Properties (16)
Id
e94ddedd-23f6-48b3-8aa0-accc0120375b
Year
2026
Abstract
ABSTRACT
Misophonia, a disorder characterized by extreme aversion to certain sounds, affects 5%–20% of the general population, yet mechanisms are still largely unknown. Recent neuroimaging studies have reported abnormal functional connectivity of the anterior insula to various limbic, salience, and motor regions in smaller samples of misophonic individuals versus controls, suggesting potential differences in underlying attentional or emotional processes. These findings prompt questions about the insular connectivity profile in larger samples of adults, what patterns emerge when the samples span a wider range of misophonia severity, and how these patterns may or may not overlap with other co‐occurring disorders. To address these questions, we analyzed resting‐state functional magnetic resonance imaging data from the open Welsh Advanced Neuroimaging Database ( N = 162) comprising participants recruited from the general adult population and assessed for sensory sensitivity, anxiety, depression, and autistic traits. A misophonia severity score was derived from the sensory sensitivity data using a model trained on a second adult self‐report sample from Oklahoma ( N = 777). Using anterior insula as a seed for a whole‐brain seed‐to‐voxel connectivity analysis, the derived misophonia severity scores were found to be significantly related to connectivity from the insula to clusters overlapping the planum temporale, operculum, precentral gyrus, and supplementary motor area. Notably, this insular connectivity profile was unique to the anterior insula of the salience network and was not observed when dividing the sample into misophonia (patient) versus control groups, or when grouping participants as a function of anxiety, depression, or autistic traits. These results underline the importance of the salience‐network anterior insula in understanding misophonic aversion and provide tentative evidence of neurological differences between misophonia and anxiety, depression, and autism. This work aids in our understanding of neural mechanisms of misophonia and emphasizes the benefit of treating misophonia as a continuous spectrum disorder to better reflect the variability of symptoms in the real world.
Cssclasses
- purpleRed
Aliases
- Selective Disruption of Salience‐Network Anterior Insula Connectivity in Misophonia
- (Hansen et al., 2026)
Import Date
02-28-2026
Template Version Notes
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Checked For Zettle Notes
true
Zotero Note Link Generated
true
Dv Authors
Heather A. Hansen, Jordan E. Norris, Catherine M. Bain, Lauren E. Ethridge, Christine L. Tardif,
Dv Initial Source
Misophonia MOCMisophonia
Dv Status
#status/reviewed
Dv Limitations
• Misophonia not directly assessed in WAND; scores predicted via regression.
• General population sample; not clinically diagnosed misophonia.
• No assessment of audiological conditions (e.g., tinnitus, hyperacusis).
• Resting-state only; no trigger stimuli presented.
• Reliance on MQ; lack of consensus assessment scale.
Dv Future Directions
• Recruit clinically severe misophonia samples with rigorous audiological screening.
• Use task-based fMRI with trigger stimuli.
• Examine internal imagination of triggering scenarios.
• Replicate findings with updated and multiple misophonia assessment scales.
• Incorporate continuous analytic approaches.
Dv Statistics
• Oklahoma model: r=0.55; adjusted R²=0.326; sensitivity=0.48; specificity=0.90; AUC=0.796; PPV=0.74; NPV=0.75; χ²(1)=8.31, p=0.004; permutation p<0.001. • Significant predictors (Bonferroni p<1.7×10⁻³): relocate with strong odors β=0.140, SE=0.038; limit distractions β=−0.130, SE=0.039; distracted by noise β=0.145, SE=0.045. • WAND predicted MQ: range 8.9–45.1; mean=24.2; 19% ≥30. • HADS anxiety: M=5.27, SD=3.67; 13% clinical; 12% borderline. • HADS depression: M=2.67, SD=2.72; 2% clinical; 4% borderline. • AQ-S: M=58.07, SD=10.10; 19% above cutoff. • Oklahoma diagnoses: anxiety 23%; depression 20%; autism 0.6%. • Point-biserial correlations (Oklahoma): anxiety rpb=0.15; depression rpb=0.13; autism rpb=0.16; all p<0.001. • Connectivity thresholding: voxel p<0.01; cluster p-FWE<0.025 (one-tailed).
Dv Relevance
• paper by heather Hansen looking at changes in salience network connectivity as a biomarker of Misophonia • sought to study Misophonia etiology thorough its neural profile and associations with other disorders • authors operationalized Misophonia as a spectrum disorder to better capture the variability of real world symptoms. • the study demonstrates the feasibility of using large open-access neuroimaging databases to study Misophonia. specifically they showed it is possible to extrapolate Misophonia status by training a model with a separate sample on a feature included in the datasets (see notes) • finds evidence for non orofacial (x finger) and nonmotor explanations for Misophonia (the way they emphasize this feels like a jab at Kumar )
•
Selective Disruption of Salience‐Network Anterior Insula Connectivity in Misophonia: A Disorder‐Specific Neural Signature
Status:: #status/reviewed
Metadata
- Item Type: #journalArticle
- Authors:: Heather A. Hansen, Jordan E. Norris, Catherine M. Bain, Lauren E. Ethridge, Christine L. Tardif,
- Date:
- Date Added: 1771296507000
- URL: https://onlinelibrary.wiley.com/doi/10.1002/hbm.70468
- DOI: 10.1002/hbm.70468
- Cite Key: hansenSelectiveDisruptionSalienceNetwork2026
- Topics: [object Object]
- Tags:: #Misophonia, #anxiety, #depression, #insula, #resting-state-connectivity, #salience-network, #seed-based-connectivity, #Fmri
- PDF Attachments
Abstract
| Background/Purpose | Background- • Misophonia affects 5%–20% of the population; neural mechanisms remain largely unknown. • Prior neuroimaging studies implicate anterior insula activation and connectivity differences in small samples. • Anterior insula connectivity alterations also reported in anxiety, depression, and autism. • Behavioral co-occurrence findings with anxiety, depression, and autism are mixed. • Misophonia symptoms proposed to lie on a spectrum. Purpose- • sought to study Misophonia etiology thorough its neural profile and associations with other disorders (page 2) • Ascertain anterior insula connectivity profile of misophonia and its relationship to symptom severity. • Determine extent of overlap between misophonia connectivity profile and anxiety, depression, and autism. • Examine misophonia as a continuous spectrum rather than binary grouping. • |
| Participants/samples | • WAND dataset: N=162 adults; 153 included in connectivity analyses after quality control; general UK population. • Oklahoma dataset: N=777 adults; used to train misophonia prediction model. • WAND: mean age ~30; mixed sex distribution. • Oklahoma: mean age ~19; predominantly university sample. |
| Measures | Misophonia • MQ (Oklahoma). • Predicted MQ total score in WAND derived via regression from 30 AASP sensation avoiding/sensitivity items. Sensory sensitivity- AASP (both samples). Anxiety/Depression- Hospital Anxiety and Depression Scale (HADS) (WAND). Autism- Autism-Spectrum Quotient Short (AQ-S) (WAND). Neuroimaging- Resting-state 3T fMRI (10 min 38 s); bilateral anterior insula seeds; CONN toolbox; seed-to-voxel connectivity. |
| Procedures | Modeling- Linear regression trained in Oklahoma sample to predict MQ total from 30 AASP items; validated in held-out test set; applied to WAND AASP data. Connectivity analyses- • Seed-to-voxel analyses from bilateral anterior insula. • Continuous misophonia severity and binary group models tested. • Additional analyses with Schaefer 100 atlas insular subdivisions. • Separate and combined models including anxiety, depression, autism, age, and sex as covariates. |
| Results | • results suggest a relationship between Misophonia symptom severity and anterior insula connectivity strength (specifically in relation to salience network). • results suggest an independence of the insular connectivity profile of Misophonia from anxiety depression, or autistic traits. (pdf) Model validation • Predicted MQ scores correlated with actual MQ scores in Oklahoma test set. • Model classification outperformed null model. • Three AASP items significantly predicted MQ total. Misophonia severity (continuous) • Greater misophonia severity associated with stronger anterior insula connectivity to clusters overlapping planum temporale, operculum, precentral gyrus, supplementary motor area, superior frontal gyrus, posterior cingulate gyrus, and temporal pole. • No negative associations observed. Binary grouping : No significant clusters when using misophonia patient vs control grouping at primary thresholds. Insular specificity • Significant associations observed for salience/ventral attention insular subdivisions. • No significant associations for default mode or somatomotor insular subdivisions. • Both left and right anterior insula contributed; bilateral pattern resembled combined model. Behavioral associations • No significant correlations between predicted misophonia severity and anxiety, depression, or autistic traits in WAND. • Small positive associations between MQ total and binary anxiety, depression, and autism diagnoses in Oklahoma. Connectivity comparisons • Anxiety and depression each showed distinct anterior insula connectivity patterns not overlapping misophonia clusters. • No significant anterior insula connectivity as a function of autistic traits. • Misophonia-related clusters remained significant after controlling for anxiety, depression, autism, age, and sex. |
| Discussion/Limits | Conclusion- • The authors conclude that misophonia symptom severity is associated with increased anterior insula connectivity strength. • The findings indicate that misophonia-related connectivity is specific to anterior insula subdivisions affiliated with the salience network. • The authors conclude that the misophonia connectivity profile overlaps somatomotor, auditory (planum temporale), and higher-order cognitive regions. • The findings indicate that misophonia involves abnormal integration of stimulus salience with higher-order auditory and cognitive processes rather than simple acoustic sensitivity. • The authors conclude that misophonia’s insular connectivity profile is neurally distinct from anxiety, depression, and autism. • The data support conceptualizing misophonia as a spectrum disorder rather than a binary condition. • The authors conclude that open-access datasets can be leveraged to study misophonia via indirect modeling approaches. |
| Limitations:: | • Misophonia not directly assessed in WAND; scores predicted via regression. • General population sample; not clinically diagnosed misophonia. • No assessment of audiological conditions (e.g., tinnitus, hyperacusis). • Resting-state only; no trigger stimuli presented. • Reliance on MQ; lack of consensus assessment scale. |
| Future Directions:: | • Recruit clinically severe misophonia samples with rigorous audiological screening. • Use task-based fMRI with trigger stimuli. • Examine internal imagination of triggering scenarios. • Replicate findings with updated and multiple misophonia assessment scales. • Incorporate continuous analytic approaches. |
| Statistics:: | • Oklahoma model: r=0.55; adjusted R²=0.326; sensitivity=0.48; specificity=0.90; AUC=0.796; PPV=0.74; NPV=0.75; χ²(1)=8.31, p=0.004; permutation p<0.001. • Significant predictors (Bonferroni p<1.7×10⁻³): relocate with strong odors β=0.140, SE=0.038; limit distractions β=−0.130, SE=0.039; distracted by noise β=0.145, SE=0.045. • WAND predicted MQ: range 8.9–45.1; mean=24.2; 19% ≥30. • HADS anxiety: M=5.27, SD=3.67; 13% clinical; 12% borderline. • HADS depression: M=2.67, SD=2.72; 2% clinical; 4% borderline. • AQ-S: M=58.07, SD=10.10; 19% above cutoff. • Oklahoma diagnoses: anxiety 23%; depression 20%; autism 0.6%. • Point-biserial correlations (Oklahoma): anxiety rpb=0.15; depression rpb=0.13; autism rpb=0.16; all p<0.001. • Connectivity thresholding: voxel p<0.01; cluster p-FWE<0.025 (one-tailed). |
| Relevance:: | • paper by heather Hansen looking at changes in salience network connectivity as a biomarker of Misophonia • sought to study Misophonia etiology thorough its neural profile and associations with other disorders • authors operationalized Misophonia as a spectrum disorder to better capture the variability of real world symptoms. • the study demonstrates the feasibility of using large open-access neuroimaging databases to study Misophonia. specifically they showed it is possible to extrapolate Misophonia status by training a model with a separate sample on a feature included in the datasets (see notes) • finds evidence for non orofacial (x finger) and nonmotor explanations for Misophonia (the way they emphasize this feels like a jab at Kumar ) |
Notes
key terms and concepts:
-
Misophonia – decreased tolerance to specific sounds (“triggers”) resulting in atypically strong negative emotional, physiological, and behavioral responses.
-
Anterior insula – cortical region implicated in processing of disgust, subjective evaluation of pain, goal-directed attentional control, and interoception.
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Salience network – network involved in determining salience and attending to stimuli.
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Seed-based connectivity – functional connectivity approach estimating correlations between a predefined seed region and all other brain voxels.
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Adolescent/Adult Sensory Profile (AASP) – 60-item sensory questionnaire with subscales of sensation avoiding, sensory sensitivity, sensation seeking, and low registration.
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Misophonia Questionnaire (MQ) – self-report measure including Symptoms, Emotions/Behaviors, and Severity scales assessing misophonia.
my engagement notes
my questions
my comments
Bookmarks
- bookmark talking about the significance of the anterior insular cortex (page 2)
- bookmark for talking about Misophonia Symptom Severity Is Associated With Anterior Insula Connectivity Strength, Specifically in Affiliation With the Salience Network (page 11)
reading notes
leveraging two datasets
- The study leveraged two independent datasets.
- The Oklahoma dataset (N=777) consisted primarily of university students and community adults who completed the Misophonia Questionnaire (MQ), the Adolescent/Adult Sensory Profile (AASP), and demographic items via online survey; these data were used to train and validate a regression model predicting MQ total scores from sensory sensitivity items.
- The WAND dataset (Welsh Advanced Neuroimaging Database; N=162, 153 included after quality control) comprised adults from the general UK population who completed resting-state 3T fMRI and multiple trait questionnaires (including AASP, HADS for anxiety/depression, and AQ-S for autistic traits), but did not include a direct misophonia measure, necessitating model-based estimation of misophonia severity. (page 2)
