Resolving the connectome – Spectrally-specific functional connectivity networks and their distinct contributions to behaviour

Becker, Robert; Hervais-Adelman, Alexis

doi:10.1101/700278

Cited by 3 publications

(2 citation statements)

References 60 publications

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“…Increased theta and alpha band activity have been reported in depressed individuals (reviewed in Leuchter et al, 2013 ), with enhanced theta rhythms hypothesized to result from aberrant generation of these rhythms in the thalamus ( Schulman et al, 2011 ). These results are also consistent with recent findings that theta band mediated connections between subcortical and frontal nodes correlate with an array of behavioral measures ( Becker and Hervais-Adelman, 2019 ). Fewer studies have examined connectivity in MDD across the frequency spectrum.…”

Section: Discussionsupporting

confidence: 92%

Multilayer MEG functional connectivity as a potential marker for suicidal thoughts in major depressive disorder

Nugent

Ballard

Gilbert

et al. 2020

NeuroImage: Clinical

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Highlights Generalized slowing of connectivity was seen in those with MDD vs healthy subjects. Slowing from alpha/beta-mediated to delta/theta-mediated connectivity was observed. Connectivity strength and suicidal thoughts were linked in MDD subjects. “Hub” regions in the brain potentially underlying suicidal thoughts were identified. Connectivity differed between those with high suicidal thought scores and controls.

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Section: Discussionsupporting

confidence: 92%

Multilayer MEG functional connectivity as a potential marker for suicidal thoughts in major depressive disorder

Nugent

Ballard

Gilbert

et al. 2020

NeuroImage: Clinical

View full text Add to dashboard Cite

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“…Moreover, whereas CCA and PLS are restricted to discovering linear relationships between two datasets, there exist non-linear extensions, such as kernel (59,60), deep (61) or non-parametric (62) CCA, as well as extensions to multiple datasets (63). Due to their increased expressivity, and therefore capacity to overfit, we expect them to require even larger sample sizes.…”

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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The role of anatomical connection strength for interareal communication in macaque cortex

Vezoli

Vinck

et al. 2020

Preprint

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What is the relationship between anatomical connection strength and rhythmic synchronization? Simultaneous recordings of 15 cortical areas in two macaque monkeys show that interareal networks are functionally organized in spatially distinct modules with specific synchronization frequencies, i.e. frequency-specific functional connectomes. We relate the functional interactions between 91 area pairs to their anatomical connection strength defined in a separate cohort of twenty six subjects. This reveals that anatomical connection strength predicts rhythmic synchronization and vice-versa, in a manner that is specific for frequency bands and for the feedforward versus feedback direction, even if interareal distances are taken into account. These results further our understanding of structure-function relationships in large-scale networks covering different modality-specific brain regions and provide strong constraints on mechanistic models of brain function. Because this approach can be adapted to non-invasive techniques, it promises to open new perspectives on the functional organization of the human brain.

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Resolving the connectome – Spectrally-specific functional connectivity networks and their distinct contributions to behaviour

Cited by 3 publications

References 60 publications

Multilayer MEG functional connectivity as a potential marker for suicidal thoughts in major depressive disorder

Multilayer MEG functional connectivity as a potential marker for suicidal thoughts in major depressive disorder

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

The role of anatomical connection strength for interareal communication in macaque cortex

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