Early deviation from normal structural connectivity

Taylor, Peter Neal; Silva, Nádia Moreira da; Blamire, Andrew M.; Wang, Yujiang; Forsyth, Rob

doi:10.1212/wnl.0000000000008902

Cited by 24 publications

(35 citation statements)

References 19 publications

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“… Young et al., 2018 ). A key implication of such trajectories is that different brain processes (or disorders) may cluster in terms of their trajectories, or share parts of their trajectories, potentially indicating shared drivers/pathways/modulations ( Jensen, Moseley, Oprea, Ellesøe, Eriksson, Schmock, Jensen, Jensen, Brunak, 2014 , Taylor, Moreira da Silva, Blamire, Wang, Forsyth, 2020 ). Especially with a comprehensive region-specific and cross-region analysis of cortical morphology we expect clusters of directions to emerge.…”

Section: Discussionmentioning

confidence: 99%

Independent components of human brain morphology

et al. 2021

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Quantification of brain morphology has become an important cornerstone in understanding brain structure. Measures of cortical morphology such as thickness and surface area are frequently used to compare groups of subjects or characterise longitudinal changes. However, such measures are often treated as independent from each other. A recently described scaling law, derived from a statistical physics model of cortical folding, demonstrates that there is a tight covariance between three commonly used cortical morphology measures: cortical thickness, total surface area, and exposed surface area. We show that assuming the independence of cortical morphology measures can hide features and potentially lead to misinterpretations. Using the scaling law, we account for the covariance between cortical morphology measures and derive novel independent measures of cortical morphology. By applying these new measures, we show that new information can be gained; in our example we show that distinct morphological alterations underlie healthy ageing compared to temporal lobe epilepsy, even on the coarse level of a whole hemisphere. We thus provide a conceptual framework for characterising cortical morphology in a statistically valid and interpretable manner, based on theoretical reasoning about the shape of the cortex.

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Section: Discussionmentioning

confidence: 99%

Independent components of human brain morphology

et al. 2021

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“…Previous studies have shown that the Mahalanobis distance provides information over and above a univariate analysis. In a study of patients with traumatic brain injury, (Taylor et al, 2020) demonstrated that the Mahalanobis distance derived from FA values of 22 white matter tracts better distinguished patients from controls (AUC = 0.82) than any individual univariate tract z-score (AUC = 0.72), and was associated with a level of cognitive impairment. A study of patients with autism demonstrated that the Mahalanobis distance could better distinguish patients from controls (Dean et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…We hypothesised that patients with a longer epilepsy duration would be associated with greater abnormalities ipsilateral to the epileptic focus. This approach has been fruitful in studies of autism and traumatic brain injury (Dean et al, 2017;Taylor et al, 2020). Applications of the Mahalanobis distance include analysing individual tracts by integrating multiple diffusion metrics into a single measure, or by pooling numerous metrics from a number of different modalities.…”

Section: Introductionmentioning

confidence: 99%

Multivariate white matter alterations are associated with epilepsy duration

Owen

Tisi

Vos

et al. 2020

Eur J of Neuroscience

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Previous studies investigating associations between white matter alterations and duration of temporal lobe epilepsy (TLE) have shown differing results, and were typically limited to univariate analyses of tracts in isolation. In this study, we apply a multivariate measure (the Mahalanobis distance), which captures the distinct ways white matter may differ in individual patients, and relate this to epilepsy duration. Diffusion MRI, from a cohort of 94 subjects (28 healthy controls, 33 left‐TLE and 33 right‐TLE), was used to assess the association between tract fractional anisotropy (FA) and epilepsy duration. Using ten white matter tracts, we analysed associations using the traditional univariate analysis (z‐scores) and a complementary multivariate approach (Mahalanobis distance), incorporating multiple white matter tracts into a single unified analysis. For patients with right‐TLE, FA was not significantly associated with epilepsy duration for any tract studied in isolation. For patients with left‐TLE, the FA of two limbic tracts (ipsilateral fornix, contralateral cingulum gyrus) were significantly negatively associated with epilepsy duration (Bonferonni corrected p < .05). Using a multivariate approach we found significant ipsilateral positive associations with duration in both left, and right‐TLE cohorts (left‐TLE: Spearman's ρ = 0.487, right‐TLE: Spearman's ρ = 0.422). Extrapolating our multivariate results to duration equals zero (i.e., at onset) we found no significant difference between patients and controls. Associations using the multivariate approach were more robust than univariate methods. The multivariate Mahalanobis distance measure provides non‐overlapping and more robust results than traditional univariate analyses. Future studies should consider adopting both frameworks into their analysis in order to ascertain a more complete understanding of epilepsy progression, regardless of laterality.

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“…The Mahalanobis distance, M, is a multivariate quantification of distance that, unlike Euclidean distance, considers the correlation between the variables (Figure 1A). As described in Taylor et al (2020), we computed the Mahalanobis distance of the edges in a bin for a patient, M, using the following:…”

Section: Hippocampal Network Change In Patientsmentioning

confidence: 99%

MRI network progression in mesial temporal lobe epilepsy related to healthy brain architecture

Morgan

Johnson

Cai

et al. 2021

Network Neuroscience

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We measured MRI network progression in mesial temporal lobe epilepsy (mTLE) patients as a function of healthy brain architecture. Resting state functional MRI and diffusion-weighted MRI were acquired in 40 unilateral mTLE patients and 70 healthy controls. Data were used to construct region-to-region functional connectivity, structural connectivity and streamline length connectomes per subject. Three models of distance from the presumed seizure focus in the anterior hippocampus in the healthy brain were computed using the average connectome across controls. A fourth model was defined using regions of transmodal (higher cognitive function) to unimodal (perceptual) networks across a published functional gradient in the healthy brain. These models were used to test whether network progression in patients increased when distance from the anterior hippocampus or along a functional gradient in the healthy brain decreases. Results showed that alterations of structural and functional networks in mTLE occur in greater magnitude in regions of the brain closer to the seizure focus based on healthy brain topology, and decrease as distance from the focus increases over duration of disease. Overall, this work provides evidence that changes across the brain in focal epilepsy occur along healthy brain architecture.

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Early deviation from normal structural connectivity

Cited by 24 publications

References 19 publications

Independent components of human brain morphology

Independent components of human brain morphology

Multivariate white matter alterations are associated with epilepsy duration

MRI network progression in mesial temporal lobe epilepsy related to healthy brain architecture

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