Resting‐State Functional Connectivity Predicts <scp>STN DBS</scp> Clinical Response

Younce, John R.; Campbell, Meghan C.; Hershey, Tamara; Tanenbaum, Aaron; Milchenko, Mikhail; Ushe, Mwiza; Karimi, Morvarid; Tabbal, Samer D.; Kim, Albert E.; Snyder, Abraham Z.; Perlmutter, Joel S.; Norris, Scott A.

doi:10.1002/mds.28376

Cited by 32 publications

(25 citation statements)

References 42 publications

(97 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such, therapeutic response to stimulation may be a direct result of a stimulation-induced change from aberrant to healthy network-level functional connectivity. This change may in turn be determined by the underlying network-level functional connectivity, as recent work has indicated that underlying functional connectivity shapes the therapeutic response to stimulation [23][24][25], and here we have also shown that it shapes stimulationinduced network-level FCC. Further work which holistically studies the underlying network, neural stimulation, FCC, and neural disorders is needed.…”

Section: Network-level Analysis Of Neural Stimulationsupporting

confidence: 66%

“…Recent research indicates that network-level functional connectivity is not only a↵ected by stimulation but also is involved in shaping the response to stimulation. For example, underlying functional connectivity has been shown to shape stimulation-induced neural activity propagation [18][19][20][21][22] as well as influence the therapeutic outcome of neural stimulation interventions for Parkinson's disease [23,24] and depression [25]. Early results have also indicated that network-level FCC induced by stimulation are correlated to baseline functional connectivity [11,17,26].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cortical network structure mediates response to stimulation: an optogenetic study in non-human primates

Bloch

Greaves-Tunnell

Shea‐Brown

et al. 2021

Preprint

View full text Add to dashboard Cite

Brain stimulation interventions are used for the treatment of neurological disorders such as Parkinson's and depression. Despite some success, the effects of stimulation are poorly understood, as evidenced by unknown mechanisms governing therapeutic outcomes, widespread side-effects, and inconsistent results in clinical translation. This incomplete understanding may originate from incomplete treatment of the effects of stimulation, as most studies limit their scale and subject of analysis to local effects of various stimulation protocols. Indeed, in a recent paper we demonstrated that neural stimulation impacts the brain at the network scale; here, we leverage advances in neural interfaces and interpretable machine learning to reveal that the cortical network is the primary controlling factor of the response to stimulation. Using optogenetic stimulation and micro-electrocorticography (μECoG) recording in two awake rhesus macaques, we induce and record widespread functional connectivity changes over large-scale sensorimotor cortical networks. We then use a novel nonparametric modeling framework to predict these changes from the stimulation protocol and from characteristics of the underlying cortical network. We observe that the stimulation protocol only explains a small portion of the network response to stimulation while the network structure characteristics explain much more of the response, implicating the underlying network as the primary mediator of the response to stimulation. We extract the relationships linking the stimulation and network characteristics to the functional connectivity changes and observe that the mappings diverge over frequency bands and successive stimulations. Finally, we uncover shared processes governing real-time and longer-term effects of stimulation, demonstrating that all neural stimulation interventions must consider both timescales for their effects to be fully understood. The insights and developments generated in this work represent a paradigm shift for the field of neural stimulation.

show abstract

Section: Network-level Analysis Of Neural Stimulationsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Cortical network structure mediates response to stimulation: an optogenetic study in non-human primates

Bloch

Greaves-Tunnell

Shea‐Brown

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…More recently, imaging studies demonstrated the predictive value of structural and functional connectivity for motor outcome of STN-DBS in PD patients based on diffusion tensor imaging (DTI) ( Horn et al, 2017 ) and resting-state functional MRI (rs-fMRI) ( Younce et al, 2020 ). Smaller ventricular volumes measured on T1 weighted images were associated with more favorable motor outcome in one report ( Younce et al, 2019 ) but not in others ( Price et al, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

“…This study conducted RA-ML analysis of specific motor outcome which is not available in most previous studies ( Price et al, 2011 ; Lönnfors-Weitzel et al, 2016 ; Horn et al, 2017 ; Younce et al, 2019 , 2020 ). Cardinal motor symptoms in PD patients include rigidity, bradykinesia, and tremor.…”

Section: Discussionmentioning

confidence: 99%

Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson’s Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study

et al. 2021

View full text Add to dashboard Cite

BackgroundEmerging evidence indicates that iron distribution is heterogeneous within the substantia nigra (SN) and it may reflect patient-specific trait of Parkinson’s Disease (PD). We assume it could account for variability in motor outcome of subthalamic nucleus deep brain stimulation (STN-DBS) in PD.ObjectiveTo investigate whether SN susceptibility features derived from radiomics with machine learning (RA-ML) can predict motor outcome of STN-DBS in PD.MethodsThirty-three PD patients underwent bilateral STN-DBS were recruited. The bilateral SN were segmented based on preoperative quantitative susceptibility mapping to extract susceptibility features using RA-ML. MDS-UPDRS III scores were recorded 1–3 days before and 6 months after STN-DBS surgery. Finally, we constructed three predictive models using logistic regression analyses: (1) the RA-ML model based on radiomics features, (2) the RA-ML+LCT (levodopa challenge test) response model which combined radiomics features with preoperative LCT response, (3) the LCT response model alone.ResultsFor the predictive performances of global motor outcome, the RA-ML model had 82% accuracy (AUC = 0.85), while the RA-ML+LCT response model had 74% accuracy (AUC = 0.83), and the LCT response model alone had 58% accuracy (AUC = 0.55). For the predictive performance of rigidity outcome, the accuracy of the RA-ML model was 80% (AUC = 0.85), superior to those of the RA-ML+LCT response model (76% accuracy, AUC = 0.82), and the LCT response model alone (58% accuracy, AUC = 0.42).ConclusionOur findings demonstrated that SN susceptibility features from radiomics could predict global motor and rigidity outcomes of STN-DBS in PD. This RA-ML predictive model might provide a novel approach to counsel candidates for STN-DBS.

show abstract

“…32 Preoperative FC between STN and the ipsilateral globus pallidus internus (GPi) has also been shown to be positively correlated with clinical improvement after STN-DBS in Parkinson's disease. 33 In contrast, there is far less known about preoperative FC and DBS outcomes in patients with idiopathic generalized dystonia, however a recent study positively correlated preoperative FC between lead site (GPi) and primary sensorimotor cortex, motor thalamus, and cerebellum. 34 It should be noted that several of these connectivity studies also utilize structural connectivity (T2 diffusion imaging) although FC often independently predicts DBS outcomes.…”

Section: Diseasementioning

confidence: 99%

Is Resting State Functional MRI Effective Connectivity in Movement Disorders Helpful? A Focused Review Across Lifespan and Disease

Sussman¹,

Wyckoff²,

Heim³

et al. 2021

Preprint

View full text Add to dashboard Cite

In the evolving modern era of neuromodulation for movement disorders in adults and children, much progress has been made recently characterizing the human motor network (MN) with potentially important treatment implications. Herein is a focused review of relevant resting state fMRI functional and effective connectivity of the human motor network across the lifespan in health and disease. The goal is to examine how the transition from static functional to dynamic effective connectivity may be especially informative of network-targeted movement disorder therapies, with hopeful implications for children.Impact StatementWhile functional connectivity has elucidated much MN properties with relation to age, disease, and behavior, effective connectivity has been shown to be useful in MN-informed therapies in adults. Thus, effective connectivity may have potential to impact childhood movement disorder therapies, given the lower to no patient demand.

show abstract

Resting‐State Functional Connectivity Predicts STN DBS Clinical Response

Cited by 32 publications

References 42 publications

Cortical network structure mediates response to stimulation: an optogenetic study in non-human primates

Cortical network structure mediates response to stimulation: an optogenetic study in non-human primates

Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson’s Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study

Is Resting State Functional MRI Effective Connectivity in Movement Disorders Helpful? A Focused Review Across Lifespan and Disease

Contact Info

Product

Resources

About