Models to Tailor Brain Stimulation Therapies in Stroke

Plow, Ela B.; Sankarasubramanian, Vishwanath; Cunningham, David A.; Potter–Baker, Kelsey A.; Varnerin, Nicole; Cohen, Leonardo G.; Sterr, Annette; Conforto, Adriana Bastos; Machado, André G.

doi:10.1155/2016/4071620

Cited by 51 publications

(45 citation statements)

References 158 publications

(231 reference statements)

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“…Individualized, state-specific networks may provide higher specificity when considered as predictive features in machine learning algorithms (67,68). Individualized networks could provide input for real-time fMRI neurofeedback paradigms (69)(70)(71), and brain stimulation therapies (72,73), focusing on the disruption of networks at the single subject level. The conventional approach of defining fixed networks via group-level parcellations could reduce the efficacy of treatments.…”

Section: Individualized Brain Network Are Neededmentioning

confidence: 99%

State-specific individualized functional networks form a predictive signature of brain state

Salehi

Karbasi

et al. 2018

Preprint

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There is extensive evidence that human brain functional organization is dynamic, varying within a subject as the brain switches between tasks demands. This functional organization also varies across subjects, even when they are all engaged in similar tasks. Currently, we lack a comprehensive model that unifies the two dimensions of variation (brain state and subject). Using fMRI data obtained across multiple task-evoked and rest conditions (which we operationally define as brain states) and across multiple subjects, we develop a state-and subject-specific functional network parcellation (the assignment of nodes to networks). Our parcellation approach provides a measure of how node-to-network assignment (NNA) changes across states and across subjects. We demonstrate that the brain's functional networks are not spatially fixed, but reconfigure with brain state. This reconfiguration is robust and reliable to such an extent that it can be used to predict brain state with accuracies up to 97%.Recent studies have made significant progress in characterizing state-evoked changes in functional connectivity elicited by task performance (20-28). However, none of these works explicitly examined the possibility that networks spatially reconfigure: (i) The majority of these studies explicitly assumed that networks remain spatially unchanged across tasks (21,22,29). These analyses were restricted to investigating differences in connectivity between networks, and not whether networks spatially reconfigure across tasks. (ii) Another line of research takes a more abstract perspective by computing global network measures (such as modularity and participation coefficient) and comparing these measures across different states (27,28,(30)(31)(32). Such approaches address the modular reconfiguration of the brain as a whole by defining a new set of networks for each state, but they do not quantify how or whether the same networks change across states. Such studies also typically define a small number of networks (~3-7) whose correspondence to the putative resting-state networks are unclear. These approaches cannot answer how specific networks reorganize as a function of brain state. (iii) Finally, many studies do not directly examine cross-subject variations in network reorganization (20), or treat crosssubject and cross-session changes as similar notions for defining reorganization (33). However, functional organization varies significantly across subjects, yielding large individual differences in network definitions (34,35). There is a clear need to account for both cross-subject and crossstate variability in functional network organization when characterizing brain dynamics.Here, we use a novel approach to dynamically map the brain's functional networks across taskevoked and resting states and across individuals. We operationalize the brain's functional subunits as a set of 268 pre-defined nodes (6). We then apply our recently developed exemplarbased parcellation method (36) to assign a set of pre-defined nodes to individualized, stat...

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Section: Individualized Brain Network Are Neededmentioning

confidence: 99%

State-specific individualized functional networks form a predictive signature of brain state

Salehi

Karbasi

et al. 2018

Preprint

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“…A prevailing model of interhemispheric imbalance after stroke espouses that NIBS be used to up-regulate excitability in ipsilesional motor cortex (M1) and down-regulate excitability in the contralesional M1 (Nowak et al, 2009). However, recent reviews suggest the interhemispheric imbalance model may be too simplistic, and instead advocate for multidimensional models that take into account the extent of structural damage and availability of residual motor pathways (Bradnam et al, 2013, Di Pino et al, 2014, Plow et al, 2016.…”

Section: Introductionmentioning

confidence: 99%

Revisiting interhemispheric imbalance in chronic stroke: A tDCS study

McCambridge

Stinear

Byblow

2018

Clinical Neurophysiology

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“…cPMd is believed to support function by alleviating competition imposed on iM1 (Johansen-Berg et al, 2002, Bestmann et al, 2010, Chen et al, 2016, Mohapatra et al, 2016). Therefore, a more recent hypothesis suggests that undamaged areas like cPMd are key for recovery of severely affected patients, while iM1 is important only for mildly affected (Di Pino et al, 2014, Plow et al, 2016). This hypothesis is also referred to as the “bimodal hypothesis” of plasticity.…”

Section: Introductionmentioning

confidence: 99%

Inhibition versus facilitation of contralesional motor cortices in stroke: Deriving a model to tailor brain stimulation

Sankarasubramanian

Machado

Conforto

et al. 2017

Clinical Neurophysiology

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Objective The standard approach to brain stimulation in stroke is based on the premise that ipsilesional M1 (iM1) is important for motor function of the paretic upper limb, while contralesional cortices compete with iM1. Therefore, the approach typically advocates facilitating iM1 and/or inhibiting contralesional M1 (cM1). But, this approach fails to elicit much improvement in severely affected patients, who on account of extensive damage to ipsilesional pathways, cannot rely on iM1. These patients are believed to instead rely on the undamaged cortices, especially the contralesional dorsal premotor cortex (cPMd), for support of function of the paretic limb. Here, we tested for the first time whether facilitation of cPMd could improve paretic limb function in severely affected patients, and if a cut-off could be identified to separate responders to cPMd from responders to the standard approach to stimulation. Methods In a randomized, sham-controlled crossover study, fifteen patients received the standard approach of stimulation involving inhibition of cM1 and a new approach involving facilitation of cPMd using repetitive transcranial magnetic stimulation (rTMS). Patients also received rTMS to control areas. At baseline, impairment [Upper Extremity Fugl-Meyer (UEFMPROXIMAL, max =36)] and damage to pathways [fractional anisotropy (FA)] was measured. We measured changes in time to perform proximal paretic limb reaching, and neurophysiology using TMS. Results Facilitation of cPMd generated more improvement in severely affected patients, who had experienced greater damage and impairment than a cut-off value of FA (0.5) and UEFMPROXIMAL (26–28). The standard approach instead generated more improvement in mildly affected patients. Responders to cPMd showed alleviation of interhemispheric competition imposed on iM1, while responders to the standard approach showed gains in ipsilesional excitability in association with improvement. Conclusions A preliminary cut-off level of severity separated responders for standard approach vs. facilitation of cPMd. Significance Cut-offs identified here could help select candidates for tailored stimulation in future studies so patients in all ranges of severity could potentially achieve maximum benefit in function of the paretic upper limb.

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Models to Tailor Brain Stimulation Therapies in Stroke

Cited by 51 publications

References 158 publications

State-specific individualized functional networks form a predictive signature of brain state

State-specific individualized functional networks form a predictive signature of brain state

Revisiting interhemispheric imbalance in chronic stroke: A tDCS study

Inhibition versus facilitation of contralesional motor cortices in stroke: Deriving a model to tailor brain stimulation

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