ArticleNetwork neuroscience (Cambridge, Mass.)2019
Dissociating tinnitus patients from healthy controls using resting-state cyclicity analysis and clustering.
Article in Network neuroscience (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Cluster-based psychological phenotyping and differences in anxiety treatment outcomes.Scientific reports · 2023Trial
- Article
- A large-scale, multi-site examination of resting state functional connectivity in tinnitus.Scientific reports · 2025Article
- Amygdala structural and functional reorganization as an indicator of affective dysfunction in patients with tinnitus.Human brain mapping · 2024Article
- Hemodynamic cortical ripples through cyclicity analysis.Network neuroscience (Cambridge, Mass.) · 2024Article
- Association between tinnitus and hearing impairment among older adults with age-related hearing loss: a multi-center cross-sectional study.Frontiers in neurology · 2024Article
- Signature methods for brain-computer interfaces.Scientific reports · 2023Article
- Comparing Clustering Methods Applied to Tinnitus within a Bootstrapped and Diagnostic-Driven Semi-Supervised Framework.Brain sciences · 2023Article
- An examination of the reliability of seed-to-seed resting state functional connectivity in tinnitus patients.Neuroimage. Reports · 2023Article
- Resting-state Networks in Tinnitus : A Scoping Review.Clinical neuroradiology · 2022Article
- Modulation of brain networks during MR-compatible transcranial direct current stimulation.NeuroImage · 2022Article
- Ultra-High Frequency Distortion Product Otoacoustic Emissions for Detection of Hearing Loss and Tinnitus.International journal of environmental research and public health · 2022Article
- Decreased resting perfusion in precuneus and posterior cingulate cortex predicts tinnitus severity.Current research in neurobiology · 2021Article
- The Neural Mechanisms of Tinnitus: A Perspective From Functional Magnetic Resonance Imaging.Frontiers in neuroscience · 2021Review
- Cerebral Blood Flow Difference Between Acute and Chronic Tinnitus Perception: A Perfusion Functional Magnetic Resonance Imaging Study.Frontiers in neuroscience · 2021Article
- Exposing Pathological Sensory Predictions in Tinnitus Using Auditory Intensity Deviant Evoked Responses.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Functional Brain Changes During Mindfulness-Based Cognitive Therapy Associated With Tinnitus Severity.Frontiers in neuroscience · 2019Article
- Comparing Cyclicity Analysis With Pre-established Functional Connectivity Methods to Identify Individuals and Subject Groups Using Resting State fMRI.Frontiers in computational neuroscience · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic tinnitus is a common and sometimes debilitating condition that lacks scientific consensus on physiological models of how the condition arises as well as any known cure. In this study, we applied a novel cyclicity analysis, which studies patterns of leader-follower relationships between two signals, to resting-state functional magnetic resonance imaging (rs-fMRI) data of brain regions acquired from subjects with and without tinnitus. Using the output from the cyclicity analysis, we were able to differentiate between these two groups with 58-67% accuracy by using a partial least squares discriminant analysis. Stability testing yielded a 70% classification accuracy for identifying individual subjects' data across sessions 1 week apart. Additional analysis revealed that the pairs of brain regions that contributed most to the dissociation between tinnitus and controls were those connected to the amygdala. In the controls, there were consistent temporal patterns across frontal, parietal, and limbic regions and amygdalar activity, whereas in tinnitus subjects, this pattern was much more variable. Our findings demonstrate a proof-of-principle for the use of cyclicity analysis of rs-fMRI data to better understand functional brain connectivity and to use it as a tool for the differentiation of patients and controls who may differ on specific traits.
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