Autonomic dysfunction in multiple sclerosis and other updates on recent autonomic research

Miglis, Mitchell G.; Muppidi, Srikanth

doi:10.1007/s10286-018-0548-5

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Subtypes of multiple sclerosis based on clinical presentation (signs and symptoms) are recognized ( 15 – 18 ) including tremor ( 19 ), ataxia ( 20 ), visual disturbances ( 21 , 22 ), sensory symptoms (numbness and paresthesias) ( 23 – 26 ), pyramidal tract findings (weakness, hyperreflexia, spasticity, and hypertonia) ( 27 – 29 ), or spinal cord findings (paraparesis, sphincter dysfunction, and sensory levels) ( 30 , 31 ). Other MS subjects show cognitive impairment ( 32 , 33 ), dysarthria ( 34 ), dysautonomia ( 35 ), depression ( 36 ), imbalance ( 37 ), paroxysmal symtoms ( 38 ), or fatigue ( 39 , 40 ).…”

Section: Introductionmentioning

confidence: 99%

Subtypes of relapsing-remitting multiple sclerosis identified by network analysis

Howlett-Prieto

Oommen²,

Carrithers³

et al. 2023

Front. Digit. Health

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We used network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. The electronic medical records of 113 subjects with relapsing-remitting multiple sclerosis were reviewed, signs and symptoms were mapped to classes in a neuro-ontology, and classes were collapsed into sixteen superclasses by subsumption. After normalization and vectorization of the data, bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using NetworkX and visualized in Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps visualized differences in features by community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.25). The bipartite network was partitioned into five communities which were named fatigue, behavioral, hypertonia/weakness, abnormal gait/sphincter, and sensory, based on feature characteristics. The unipartite network was partitioned into five communities which were named fatigue, pain, cognitive, sensory, and gait/weakness/hypertonia based on features. Although we did not identify pure subtypes (e.g., pure motor, pure sensory, etc.) in this cohort of multiple sclerosis subjects, we demonstrated that network analysis could partition these subjects into different subtype communities. Larger datasets and additional partitioning algorithms are needed to confirm these findings and elucidate their significance. This study contributes to the literature investigating subtypes of multiple sclerosis by combining feature reduction by subsumption with network analysis.

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Section: Introductionmentioning

confidence: 99%

Subtypes of relapsing-remitting multiple sclerosis identified by network analysis

Howlett-Prieto

Oommen²,

Carrithers³

et al. 2023

Front. Digit. Health

View full text Add to dashboard Cite

show abstract

“…MS patients can present with different predominant symptoms that may emphasize tremor [20], ataxia [21], visual symptoms [22, 23], sensory symptoms including numbness and paresthesias [24, 25, 26, 27], pyramidal tract findings such as weakness, hyperreflexia, spasticity, and hypertonia [28, 29, 30], or spinal cord presentations that include paraparesis, sphincter dysfunction, and sensory levels [31, 32]. Some patients present with cognitive impairment [33, 34], dysarthria [35], dysautonomia [36], depression and psychiatric disturbances [37], imbalance [38] or fatigue [39]. It is of interest whether identifiable subtypes of MS based on predominant clinical presentation (sensory, cerebellar, motor, visual, cognitive, fatigue, etc.)…”

Section: Introductionmentioning

confidence: 99%

Subtypes of Relapsing-Remitting Multiple Sclerosis Identified by Network Analysis

Howlett-Prieto

Oommen

Carrithers

et al. 2022

Preprint

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The objective of this study was to use network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. We reviewed the electronic medical records of 120 subjects with relapsing-remitting multiple sclerosis and recorded signs and symptoms. Signs and symptoms were mapped to a neuro-ontology and then collapsed into 16 superclasses by subsumption and normalized. Bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps were used to visualize differences in features by the community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.247). Network analysis can partition multiple sclerosis subjects into communities based on signs and symptoms. Communities of subjects with predominant motor, sensory, pain, fatigue, cognitive, behavior, and fatigue features were found. Larger datasets and additional partitioning algorithms are needed to confirm these results and elucidate their clinical significance.

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“…As always, we would like to underline the colossal value and devotion of our associate editors who provide work of excellence to the assessment of every submission they manage. Special thanks to Dr. David S. Goldstein and Dr. William P. Cheshire for their outstanding series on autonomic function testing [1,2], and to Dr. Mitch Miglis and Dr. Sri Muppidi for their timely summaries on recent autonomic news [3,4], which will continue in 2019.…”

mentioning

confidence: 99%

The Clinical Autonomic Research journal 2019 and onward

Jordan

2019

Clin Auton Res

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Autonomic dysfunction in multiple sclerosis and other updates on recent autonomic research

Cited by 4 publications

References 7 publications

Subtypes of relapsing-remitting multiple sclerosis identified by network analysis

Subtypes of relapsing-remitting multiple sclerosis identified by network analysis

Subtypes of Relapsing-Remitting Multiple Sclerosis Identified by Network Analysis

The Clinical Autonomic Research journal 2019 and onward

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