Establishing a Baseline for Human Cortical Folding Morphological Variables: A Multisite Study

Moraes, Fernanda Hansen Pacheco de; Mello, Victor B B; Tovar‐Moll, Fernanda; Mota, Bruno

doi:10.3389/fnins.2022.897226

Cited by 5 publications

(10 citation statements)

References 42 publications

(63 reference statements)

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“…We control for sex in all of the reported analyses (also see the supplement for an analysis controlling head size with comparable results). Fourthly, the magnitude of longitudinal changes in gyrification during the relatively short follow-up period in our study is modest, but within the range of lifespan, changes were observed among healthy subjects in larger studies 73 , 74 . Our careful exclusion of movement-related confounds, the application of longitudinal image registration, and the use of the same scanning parameters over a relatively short time (months) reduced measurement inaccuracy, thus strengthening our interpretation.…”

Section: Discussionmentioning

confidence: 47%

Prognostic associations of cortical gyrification in minimally medicated schizophrenia in an early intervention setting

Pan

Chen

et al. 2022

Schizophr

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The aberration in cortical gyrification seen in schizophrenia likely originates in the earliest phase of life, as gyrification begins in utero and reaches its peak in infancy. However, emerging observations have indicated a later reduction in gyrification, especially in early adulthood, may also occur in schizophrenia. At present, it is unclear whether the baseline and later gyrification reduction has any prognostic importance in schizophrenia. We address this question in a longitudinal design in patients minimally medicated at inception. About 108 minimally medicated (duration of medication = <14 days of antipsychotics) patients and 106 healthy controls underwent structural magnetic resonance imaging, with 34 patients being selectively re-scanned when clinically stable following antipsychotic treatment. The cortical surface from each structural image was reconstructed, and the local gyrification index and cortical thickness were computed for each vertex on the surface. We found minimally medicated schizophrenia patients during the first episode had a relatively higher gyrification in bilateral supramarginal, left superior temporal, and right posterior cingulate and paracentral regions. However, poor prognostic features were more likely in patients with lower baseline gyrification. Longitudinal reductions in left superior parietal and right precentral gyrification were associated with lower improvements in both positive and negative symptoms over time. The spatial pattern of longitudinal changes in gyrification was distinct from the changes in cortical thickness. These results indicated that schizophrenia is characterized by a relative hypergyrification in parieto-temporal and medial cortical areas at a group level at first presentation, but poor outcomes relate to lower-gyrification elsewhere both at the onset and during the early course. The early post-onset reduction of gyrification is rather limited in space and magnitude, but occurs unrelated to the progressive thinning, representing a distinct, prognostically important structural trajectory.

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Section: Discussionmentioning

confidence: 47%

Prognostic associations of cortical gyrification in minimally medicated schizophrenia in an early intervention setting

Pan

Chen

et al. 2022

Schizophr

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“…Performing the PCA in the human harmonized dataset [1, 3, 9, 19, 25, 30, 40, 41], one obtain the results summarized in Figure 1. We analyzed separately subjects in the 20-40 yo age range, who presumably have a fully developed cortex (in broad terms, after development but before significant aging), and subjects in the age range between 20-60 years, for whom age effects were mathematically removed (henceforth “de-aged”) [3]. Table 1 summarizes the differences between the principal components and the theoretical expectation.…”

Section: Resultsmentioning

confidence: 99%

“…To avoid this problem, we used two techniques: (i) restrict the analysis to the age range between 20-40 years (adulthood) as it would be the best representation of a stationary fully developed cortex; (ii) take advantage of the harmonization linear regression, removing the age dependence using each subject age and the angular coefficient fitted, a procedure called here deageing. The latter allows to perform the analysis to the full age range (20-60 years) in which the model was experimentally validated [3]. In Appendix 5, one finds the replication of the analysis for all age ranges on the dataset (with and without the deageing).…”

Section: Methodsmentioning

confidence: 99%

“…The objective of this work is to test and systematize the description of cortical morphology, contrasting the old and new variables in terms of their statistical interdependence. To test the independence between the new variable set { K, S, I} we performed a standard Principal Component Analysis (PCA) to obtain the independent components of variance in the {log T 2 , log A T , log A E } space, for two different cases: across multiple species and across healthy human subjects (obtained from [3, 22]). The principal components (PC) expected for the first case are the independent set { K, S, I} [41].…”

Section: Introductionmentioning

confidence: 99%

“…It is also important to emphasize that although the variables K, S and I were derived from a specific model, their use to express and analyze morphology over the cortical plane is model-agnostic. [3,22]). The principal components (PC) expected for the first case are the independent set {K, S, I} [41].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the principal components of cortical morphological variables

Mello

Moraes

Mota

2022

Preprint

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The generating mechanism for the gyrification of the mammalian cerebral cortex remains a central open question in neuroscience. Although many models have been proposed over the years, very few were able to provide empirically testable predictions. In this paper, we assume a model in which the cortex folds for all species of mammals according to a simple mechanism of effective free energy minimization of a growing self-avoiding surface subjected to inhomogeneous bulk stresses to derive a new set of morphological variables to express cortical morphology. In terms of these new variables, we seek to understand the variance present in two morphometric datasets: a human MRI harmonized multi-site dataset comprised by 3324 healthy controls (CTL) from 4 to 96 years old and a collection of different mammalian cortices with morphological measurements extracted manually. This is done using a standard Principal Component Analysis (PCA) of the cortical morphometric space. We prove there is a remarkable coincidence (typically less than 8 degrees) between the resulting principal components vectors in each datasets and the directions corresponding to the new variables. This shows that the new, theoretically-derived variables are a set of natural and independent morphometrics with which to express cortical shape and size.

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SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites

Zhang,

Chen,

Oliver

et al. 2024

Human Brain Mapping

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In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter‐scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter‐scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex‐level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex‐level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner‐invariant and scanner‐specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.

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Establishing a Baseline for Human Cortical Folding Morphological Variables: A Multisite Study

Cited by 5 publications

References 42 publications

Prognostic associations of cortical gyrification in minimally medicated schizophrenia in an early intervention setting

Prognostic associations of cortical gyrification in minimally medicated schizophrenia in an early intervention setting

Predicting the principal components of cortical morphological variables

SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites

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