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

Tozlu, Ceren; Card, Sophie; Jamison, Keith; Gauthier, Susan A.; Kuceyeski, Amy

doi:10.1162/netn_a_00292

Cited by 10 publications

(11 citation statements)

References 68 publications

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“…This aligns with findings from prior research on shifts in connectivity states in MS. 15,45,46 The same might be true for the detected increase in control energy for CI patients, as recent work showed that MS patients with physical disability required more control energy compared to non-disabled patients. 21 In contrast to that finding, we did not observe a relationship between physical disability and transition control energy in this study, possibly as our cohort had longer disease durations where disability mechanisms might be different. Interestingly, the transition control energy was particularly relevant for information processing speed and verbal fluency which suggests that transition control energy might be related to shared cognitive processes, such as cognitive flexibility.…”

Section: Discussioncontrasting

confidence: 96%

“…43 Parameter choice is still a topic of debate, however, particularly for the arbitrary choice of a control horizon, which is why we optimized this parameter using a data-driven approach (see eMethods 1). 21 Despite these challenges, the current framework offers an exciting opportunity and generalizable approach to study and develop personalized treatment of cognitive impairment in MS by identifying optimal target areas for transcranial magnetic stimulation or for tailoring pharmacological interventions. 48,49 Furthermore, although fine-grained temporal scales can increase noisiness of windowed connectivity (i.e., short windows), the current approach utilizes information (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…20 Control energy has been likened to cognitive demand or mental load, so these measures quantify how effortful transitions between connectivity states are. Recent work showed that physically disabled MS patients required more control energy to transition between activity states, 21 but it is unclear whether this can explain the disrupted functional network dynamics in patients with cognitive impairment.…”

Section: Introductionmentioning

confidence: 99%

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Energetically costly functional network dynamics in cognitively impaired multiple sclerosis patients

Broeders,

van Dam,

Pontillo

et al. 2024

Preprint

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Background and objectives. Patients with multiple sclerosis (MS) often experience cognitive impairment, and this is related to structural disconnection and subsequent functional reorganization. It is not clear how specific patterns of functional reorganization might make it harder for cognitively impaired (CI) MS patients to dynamically adapt how brain regions communicate, which is crucial for normal cognition. To identify patterns relevant to cognitive impairment in MS, we performed a temporally detailed analysis of connectivity state transitions and quantified the effort associated with such dynamic alterations. Methods. Resting-state functional and diffusion MRI was acquired from 95 controls and 330 MS patients (mean disease duration: 15 years) in a cross-sectional design, of whom 86 were classified as CI (≥2/7 domains Z < −2) and 65 as mildly CI (≥2/7 domains Z < −1.5) based on the performance on an expanded Brief Repeatable Battery of Neuropsychological Tests. Four functional connectivity states were determined using K-means clustering of moment-to-moment co-fluctuations (i.e., edge time series), and the resulting state sequence was used to characterize the frequency of transitions between network configurations. The control energy associated with the transitions between states was then calculated using the structural network of each subject with network control theory. Results. CI patients transition less frequently between connectivity states. Relative to the time spent in a particular state, CI patients specifically transition less from a weakly connected and highly modular state (i.e., the weakly connected state) to a more integrated state that featured strong involvement of the visual network (i.e., the visual network state), but more in the opposite direction. CI patients also required more control energy to transition between states. Discussion. This study showed that it became more effortful for MS patients with cognitive impairment to dynamically change the organization of the functional network, providing an intuitive understanding of why these transitions occur less frequently in some patients. In particular, transitions between the weakly connected state and the more integrated visual network state were relevant for cognition in these patients. The findings highlight a possible underpinning of disturbed cognition in MS patients and also provide novel avenues for studying and possibly improving network dynamics.

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Section: Discussioncontrasting

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energetically costly functional network dynamics in cognitively impaired multiple sclerosis patients

Broeders,

van Dam,

Pontillo

et al. 2024

Preprint

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“…Despite this, correlations of SC and FC are moderate at best. Understanding the relationship between SC and FC is key to deciphering how the brain's physical structure supports its dynamic functions and may offer insights into the neural mechanisms underlying behavior, injury or disease, and recovery [1][2][3][4] .…”

Section: Introductionmentioning

confidence: 99%

Release the Krakencoder: A unified brain connectome translation and fusion tool

Jamison,

Gu,

Wang

et al. 2024

Preprint

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Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional connectivity (FC), and across different atlases and processing choices via a common latent representation. These mappings demonstrate unprecedented accuracy and individual-level identifiability; the mapping between SC and FC has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This "fusion" representation i) better reflects familial relatedness, ii) preserves age- and sex- relevant information and iii) enhances cognition-relevant information. The Krakencoder can be applied without retraining to new, out-of-age-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a significant leap forward in capturing the relationship between multi-modal brain connectomes in an individualized, behaviorally- and demographically-relevant way.

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“…27 In MS, a relevant measure, the energy required by brain state transitions, has proven to be useful for distinguishing patients in different disability status. 28 However, whether network controllability measures alter in people with MS and whether these changes (if exist) differ between patients with different status of cognition remain unclear.…”

Section: Introductionmentioning

confidence: 99%

Thalamic network controllability changes and cognitive impairment in multiple sclerosis

Yang,

Woollams,

Lipp

et al. 2023

Preprint

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Multiple sclerosis is a neuroinflammatory and neurodegenerative disease commonly associated with cognitive impairment. Understanding brain mechanisms of cognitive impairment in multiple sclerosis is crucial for early diagnosis and developing effective interventions to improve the quality of life in patients. Recent studies indicate that individuals with multiple sclerosis who develop cognitive impairment display changes in network activity in the brain, such as altered transitions between network states (network activity patterns). Particularly, regions within the subcortical network, like the thalamus, show abnormalities early in multiple sclerosis, possibly driving the subsequent changes in the rest of the brain. In this study, we investigated whether there are brain regions specifically involved in driving network changes throughout the brain in multiple sclerosis, and assessed how this relates to cognitive impairment in patients. To this end, we constructed functional brain networks based on resting-state functional MRI data from 102 multiple sclerosis patients and 27 healthy controls. Then we applied network controllability analysis using the most commonly used controllability measures to quantify the effect that brain networks or regions have on driving network changes and state transitions in multiple sclerosis. Furthermore, we compared network controllability changes between patients with different cognitive status. Finally, we tested the reproducibility of our main results using a separate dataset of 95 multiple sclerosis patients and 45 healthy controls. We found significant global, cortical, and subcortical controllability changes in multiple sclerosis, as indicated by increased average controllability while decreased modal controllability and decreased activation energy in patients compared to controls. These changes predominately concentrated in the subcortical network, particularly the thalamus, and were further confirmed in the replication dataset. The controllability changes suggest a compensatory strategy in the brain network of patients, towards preserving fundamental transitions between easy-to-reach network states while relinquishing energetically costly transitions between difficult-to-reach network states. Moreover, while both cognitively impaired and cognitively preserved patients showed controllability changes compared to healthy controls, the thalamus in cognitively impaired patients exhibited a significantly greater increase in average controllability than cognitively preserved patients. This emphasizes the crucial role of the thalamus in the emergence of cognitive impairment in multiple sclerosis. Overall, this study highlights the effect that the subcortical network and the thalamus has on driving network changes across the brain in multiple sclerosis and especially in those cognitively impaired patients, suggesting a possible brain mechanism underpinning cognitive impairment in individuals with multiple sclerosis.

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

Cited by 10 publications

References 68 publications

Energetically costly functional network dynamics in cognitively impaired multiple sclerosis patients

Energetically costly functional network dynamics in cognitively impaired multiple sclerosis patients

Release the Krakencoder: A unified brain connectome translation and fusion tool

Thalamic network controllability changes and cognitive impairment in multiple sclerosis

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