 to 3, and thus, the higher frequencies. It should also be mentioned that no MSE difference between SP and HC was present in the fear and anxiety system when all ROIs were concatenated or in the global grey matter MSE. Finally, higher regional MSE was moderately associated with increased spider-phobic fear in most of the ROIs, yet only the BNST correlation was significant after correction for multiple testing. Such associations were much weaker with trait anxiety and were not significant. Taken together, these results might mirror a hypersensitivity in the sensory and fear-processing regions that results in inappropriate information generation of potentially incoming phobic stimuli. Possibly, the BNST plays an important role in linking altered fear processing to increased baseline stress via its connections to hormonal structures ( Lebow and Chen, 2016 ). 4.2. In-depth findings Significant group differences were detected on at least one ROI of each predefined functional unit. This might indicate that the adaptability of the fear and anxiety circuits is elevated across the various stages in threat cue processing, monitoring and regulating. In contrast, most of the 120 group comparisons were insignificant, and 22 group differences did not survive FDR correction. Thus, almost half of the ROIs were not affected by MSE alterations linked to SP, or the statistical power was insufficient to detect such effects. Considering that the data of this study was acquired during the participants at rest, without exposure to any threat cue, the discovery of increased MSE in SP on spatial (i.e., ROI) and temporal dimensions (i.e., scale) merits a thorough inspection – potentially indicative of altered functioning in SP in supposedly benign situations and irrespective of the presence of threat-related material. 4.2.1. Sensory processing of threat cue unit Fig. 2 exemplifies that in SP MSE was elevated on the two highest frequency scales (1 Hz and 0.5 Hz) in the “sensory processing of threat cue” unit. This heightened complexity in the intra-regional processing frequencies might be linked to an altered functional architecture at the early processing stage of threat cues. At this stage, sensory signals are processed and modulated by threat anticipation (parahippocampal gyrus) and the integration of sensory input (thalamus, Maren, 2001 ). This is thus in line with previous evidence showing that personal expectations affect the sensory perception of possible threats in SP (for an overview, see Aue and Okon-Singer, 2015 ). These findings in the sensory unit further demonstrate that the construct validity of the MSE metric might not be limited to higher-order cognition but, as the term implies, also measures more fine-grained alterations in complex neural systems. 4.2.2. Fear processing unit The fear processing unit revealed a single MSE increase for SP, relative to HC, in the amygdala on scale 1 (1 Hz). The amygdala is a key hyperactive region in specific phobia during threat exposure ( Goossens et al., 2007 ; Stein et al., 2007b ). The amygdala is not only pivotal in fear processing, but is also functionally connected to the BNST, which mediates sustained anxiety and plays a role in stress processing ( Avery et al., 2014 ; Kim et al., 2013 ). The connections between the amygdala and BNST and their complex functional relationship might therefore contribute to the altered functional architecture in these fear and anxiety hubs manifested in changed MSE. 4.2.3. Contextual/top-down regulation unit Regarding top-down regulation of the apprehensive state or putting an anticipated threat cue into an anxiolytic context, the hippocampus was the only ROI showing increased MSE within this functional unit. The role of the hippocampus in SP has been attributed to fear learning and modulation, defensive reactivity during a threat, and fear extinction, which might be reflected in these MSE alterations ( Lueken et al., 2014 ; Paquette et al., 2003 ). At the same time, frontal cortex regions were not affected by MSE alterations in the assessed frequency spectrum of our data, covering intra- rather than inter-regional processes. This may explain why we did not find any MSE alterations attributed to ineffective prefrontal down-regulation of excessive fear – they might rely on more distant connections ( Stein et al., 2007a ). Thus, although the hippocampus was assigned to the same functional unit as the MPFC and OFC, the results underscore the distinct regulatory roles of medial temporal and frontal lobe regions. 4.2.4. Fear / anxiety monitoring/ behavior unit The most robust and scale-overarching changes in MSE were found in the BNST (scales 2 – 8 and 10). Specifically, the BNST was the only ROI that showed group differences in frequency scales lower than 3 (0.25 Hz, 0.2 Hz, 0.167 Hz, 0.143 Hz, 0.125 Hz, and 0.1 Hz). By contrast, on scale 1, the highest assessed frequency (1 Hz), no group difference was found in the BNST. Note that scale 1 is a special case of the scales examined in this study, as it reflects the original signal frequency without coarse-graining and thus exhibits the greatest distance from all the remaining scales. The exact mechanism underlying the effect on MSE in the BNST has yet to be investigated. It ought to be noted in that respect that a sufficient understanding of the implications of fine-grained entropy differences between the frequencies is still lacking. Within this functional unit, SP MSE elevations were also detected on scales 2 and 3 in the caudate nucleus, but none in the hypothalamus. Thus, the most substantial MSE increase in SP appears to be linked to threat monitoring and controlling fear and anxiety-related behaviors, given their role in the BNST and the caudate nucleus. The novel finding of the BNST playing a substantial role in SP corroborates results from an earlier study of ours where we implicated the BNST in FC alterations ( Jehli et al., 2024 ). The caudate nucleus has functional and structural connections to the BNST, and they share a threat-monitoring function ( Avery et al., 2014 ; Mobbs et al., 2010 ) – as a subregion of the striatum, it is also involved in threat anticipation ( Drabant et al., 2011 ). 4.2.5. Correlations with fear and anxiety In line with the regional MSE alterations in SP, we found a positive association between MSE and spider-phobic fear, but not between MSE and trait anxiety. This fear-related association was only significant in the BNST after FDR correction. One should nonetheless consider that correlations were computed using fear and anxiety scores with mean MSE values over all scales and across the total study sample. This approach might have contributed to comparably small effect sizes (i.e. moderate rho values). Principally, this was due to including scales, without group difference effects or with negative rho values – i.e., they were inversely linked to SP or rendered irrelevant. In our view, the stronger relationship between MSE and spider-phobic fear severity relative to trait anxiety underlines the pertinence of MSE alterations to our understanding of SP. 4.3. Synthesis The increased MSE in brain regions of the fear and anxiety circuitry (e.g., amygdala, hippocampus, and parahippocampal gyrus) is in line with the notion of hyperactivity in these regions in SP (see Soravia et al., 2016 ). Indeed, the elevated complexity found in this study might reflect an inherent hypersensitivity to adapt the fear and anxiety system to potentially threatening situations that are, objectively, not that endangering. This hypersensitivity or reactivity in the context of MSE increments paired with anxiety as a quasi-stable mental condition can be seen as a hyperresponsive state, heightening alertness and response to fearful stimuli. This idea is supported by animal research showing that complexity of local field potentials and of the BOLD signal decreases significantly when altering the excitation/inhibition (E/I) balance ( Wang et al., 2018 ). Critically, increases in the E/I balance will likely manifest as increases in complexity, as observed in this study. The assumed apprehensive state in SP is termed paranoia in schizophrenia spectrum disorders, and has been related to increased FC between the amygdala and hippocampus in prior work ( Walther et al., 2022 ); both of these regions exhibited increased MSE in SP in this study. These MSE outcomes tie in with results from Jehli et al. (2024) in virtually the same sample, showing increased FC in the amygdala in SP with elevated phobic fear. Interestingly, this study also yielded changes in FC in the BNST, specifically in association with general anxiety and social anxiety. It seems that the consistent occurrence of BNST alterations in both MSE and FC is linked to the higher spatial specificity of 7T MRI compared to 3T. Notably, the FC results were based on correlations with clinical scales, but neither FC nor IC showed any significant group differences. This null effect of group confirms our assumption that the MSE measure is more sensitive to subtle neural changes than FC or IC metrics. Other research on MSE alterations in mental disorders has primarily found reductions in entropy. The comparison of the diseases examined in those works with SP appears delicate, as they were either degenerative ( Grieder et al., 2018 ; Jann et al., 2023 ) or in younger cohorts with a different symptomatology ( Zhang et al., 2024 ). Increased brain entropy has, in turn, been found in cohorts with obsessive-compulsive disorder (OCD, Jiang et al., 2021 ) and generalized anxiety disorder ( Fan et al., 2023 ). It is thus possible that elevated brain entropy is found in disorders characterized by interpersonal sensitivity and obsessive doubts, features common to both SP and OCD ( Carpita et al., 2020 ). Taken together, our finding of SP-related MSE augmentation across several frequencies and brain regions adds critical insight into the temporal dynamics of neural processing complexity in mental disorders. 4.4. Limitations A limitation of the present study is the unequal sex distribution between SP and HC. Since sex differences may influence fear-related neural processing, this imbalance could have introduced a confounding effect. To mitigate this, we statistically controlled for sex in our group-level analyses. Nevertheless, future studies should aim to recruit sex-matched groups to disentangle sex-specific contributions to fear processing further. The limited frequency range covered by the scanning protocol may constitute another limitation of this study. With a total acquisition time of six minutes and a TR of 1000 ms, the lowest reliable frequency included in the analyses was 0.1 Hz (with 36 data points), which is considered the lower limit of the high-frequency range ( Wang et al., 2018 ). Our study, therefore, comprised only high but not low frequencies; longer acquisition times are needed to address this issue. However, one could hold that frequencies between 0.001 and 0.1 Hz are already covered by most studies assessing FC metrics. The higher frequencies, in turn, have received comparatively less attention, although they are also linked to FC ( Cordes et al., 2001 ; Smith et al., 2013 ). Inspecting Figs. 2 – 5 , MSE group differences across ROIs and scales appear relatively small. Similarly, Spearman’s rho coefficients as a measure of effect size of the correlations between regional MSE and FSQ are small to moderate. A study design including 22 factorial steps concerning MSE alone inherently leads to a high number of statistical tests, requiring some form of correction for multiple testing. On the one hand, one might hold that this study had a power problem (i.e., insufficient sample size), as numerous effects did not overcome FDR correction. On the other hand, this study relied on precise and sensitive measures of the neural architecture (7T and MSE), providing sufficient power to identify novel findings – often unmatched in other research ( Nebe et al., 2023 ). For example, with tSNR values ranging between 33 and 75 %, several group differences were detected for MSE but not IC. The fact that the correlational effect between MSE in the BNST and FSQ was computed across the entire study sample may constitute another limitation in that it might have been driven by the group differences in FSQ. Finally, the numerous inferences made regarding the specific functional roles of the included ROIs in this study could be perceived as yet another limitation. These functional roles were drawn from other studies and cannot be verified with the current dataset. Nonetheless, the current study revealed a correlational link between MSE and phobic fear severity, thereby confirming the functional relevance of these regions for SP. Task-based approaches may need to establish causal relationships between entropy measures and cognitive functions of segregated brain regions, although metrics other than MSE might be more suitable in this context ( Wehrheim et al., 2024 ). 4.5. Conclusions As demonstrated by the link between MSE and spider-phobic fear uncovered in this study, increased MSE in the fear and anxiety circuits is related to elementary hypervigilance in individuals affected by SP. MSE is a promising measure, permitting the detection of comparably small yet robust differences when combined with the higher temporal and spatial SNR of 7T fMRI, as this study advocates. Using MSE as a complementary measure of conventional FC measures, a more refined picture of the dynamics of complex neural systems can be achieved. MSE has been linked to higher-order cognition ( Omidvarnia et al., 2021 ). Yet, the current findings underline the potential role of MSE in integrating sensory processes, subserving such higher-order cognitive functions. This suggestion is supported by the increases in MSE in the fusiform gyrus, the parahippocampal gyrus, and the thalamus in individuals with SP. Whereas the fusiform gyrus responds to visual cue configurations, thalamic processes are linked to arousal, fear, and anxiety responses ( Del Casale et al., 2012 ; Duval et al., 2015 ). These processes, in turn, may provide cognitive correlates of our MSE effects in these regions and merit closer examination in dedicated future studies. This is the first study examining MSE in 7T fMRI, capitalizing on the improved image resolution at this field strength. Nonetheless, we cannot quantify its expected superior sensitivity relative to lower magnetic field MRI, as comparable data with 3T does not exist. However, in a previous 3T study of ours on mild Alzheimer’s disease, we only detected MSE-linked group differences on two scales and in one ROI ( Grieder et al. 2018 ). This is in stark contrast to the current study with 7T, demonstrating a larger amount of MSE differences across a multitude of scales and regions. Albeit this difference in effects may be disorder-specific, it could also be seen as a clear indication of the capability of 7T to detect more subtle changes in MSE. To conclude, this study emphasizes the potential of 7T complexity in illuminating and expanding our understanding of symptom-linked alterations in the neural architecture of anxiety disorders. Supplementary Material 1 NIHMS2170182-supplement-1.docx (396.4KB, docx) Supplementary material associated with this article can be found, in the online version, at doi: 10.1016/j.neuroimage.2025.121371 . Acknowledgement The University Hospital for Psychiatry and Psychotherapy Bern, Switzerland, supported this study. The development of the LOFT complexity toolbox was supported by funding from NIA (grant no R01AG066711). We thank the following contributors: Andrea Federspiel and Piotr Radojewski of the Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland, for technical and clinical support at the MRI scanner site; Dilmini Wijesinghe of the Laboratory of FMRI Technology, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Keck School of Medicine, Los Angeles, California, USA, for providing valuable insight into fMRI complexity developments; Thomas Dierks of the Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland, for conceptual advice. Footnotes CRediT authorship contribution statement Matthias Grieder: Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Niklaus Denier: Writing – review & editing, Formal analysis. Kay Jann: Writing – review & editing, Software, Methodology, Funding acquisition, Formal analysis. Werner Strik: Writing – review & editing, Resources, Funding acquisition, Conceptualization. Leila M. Soravia: Writing – review & editing, Conceptualization. Kristina Adorjan: Writing – review & editing, Resources, Funding acquisition. Marcel Meyer: Writing – review & editing, Writing – original draft. Elisabeth Jehli: Writing – review & editing, Investigation, Formal analysis, Data curation. Declaration of competing interest The authors declare no competing interests. Data and code availability Codes used for data analysis are made available upon request to the corresponding author. The data are not publicly available due to privacy or ethical restrictions, but will be provided upon request to the corresponding author. Data availability Data will be made available on request. References Aue T, Okon-Singer H, 2015. 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Supplementary Materials 1 NIHMS2170182-supplement-1.docx (396.4KB, docx) Data Availability Statement Codes used for data analysis are made available upon request to the corresponding author. The data are not publicly available due to privacy or ethical restrictions, but will be provided upon request to the corresponding author. Data will be made available on request. ACTIONS View on publisher site PDF (2.0 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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