Cerebrospinal fluid Aβ42 and fractalkine are associated with Parkinson’s disease with freezing of gait

Hatcher-Martin, Jaime M.; McKay, J. Lucas; Sommerfeld, Barbara; Howell, J. Christina; Goldstein, Felicia C.; Hu, William T.; Factor, Stewart A.

doi:10.1101/2020.12.16.20248342

Cited by 4 publications

(1 citation statement)

References 46 publications

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“…The most established self-reporting scale used in research settings, the N-FOG-Q is acknowledged to be insufficiently sensitive for clinical trial use [ 10 ]. Previous work have shown that FOG may be associated with non-dopaminergic system changes [ 3 , 11 , 12 ], which suggests the potential for new treatments beyond dopaminergic medications like carbidopa-levodopa [ 4 ]. However, developing a novel drug that is effective in treating FOG requires accurately quantifying FOG to increase the precision for clinical trials.…”

Section: Introductionmentioning

confidence: 99%

An Explainable Spatial-Temporal Graphical Convolutional Network to Score Freezing of Gait in Parkinsonian Patients

Kwon

Clifford

Genias

et al. 2023

Sensors

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Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (i.e., MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. A major innovation of our study is that it is the first study of its kind that uses the largest sample size (>30 h, N = 57) in order to apply explainable, multi-task deep learning models for quantifying FOG over the course of the medication cycle and at varying levels of parkinsonism severity. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N = 57 patients during levodopa challenge tests. The proposed model was able to explain how kinematic movements are associated with each FOG severity level that were highly consistent with the features, in which movement disorders specialists are trained to identify as characteristics of freezing. Overall, we demonstrate that deep learning models’ capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.

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Section: Introductionmentioning

confidence: 99%

An Explainable Spatial-Temporal Graphical Convolutional Network to Score Freezing of Gait in Parkinsonian Patients

Kwon

Clifford

Genias

et al. 2023

Sensors

Self Cite

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Enhanced levels of fractalkine and HSP60 in cerebrospinal fluid of sporadic amyotrophic lateral sclerosis patients

Savant,

Pradhan,

Bhagat

et al. 2024

International Journal of Neuroscience

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An explainable spatial-temporal graphical convolutional network to score freezing of gait in parkinsonian patients

Kwon

Clifford

Genias

et al. 2023

Preprint

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Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (ie. MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N=57 patients during levodopa challenge tests. The proposed model was able to identify kinematic features associated with each FOG severity level that were highly consistent with the features that movement disorders specialists are trained to identify as characteristic of freezing. In this work, we demonstrate that deep learning models' capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.

show abstract

Cerebrospinal fluid Aβ42 and fractalkine are associated with Parkinson’s disease with freezing of gait

Cited by 4 publications

References 46 publications

An Explainable Spatial-Temporal Graphical Convolutional Network to Score Freezing of Gait in Parkinsonian Patients

An Explainable Spatial-Temporal Graphical Convolutional Network to Score Freezing of Gait in Parkinsonian Patients

Enhanced levels of fractalkine and HSP60 in cerebrospinal fluid of sporadic amyotrophic lateral sclerosis patients

An explainable spatial-temporal graphical convolutional network to score freezing of gait in parkinsonian patients

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