Multivariate Relationships Between Cognition and Brain Anatomy Across the Psychosis Spectrum

Rodrigue, Amanda; McDowell, Jennifer E.; Tandon, Neeraj; Keshavan, Matcheri S.; Tamminga, Carol A.; Pearlson, Godfrey D.; Sweeney, John A.; Gibbons, Robert D.; Clementz, Brett A.

doi:10.1016/j.bpsc.2018.03.012

Cited by 23 publications

(17 citation statements)

References 92 publications

(104 reference statements)

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“…was largely driven by differences in cognitive performance between SZ/SZAD and ADHD/BD patients. This is consistent with a frontoparietal atrophy pattern being associated with cognitive performance in a transdiagnostic sample, and differentiated between SZ/SZAD and BD patients (73). Previous reports have also suggested similar cognitive deficit patterns among SZ, SZAD and BD patients, although the latter typically exhibited less severe deficits (3,(74)(75)(76)(77).…”

Section: Somatomotor Network Are Transdiagnostic Hubssupporting

confidence: 87%

Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

Kebets

Holmes

Orban

et al. 2019

Preprint

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word count: 247 Main text word count: 3980 Number of figures: 4 Number of tables: 1 Number of supplemental files: 1 AbstractBackground: There is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thus neglecting the potential influence of huge swaths of the brain. Methods:A multivariate data-driven approach (partial least squares; PLS) was utilized to identify latent components linking a large set of clinical, cognitive and personality measures to whole-brain resting-state functional connectivity (RSFC) patterns across 224 participants.The participants were either healthy (N=110) or diagnosed with bipolar disorder (N=40), attention-deficit/hyperactivity disorder (N=37), schizophrenia (N=29) or schizoaffective disorder (N=8). In contrast to traditional case-control analyses, the diagnostic categories were not utilized in the PLS analysis, but were helpful for interpreting the components.Results: Our analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction and impulsivity. Each component was associated with a unique whole-brain RSFC signature and shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network, and its connectivity with subcortical structures and cortical executive networks. Conclusions:We identified three distinct dimensions with dissociable (but overlapping) whole-brain RSFC signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span across diagnostic categories.Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.

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Section: Somatomotor Network Are Transdiagnostic Hubssupporting

confidence: 87%

Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

Kebets

Holmes

Orban

et al. 2019

Preprint

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“…This finding is particularly important as the influence of the correlation strength on the stability of CCA results has been largely overlooked in previous studies. Indeed, many previous studies reported moderate CCCs-for example, 0.43 for a SVR of 8 (Lin et al, 2018) and 0.54 for a SVR of 9.97 (Rodrigue et al, 2018), or strong CCCs but at a relatively low SVR-for example, 0.87 for a SVR of 3.64 (Will et al, 2017) and 0.87 for a SVR of 4.61 (Smith et al, 2015), and the stability of their results may need to be reevaluated.…”

Section: The Stability Of Cca Results Is Affected By the Svr And The Correlation Strength Between Two Sets Of Variablesmentioning

confidence: 95%

“…We noticed that three subject measures (gender, height and weight) had very large loadings (Figures S2B and S3B), indicating that these three subject measures had major contributions to the first-mode correlation between brain imaging measures and subject measures. To further test whether the correlation strength between the two sets of variables also affects the stability of CCA results and whether the above observations also hold when there is only a moderate correlation between brain imaging measures and subject measures (Lin et al, 2018;Rodrigue et al, 2018), we removed these three subject measures and repeated all the above analyses to re-assess the stability of CCA results.…”

Section: Data Quality Controlmentioning

confidence: 99%

“…Due to these advantages, CCA is becoming increasingly popular in the identification of relationships between brain imaging measures and behavioral measures in both health (Davis et al, 2011;Perry et al, 2017;Shen et al, 2016;Smith et al, 2015;Tsvetanov et al, 2016;Vidaurre, Smith, & Woolrich, 2017;Wee et al, 2017;Will, Rutledge, Moutoussis, & Dolan, 2017) and diseases (Ball et al, 2017;Cai et al, 2019;Drysdale et al, 2017;Kottaram et al, 2019;Lin et al, 2018;McAnulty et al, 2013;Rodrigue et al, 2018;Viviano et al, 2018). For example, using CCA, Smith et al identified a strong positive-negative mode of population co-variation linking brain resting-state functional connectivity and human behaviors/ demographics, showing that subjects were predominantly spread along a single "positive-negative" axis linking lifestyle and psychometric measures to a specific pattern of brain connectivity (Smith et al, 2015).…”

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confidence: 99%

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Stability test of canonical correlation analysis for studying brain‐behavior relationships: The effects of subject‐to‐variable ratios and correlation strengths

Yang

Zhang

Song

et al. 2021

Human Brain Mapping

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Canonical correlation analysis (CCA), a multivariate approach to identifying correlations between two sets of variables, is becoming increasingly popular in neuroimaging studies on brain-behavior relationships. However, the CCA stability in neuroimaging applications has not been systematically investigated. Although it is known that the number of subjects should be greater than the number of variables due to the curse of dimensionality, it is unclear at what subject-to-variable ratios (SVR) and at what correlation strengths the CCA stability can be maintained. Here, we systematically assessed the CCA stability, in the context of investigating the relationship between the brain structural/functional imaging measures and the behavioral measures, by measuring the similarity of the first-mode canonical variables across randomly sampled subgroups of subjects from a large set of 936 healthy subjects. Specifically, we tested how the CCA stability changes with SVR under two different brain-behavior correlation strengths. The same tests were repeated using an independent data set (n = 700) for validation. The results confirmed that both SVR and correlation strength affect greatly the CCA stability-the CCA stability cannot be guaranteed if the SVR is not sufficiently high or the brain-behavior relationship is not sufficiently strong. Based on our quantitative characterization of CCA stability, we provided a practical guideline to help correct interpretation of CCA results and proper applications of CCA in neuroimaging studies on brain-behavior relationships.

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“…We note that sparse methods are also often used in classification tasks, where they have been observed to provide better prediction but less stable weights (55,56), which indicates a trade-off between prediction and inference (55). Correspondingly, it has been suggested to consider weight stability as a criterion in sparsity parameter selection (55,57,58).…”

Section: Brain-behavior Associationsmentioning

confidence: 99%

On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations

Helmer

Warrington

Mohammadi‐Nejad

et al. 2020

Preprint

112

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Associations between high-dimensional datasets, each comprising many features, can be discovered through multivariate statistical methods, like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). CCA and PLS are widely used methods which reveal which features carry the association. Despite the longevity and popularity of CCA/PLS approaches, their application to high-dimensional datasets raises critical questions about the reliability of CCA/PLS solutions. In particular, overfitting can produce solutions that are not stable across datasets, which severely hinders their interpretability and generalizability. To study these issues, we developed a generative model to simulate synthetic datasets with multivariate associations, parameterized by feature dimensionality, data variance structure, and assumed latent association strength. We found that resulting CCA/PLS associations could be highly inaccurate when the number of samples per feature is relatively small. For PLS, the profiles of feature weights exhibit detrimental bias toward leading principal component axes. We confirmed these model trends in state-ofthe-art datasets containing neuroimaging and behavioral measurements in large numbers of subjects, namely the Human Connectome Project (n ≈ 1000) and UK Biobank (n = 20000), where we found that only the latter comprised enough samples to obtain stable estimates. Analysis of the neuroimaging literature using CCA to map brain-behavior relationships revealed that the commonly employed sample sizes yield unstable CCA solutions. Our generative modeling framework provides a calculator of dataset properties required for stable estimates. Collectively, our study characterizes dataset properties needed to limit the potentially detrimental effects of overfitting on stability of CCA/PLS solutions, and provides practical recommendations for future studies.Significance StatementScientific studies often begin with an observed association between different types of measures. When datasets comprise large numbers of features, multivariate approaches such as canonical correlation analysis (CCA) and partial least squares (PLS) are often used. These methods can reveal the profiles of features that carry the optimal association. We developed a generative model to simulate data, and characterized how obtained feature profiles can be unstable, which hinders interpretability and generalizability, unless a sufficient number of samples is available to estimate them. We determine sufficient sample sizes, depending on properties of datasets. We also show that these issues arise in neuroimaging studies of brain-behavior relationships. We provide practical guidelines and computational tools for future CCA and PLS studies.

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Multivariate Relationships Between Cognition and Brain Anatomy Across the Psychosis Spectrum

Cited by 23 publications

References 92 publications

Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

Stability test of canonical correlation analysis for studying brain‐behavior relationships: The effects of subject‐to‐variable ratios and correlation strengths

On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations

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