Delayed stabilization and individualization in connectome development are related to psychiatric disorders

Kaufmann, Tobias; Alnæs, Dag; Doan, Nhat Trung; Brandt, Christine Lycke; Andreassen, Ole A.; Westlye, Lars T.

doi:10.1038/nn.4511

Cited by 220 publications

(261 citation statements)

References 35 publications

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“…An additional possible explanation for the adolescent decrease in structural correlation is a “decoherence” related to interindividual differences in the timing of maturation of association areas—although the verification of such a hypothesis would again require longitudinal data. On a related note, recent work on functional connectivity has shown an adolescent increase the “distinctiveness” of individual functional connectomes (Kaufmann et al 2017). We further note that the association of changes in structural network architecture to rates of myelination is stronger (than to rates of cortical thinning), and that subtle nonlinearities in trajectories of myelination seem more strongly related to nonlinearities in trajectories of structural correlation, suggestive of the idea that myelination may be a driver of (changes) in structural covariance.…”

Section: Discussionmentioning

confidence: 99%

Adolescent Tuning of Association Cortex in Human Structural Brain Networks

et al. 2017

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Motivated by prior data on local cortical shrinkage and intracortical myelination, we predicted age-related changes in topological organization of cortical structural networks during adolescence. We estimated structural correlation from magnetic resonance imaging measures of cortical thickness at 308 regions in a sample of N = 297 healthy participants, aged 14–24 years. We used a novel sliding-window analysis to measure age-related changes in network attributes globally, locally and in the context of several community partitions of the network. We found that the strength of structural correlation generally decreased as a function of age. Association cortical regions demonstrated a sharp decrease in nodal degree (hubness) from 14 years, reaching a minimum at approximately 19 years, and then levelling off or even slightly increasing until 24 years. Greater and more prolonged age-related changes in degree of cortical regions within the brain network were associated with faster rates of adolescent cortical myelination and shrinkage. The brain regions that demonstrated the greatest age-related changes were concentrated within prefrontal modules. We conclude that human adolescence is associated with biologically plausible changes in structural imaging markers of brain network organization, consistent with the concept of tuning or consolidating anatomical connectivity between frontal cortex and the rest of the connectome.

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Section: Discussionmentioning

confidence: 99%

Adolescent Tuning of Association Cortex in Human Structural Brain Networks

et al. 2017

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“…Note that to avoid age effects on the WRAT scores, subjects whose age was below 16 years were removed, eventually leaving 267 subjects in total retained (Kaufmann et al, 2017). Note that to avoid age effects on the WRAT scores, subjects whose age was below 16 years were removed, eventually leaving 267 subjects in total retained (Kaufmann et al, 2017).…”

Section: Continuous Phenotypementioning

confidence: 99%

“…Recently, several fMRI studies considering the information provided by subject-level variability, have paved the way to the new promising avenue of detecting individual differences (Chamberland et al, 2017;Mueller et al, 2013). Besides, they also showed that a delay in this network-tuning process was associated with mental diseases (Kaufmann et al, 2017). Kaufmann et al reported that functional connectomes developed into a more stable, individual wiring pattern during adolescence.…”

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confidence: 99%

Refined measure of functional connectomes for improved identifiability and prediction

Cai

Zhang

et al. 2019

Human Brain Mapping

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Brain functional connectome analysis is commonly based on population‐wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population‐wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject‐specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group‐wise inferences and refined connectomes are indeed desirable for brain mapping.

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“…These functional connectivity networks are unique to the individual and consistent across testing conditions. 3,4 In the largest of these studies, with nearly 800 participants tested between the ages of 8 and 22 years, networks stabilized earlier in female than in male participants and earlier in healthy adolescents than psychologically troubled ones. 4 Individual differences in these "fingerprints" show evidence of being shaped by early life experiences 5,6 and of corresponding to cognitive-affective traits.…”

Section: Neural Fingerprintingmentioning

confidence: 99%