Individualized covariance profile of cortical morphology for auditory hallucinations in first‐episode psychosis

Yun, Je Yeon; Kim, Sung Nyun; Lee, Tae Young; Chon, Myong-Wuk; Kwon, Jun Soo

doi:10.1002/hbm.23083

Cited by 29 publications

(19 citation statements)

References 63 publications

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“…The resulting residuals were then z-score transformed using mean and SD values of each region of interest calculated from healthy controls (to derive the degrees of brain morphological variations per region of interest relative to the 'average healthy controls' values). Finally, a measure of joint variation (which is not the same as the classical statistical definition of covariance) between the 72 morphometric features (33 cortical surface area values, 33 cortical thickness values, and six subcortical values) represented the edge-weights (distributed between 0 and 1) of the network and was calculated using the following formula (Yun et al, 2015(Yun et al, , 2016:…”

Section: Intra-individual Cortical-subcortical Structural Covariance mentioning

confidence: 99%

“…Brain structural covariance networks reflect intra-individual (Yun et al, 2016;Seidlitz et al, 2018a) or inter-individual (Alexander-Bloch et al, 2013;Kaczkurkin et al, 2019;Wannan et al, 2019) covariation in morphology of different brain areas, which may in turn point to common trajectories in brain development and maturation (Yun et al, 2015(Yun et al, , 2016Hunt et al, 2016). Such networks may focus on a range of morphological features including regional brain volume (Spreng et al, 2019), cortical thickness (Solé-Casals et al, 2019), cortical surface area (Sharda et al, 2017), and cortical white-grey contrast (Makowski et al, 2019), as well as the paired or conjoint patterns between different brain regions (Seidlitz et al, 2018b;Hoagey et al, 2019) Brain structural covariance has been estimated using Pearson's correlation coefficient (Seidlitz et al, 2018a;Solé-Casals et al, 2019;Wannan et al, 2019), partial least squares (Hoagey et al, 2019;Spreng et al, 2019), non-negative matrix factorization (Kaczkurkin et al, 2019), and inverse exponential of the difference between z-score transformed brain morphological values (Wee et al, 2013;Yun et al, 2015Yun et al, , 2016, among others. Structural covariance networks are more similar to patterns of functional connectivity than the architecture of white matter connections, suggesting that areas that co-vary in morphological characteristics also belong to the same functional network (Zielinski et al, 2010;Soriano-Mas et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Here we constructed intra-individual structural covariance networks from region of interest-based brain morphological features using 37 datasets worldwide (n = 1616 for OCD; n = 1463 for healthy controls), and investigated network topology using a graph theory approach. The current study aimed to capture the intra-individual distribution of brain morphological changes (Wee et al, 2013;Yun et al, 2015Yun et al, , 2016 in OCD across 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes (Kremen et al, 2013;Amlien et al, 2016;Sussman et al, 2016;Vijayakumar et al, 2016;Krongold et al, 2017;Schmaal et al, 2017). Thus edge weights of the intra-individual structural covariance networks were estimated in proportion to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e.…”

Section: Introductionmentioning

confidence: 99%

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OUP accepted manuscript

Yun¹,

Boedhoe²,

Vriend³

et al. 2020

Brain

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Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T 1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach.

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Section: Intra-individual Cortical-subcortical Structural Covariance mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

OUP accepted manuscript

Yun¹,

Boedhoe²,

Vriend³

et al. 2020

Brain

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show abstract

“…28 studies employed a regional and/or non-voxelwise approach to evaluate structural MRI data with respect to hallucination status: seven studies performed VBM restricted to predefined ROIs 43,61,69-73 , one performed sourcebased morphometry 74 , nine explored cortical thickness (CT) and/or surface area [75][76][77][78][79][80][81][82][83] , three investigated gyral/sulcal properties 43,84,85 , and 11 assessed structure-specific shape parameters 43,81,83,[86][87][88][89][90][91][92][93] . Results are summarized in Tables 5-6.…”

Section: Resultsmentioning

confidence: 99%

Meta-analytic evidence for the plurality of mechanisms in transdiagnostic structural MRI studies of hallucination status

Rollins

Garrison

Simons

et al. 2018

Preprint

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BACKGROUND:Hallucinations are transmodal and transdiagnostic phenomena, occurring across sensory modalities and presenting in psychiatric, neurodegenerative, neurological, and non-clinical populations. Despite their cross-category occurrence, little empirical work has directly compared between-group neural correlates of hallucinations. METHODS:We performed whole-brain voxelwise meta-analyses of hallucination status across diagnoses using AES-SDM, and conducted a comprehensive systematic review in PubMed and Web of Science until May 2018 on other structural correlates of hallucinations, including cortical thickness and gyrification.FINDINGS: 3214 abstracts were identified. Patients with psychiatric disorders and hallucinations (eight studies) exhibited reduced gray matter (GM) in the left insula, right inferior frontal gyrus, left anterior cingulate/paracingulate gyrus, left middle temporal gyrus, and increased in the bilateral fusiform gyrus, while patients with neurodegenerative disorders with hallucinations (eight studies) showed GM decreases in the left lingual gyrus, right supramarginal gyrus/parietal operculum, left parahippocampal gyrus, left fusiform gyrus, right thalamus, and right lateral occipital gyrus. Group differences between meta-analyses were formally confirmed and a jackknife sensitivity analysis established the reproducibility of results across nearly all study combinations. For other measures (28 studies), the most consistent findings associated with hallucination status were reduced cortical thickness in temporal gyri in schizophrenia and altered hippocampal volume in Parkinson's disease and dementia. INTERPRETATION: Distinct patterns of neuroanatomical alteration characterize hallucination status in patientswith psychiatric and neurodegenerative diseases, suggesting a plurality of anatomical signatures. This approach has implications for treatment, theoretical frameworks, and generates refutable predictions for hallucinations in other diseases and their occurrence within the general population. FUNDING: None. Research in context Evidence before this studyThere is increasing recognition that hallucinations occur beyond the archetype of schizophrenia, presenting in other psychiatric disorders, neurological and neurodegenerative conditions, and among the general population. Not only are hallucinations a transdiagnostic phenomenon, but also the experience of hallucinating is phenomenologically diverse, varying in modality, content, frequency, and affect. It has been suggested that no one type of hallucination is pathognomic to any one disorder, but rather that hallucinations may exist on a continuum. However, limited research has been done to directly compare the underlying neuroanatomy of hallucinations between different disorders. With this aim, we conducted a meta-analysis and systematic review of structural MRI studies comparing individuals who experience hallucinations with those who do not, to investigate the brain morphology related to the transdiagnostic presentation of hallucinations. We...

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“…28 studies employed a regional and/or non-voxelwise approach to evaluate structural MRI data with respect to hallucination status: seven studies performed VBM restricted to predefined ROIs [43,63,[71][72][73][74][75], one performed source-based morphometry [76], nine explored cortical thickness (CT) and/or surface area [77][78][79][80][81][82][83][84][85], three investigated gyral/sulcal properties [43,86,87], and 11 assessed structure-specific shape parameters [43,83,85,[88][89][90][91][92][93][94][95]. Results are summarized in Tables 5-6.…”

Section: Resultsmentioning

confidence: 99%

Meta-Analytic Evidence for the Plurality of Mechanisms in Transdiagnostic Structural MRI Studies of Hallucination Status

et al. 2018

View full text Add to dashboard Cite

Background: Hallucinations are transmodal and transdiagnostic phenomena, occurring across sensory modalities and presenting in psychiatric, neurodegenerative, neurological, and non-clinical populations. Despite their crosscategory occurrence, little empirical work has directly compared between-group neural correlates of hallucinations. Methods: We performed whole-brain voxelwise meta-analyses of hallucination status across diagnoses using anisotropic effect-size seed-based d mapping (AES-SDM), and conducted a comprehensive systematic review in PubMed and Web of Science until May 2018 on other structural correlates of hallucinations, including cortical thickness and gyrification. Findings: 3214 abstracts were identified. Patients with psychiatric disorders and hallucinations (eight studies) exhibited reduced gray matter (GM) in the left insula, right inferior frontal gyrus, left anterior cingulate/ paracingulate gyrus, left middle temporal gyrus, and increased in the bilateral fusiform gyrus, while patients with neurodegenerative disorders with hallucinations (eight studies) showed GM decreases in the left lingual gyrus, right supramarginal gyrus/parietal operculum, left parahippocampal gyrus, left fusiform gyrus, right thalamus, and right lateral occipital gyrus. Group differences between psychiatric and neurodegenerative hallucination meta-analyses were formally confirmed using Monte Carlo randomizations to determine statistical significance, and a jackknife sensitivity analysis established the reproducibility of results across nearly all study combinations. For other structural measures (28 studies), the most consistent findings associated with hallucination status were reduced cortical thickness in temporal gyri in schizophrenia and altered hippocampal volume in Parkinson's disease and dementia. Additionally, increased severity of hallucinations in schizophrenia correlated with GM reductions within the left superior temporal gyrus, right middle temporal gyrus, bilateral supramarginal and angular gyri. Interpretation: Distinct patterns of neuroanatomical alteration characterize hallucination status in patients with psychiatric and neurodegenerative diseases, suggesting a plurality of anatomical signatures. This approach has implications for treatment, theoretical frameworks, and generates refutable predictions for hallucinations in other diseases and their occurrence within the general population. Funding: None.

show abstract

Individualized covariance profile of cortical morphology for auditory hallucinations in first‐episode psychosis

Cited by 29 publications

References 63 publications

OUP accepted manuscript

OUP accepted manuscript

Meta-analytic evidence for the plurality of mechanisms in transdiagnostic structural MRI studies of hallucination status

Meta-Analytic Evidence for the Plurality of Mechanisms in Transdiagnostic Structural MRI Studies of Hallucination Status

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