BrainStat: A toolbox for brain-wide statistics and multimodal feature associations

Park

et al. 2023

Human Brain Mapping

Migraine is a type of headache with multiple neurological symptoms. Prior neuroimaging studies in patients with migraine based on functional magnetic resonance imaging have found regional as well as network‐level alterations in brain function. Here, we expand on prior studies by establishing whole‐brain functional connectivity patterns in patients with migraine using dimensionality reduction techniques. We studied functional brain connectivity in 50 patients with episodic migraine and sex‐ and age‐matched healthy controls. Using dimensionality reduction techniques that project high‐dimensional functional connectivity onto low‐dimensional representations (i.e., eigenvectors), we found significant between‐group differences in the eigenvectors between patients with migraine and healthy controls, particularly in the sensory/motor and limbic cortices. Furthermore, we assessed between‐group differences in subcortical connectivity with subcortical weighted manifolds defined by subcortico‐cortical connectivity multiplied by cortical eigenvectors and revealed significant alterations in the amygdala. Finally, leveraging supervised machine learning, we moderately predicted headache frequency using cortical and subcortical functional connectivity features, again indicating that sensory and limbic regions play a particularly important role in predicting migraine frequency. Our study confirmed that migraine is a hierarchical disease of the brain that shows alterations along the sensory‐limbic axis, and therefore, the functional connectivity in these areas could be a useful marker to investigate migraine symptomatology.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Whole‐brain functional gradients reveal cortical and subcortical alterations in patients with episodic migraine

Park

et al. 2023

Human Brain Mapping

“…This allowed direct comparison of the organization of functional asymmetry across groups and individuals, in line with previous work (Hong et al, 2019;Wan et al, 2022). Individual functional gradient computation and analyses with Python packages BrainSpace (Vos de Wael et al, 2020) and BrainStat (Larivière et al, 2023) are described in the Methods.…”

Section: Asymmetry Along Functional Organization Axes (Figure 1)mentioning

confidence: 64%

Diverging asymmetry of intrinsic functional organization in autism

Wan

Hong

Bethlehem

et al. 2023

Preprint

Self Cite

Autism is a neurodevelopmental condition involving atypical sensory-perceptual functions together with language and socio-cognitive deficits. Previous work has reported subtle alterations in the asymmetry of brain structure and reduced laterality of functional activation in individuals with autism relative to non-autistic individuals (NAI). However, whether functional asymmetries show altered intrinsic systematic organization in autism remains unclear. Here, we computed inter- and intra-hemispheric asymmetry of intrinsic functional gradients capturing connectome organization along three axes, stretching between sensory-default, somatomotor-visual, and default-multiple demand networks, to study system-level hemispheric imbalances in autism. We observed decreased leftward functional asymmetry of language network organization in individuals with autism, relative to NAI. Whereas language network asymmetry varied across age groups in NAI, this was not the case in autism, suggesting atypical functional laterality in autism may result from altered developmental trajectories. Finally, we observed that intra- but not inter-hemispheric features were predictive of the severity of autistic traits. In sum, our findings illustrate how regional and patterned functional lateralization is altered in autism at the system level. Such differences may be rooted in altered developmental trajectories of functional organization asymmetry in autism.

“…98,115 Computation and derivation of the metrics are described in the supplementary text S23. 33,95,96,99,[116][117][118][119] For this analysis, statistical significance of spatial correlations was assessed via spin permutations (n = 1,000) which represent a null model preserving the inherent spatial autocorrelation of cortical information. 120 Spin permutations are performed by projecting parcel-wise data onto a sphere which then is randomly rotated.…”

Section: Contextualization Analysismentioning

confidence: 99%

“…Besides descriptive group statistics, individuals with MetS and matched controls were compared on a surface vertex-level leveraging the BrainStat toolbox (v 0.3.6, https://brainstat.readthedocs.io/). 96 A general linear model was applied correcting for age, gender, education and cohort effects. Vertex-wise p-values were FDR-corrected for multiple comparisons.…”

Section: Sensitivity Analysesmentioning

confidence: 99%

A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition

Petersen

Hoffstaedter

Naegele

et al. 2023

Preprint

Metabolic syndrome (MetS) describes the clustered prevalence of abdominal obesity, arterial hypertension, dyslipidemia and insulin resistance, collectively promoting cerebrovascular disease. Although this risk factor complex causes a substantial health burden in modern societies, its neural underpinnings remain elusive. To investigate the multivariate association of MetS and cortical thickness, we applied partial least squares (PLS) correlation to a pooled sample of two large-scale population-based cohort studies totaling 40,087 individuals. PLS identified a latent clinical-anatomical dimension relating more severe MetS with a widespread pattern of cortical thickness abnormalities and worse cognitive performance. MetS effects were strongest in regions with high density of endothelial cells, microglia and excitatory neurons of subtype 8. Moreover, regional MetS effects correlated within functionally and structurally connected networks. Overall, our research suggests a low-dimensional relationship between MetS and brain structure that is governed by both the microscopic composition of brain tissue as well as macroscopic brain network organization.