Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups

Tozlu, Ceren; Jamison, Keith; Gu, Zijin; Gauthier, Susan A.; Kuceyeski, Amy

doi:10.1016/j.nicl.2021.102827

Cited by 11 publications

(11 citation statements)

References 54 publications

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“…It has been suggested (Tian et al, 2021 ) that the parameter coefficients of the prediction models can be unreliable to assess the feature importance. Therefore, similar to our recent work that compared the prediction ability of observed vs. estimated SC and FC networks in classifying pwMS by disability status (Tozlu et al, 2021 ), here we report the important features that had high feature weight from the classification models and that also showed a larger difference in the mass univariate group comparisons.…”

Section: Discussionsupporting

confidence: 86%

Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis

et al. 2021

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Background: Advanced imaging techniques such as diffusion and functional MRI can be used to identify pathology-related changes to the brain's structural and functional connectivity (SC and FC) networks and mapping of these changes to disability and compensatory mechanisms in people with multiple sclerosis (pwMS). No study to date performed a comparison study to investigate which connectivity type (SC, static or dynamic FC) better distinguishes healthy controls (HC) from pwMS and/or classifies pwMS by disability status.Aims: We aim to compare the performance of SC, static FC, and dynamic FC (dFC) in classifying (a) HC vs. pwMS and (b) pwMS who have no disability vs. with disability. The secondary objective of the study is to identify which brain regions' connectome measures contribute most to the classification tasks.Materials and Methods: One hundred pwMS and 19 HC were included. Expanded Disability Status Scale (EDSS) was used to assess disability, where 67 pwMS who had EDSS<2 were considered as not having disability. Diffusion and resting-state functional MRI were used to compute the SC and FC matrices, respectively. Logistic regression with ridge regularization was performed, where the models included demographics/clinical information and either pairwise entries or regional summaries from one of the following matrices: SC, FC, and dFC. The performance of the models was assessed using the area under the receiver operating curve (AUC).Results: In classifying HC vs. pwMS, the regional SC model significantly outperformed others with a median AUC of 0.89 (p <0.05). In classifying pwMS by disability status, the regional dFC and dFC metrics models significantly outperformed others with a median AUC of 0.65 and 0.61 (p < 0.05). Regional SC in the dorsal attention, subcortical and cerebellar networks were the most important variables in the HC vs. pwMS classification task. Increased regional dFC in dorsal attention and visual networks and decreased regional dFC in frontoparietal and cerebellar networks in certain dFC states was associated with being in the group of pwMS with evidence of disability.Discussion: Damage to SCs is a hallmark of MS and, unsurprisingly, the most accurate connectomic measure in classifying patients and controls. On the other hand, dynamic FC metrics were most important for determining disability level in pwMS, and could represent functional compensation in response to white matter pathology in pwMS.

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Section: Discussionsupporting

confidence: 86%

Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis

et al. 2021

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“…3,4 FMRI in pwMS has largely been used to investigate the association between the brain's static and/or dynamic functional connectivity (FC) and motor/cognitive impairment. [5][6][7] The majority of FC studies have assumed that the FC is static 3,4 and have ignored shorter scale changes of brain network activity that have been shown to occur. 8 The dynamic FC (dFC) approach captures recurrent co-fluctuation states by clustering dynamic FC matrices computed over sliding windows of BOLD time series.…”

Section: Introductionmentioning

confidence: 99%

Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity

Tozlu

Card²,

Jamison

et al. 2023

Network Neuroscience

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Quantifying the relationship between the brain’s functional activity patterns and its structural backbone is crucial when relating the severity of brain pathology to disability in multiple sclerosis (MS). Network control theory (NCT) characterizes the brain’s energetic landscape using the structural connectome and patterns of brain activity over time. We applied NCT to investigate brain state dynamics and energy landscapes in controls and people with MS (pwMS). We also computed entropy of brain activity and investigated its association with the dynamic landscape’s transition energy and lesion volume. Brain states were identified by clustering regional brain activity vectors, and NCT was applied to compute the energy required to transition between these brain states. We found that entropy was negatively correlated with lesion volume and transition energy, and that larger transition energies were associated with pwMS with disability. This work supports the notion that shifts in the pattern of brain activity in pwMS without disability results in decreased transition energies compared to controls, but, as this shift evolves over the disease, transition energies increase beyond controls and disability occurs. Our results provide the first evidence in pwMS that larger lesion volumes result in greater transition energy between brain states and decreased entropy of brain activity.

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“…However, the contribution of these delayed network disruption mechanisms is likely to be minor in the timeframe up to 36 h after stroke studied here. Moreover, despite this methodological limitation, NeMo, the particular toolbox used in this study, has been shown repeatedly to produce clinically and anatomically valid estimates of structural disconnection (Kuceyeski et al, 2014(Kuceyeski et al, , 2015(Kuceyeski et al, , 2016Olafson et al, 2021;Respino et al, 2019;Tozlu et al, 2021). Thus, while indirect network mapping only provides surrogate markers of true neuronal disconnection and is contingent on both the validity of the tractography algorithm performed in the underlying reference population and the assumption that lesion-induced effects exceed inter-individual variability in white matter structure, it has significant utility in situations in which subject-specific diffusion imaging, tractography and network reconstruction cannot be performed.…”

Section: Discussionmentioning

confidence: 95%

Early effect of thrombolysis on structural brain network organisation after anterior‐circulation stroke in the randomized WAKE‐UP trial

Schlemm

Jensen

Kuceyeski

et al. 2022

Human Brain Mapping

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The symptoms of acute ischemic stroke can be attributed to disruption of the brain network architecture. Systemic thrombolysis is an effective treatment that preserves structural connectivity in the first days after the event. Its effect on the evolution of global network organisation is, however, not well understood. We present a secondary analysis of 269 patients from the randomized WAKE-UP trial, comparing 127 imaging-selected patients treated with alteplase with 142 controls who received placebo. We used indirect network mapping to quantify the impact of ischemic lesions on structural brain network organisation in terms of both global parameters of segregation and integration, and local disruption of individual connections. Network damage was estimated before randomization and again 22 to

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Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups

Cited by 11 publications

References 54 publications

Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis

Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis

Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity

Early effect of thrombolysis on structural brain network organisation after anterior‐circulation stroke in the randomized WAKE‐UP trial

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