Revealing motor behavior patterns and functional improvement trajectories following spinal cord injury using unsupervised machine learning Article Swipe
Jaclyn T. Eisdorfer
,
J. Thackray
,
Thomas Theis
,
Ana L. Vivinetto
,
Matthew T Ricci
,
Sherry Lin
,
Olisemeka Oputa
,
A Martínez
,
Hannah D. Nacht
,
Monica Tschang
,
Malaika Mahmood
,
Ashley Tucker
,
Manon Bohic
,
Shailee Pusuloori
,
Lance Zmoyro
,
Suneel Kumar
,
Melitta Schachner
,
Phillip G. Popovich
,
Adam R. Ferguson
,
Dana M. McTigue
,
Vicki M. Tysseling
,
Jennifer N. Dulin
,
Edmund Hollis
,
Sandeep Robert Datta
,
Victoria E. Abraira
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.10.31.564826
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.10.31.564826
Spinal cord injury (SCI) disrupts motor function, with recovery involving complex adaptive motor behaviors that cannot be fully assessed with traditional manual recovery assessments and their predefined recovery criteria. To move beyond these constraints, we employed motion sequencing (MoSeq), a machine learning-driven framework that automatically segments freely moving mouse behavior into discrete, repeated behavioral “syllables”, effectively capturing entire behavioral repertoires and dynamic behavioral sequences observed throughout SCI recovery. We observed that functional improvements involved the reemergence of preinjury behaviors, emergence of adaptive behaviors, and subtle individual differences between mice. Interestingly, the temporal organization of behavioral sequences evolved consistently across injury severities, suggesting a ceiling effect in sequence reorganization. MoSeq syllables also correlated with the Basso Mouse Scale (BMS) and outperformed BMS in correlation to several injury-related histological findings. We also introduced the “Recovery Score,” a new metric derived from aggregated MoSeq data that quantifies recovery based on syllable usage patterns. The Recovery Score identified clusters of syllables with high usages, which highlights the potential of using hallmark syllables as behavioral biomarkers for assessing recovery and informing therapies. Future research should incorporate more complex experimental designs to refine this aggregation approach and advance the pursuit of a new gold standard in recovery assessment.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.10.31.564826
- https://www.biorxiv.org/content/biorxiv/early/2023/11/05/2023.10.31.564826.full.pdf
- OA Status
- green
- Cited By
- 3
- References
- 124
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388211385
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388211385Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.10.31.564826Digital Object Identifier
- Title
-
Revealing motor behavior patterns and functional improvement trajectories following spinal cord injury using unsupervised machine learningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-02Full publication date if available
- Authors
-
Jaclyn T. Eisdorfer, J. Thackray, Thomas Theis, Ana L. Vivinetto, Matthew T Ricci, Sherry Lin, Olisemeka Oputa, A Martínez, Hannah D. Nacht, Monica Tschang, Malaika Mahmood, Ashley Tucker, Manon Bohic, Shailee Pusuloori, Lance Zmoyro, Suneel Kumar, Melitta Schachner, Phillip G. Popovich, Adam R. Ferguson, Dana M. McTigue, Vicki M. Tysseling, Jennifer N. Dulin, Edmund Hollis, Sandeep Robert Datta, Victoria E. AbrairaList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.10.31.564826Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/11/05/2023.10.31.564826.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/11/05/2023.10.31.564826.full.pdfDirect OA link when available
- Concepts
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Spinal cord injury, Behavioral pattern, Computer science, Psychology, Neuroscience, Physical medicine and rehabilitation, Cognitive psychology, Artificial intelligence, Machine learning, Medicine, Spinal cord, Software engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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124Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.also | 111, 130 |
| abstract_inverted_index.cord | 2 |
| abstract_inverted_index.data | 142 |
| abstract_inverted_index.from | 139 |
| abstract_inverted_index.gold | 198 |
| abstract_inverted_index.high | 159 |
| abstract_inverted_index.into | 51 |
| abstract_inverted_index.more | 182 |
| abstract_inverted_index.move | 31 |
| abstract_inverted_index.that | 15, 44, 71, 143 |
| abstract_inverted_index.this | 188 |
| abstract_inverted_index.with | 8, 20, 113, 158 |
| abstract_inverted_index.(BMS) | 118 |
| abstract_inverted_index.(SCI) | 4 |
| abstract_inverted_index.Basso | 115 |
| abstract_inverted_index.MoSeq | 109, 141 |
| abstract_inverted_index.Mouse | 116 |
| abstract_inverted_index.Scale | 117 |
| abstract_inverted_index.Score | 153 |
| abstract_inverted_index.based | 146 |
| abstract_inverted_index.fully | 18 |
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| abstract_inverted_index.motor | 6, 13 |
| abstract_inverted_index.mouse | 49 |
| abstract_inverted_index.their | 26 |
| abstract_inverted_index.these | 33 |
| abstract_inverted_index.usage | 149 |
| abstract_inverted_index.using | 166 |
| abstract_inverted_index.which | 161 |
| abstract_inverted_index.Future | 178 |
| abstract_inverted_index.Spinal | 1 |
| abstract_inverted_index.across | 99 |
| abstract_inverted_index.beyond | 32 |
| abstract_inverted_index.cannot | 16 |
| abstract_inverted_index.effect | 105 |
| abstract_inverted_index.entire | 58 |
| abstract_inverted_index.freely | 47 |
| abstract_inverted_index.injury | 3, 100 |
| abstract_inverted_index.manual | 22 |
| abstract_inverted_index.metric | 137 |
| abstract_inverted_index.motion | 37 |
| abstract_inverted_index.moving | 48 |
| abstract_inverted_index.refine | 187 |
| abstract_inverted_index.should | 180 |
| abstract_inverted_index.subtle | 85 |
| abstract_inverted_index.advance | 192 |
| abstract_inverted_index.between | 88 |
| abstract_inverted_index.ceiling | 104 |
| abstract_inverted_index.complex | 11, 183 |
| abstract_inverted_index.derived | 138 |
| abstract_inverted_index.designs | 185 |
| abstract_inverted_index.dynamic | 62 |
| abstract_inverted_index.evolved | 97 |
| abstract_inverted_index.machine | 41 |
| abstract_inverted_index.pursuit | 194 |
| abstract_inverted_index.several | 125 |
| abstract_inverted_index.usages, | 160 |
| abstract_inverted_index.(MoSeq), | 39 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.Recovery | 152 |
| abstract_inverted_index.adaptive | 12, 82 |
| abstract_inverted_index.approach | 190 |
| abstract_inverted_index.assessed | 19 |
| abstract_inverted_index.behavior | 50 |
| abstract_inverted_index.clusters | 155 |
| abstract_inverted_index.disrupts | 5 |
| abstract_inverted_index.employed | 36 |
| abstract_inverted_index.hallmark | 167 |
| abstract_inverted_index.involved | 74 |
| abstract_inverted_index.observed | 65, 70 |
| abstract_inverted_index.recovery | 9, 23, 28, 145, 174, 201 |
| abstract_inverted_index.repeated | 53 |
| abstract_inverted_index.research | 179 |
| abstract_inverted_index.segments | 46 |
| abstract_inverted_index.sequence | 107 |
| abstract_inverted_index.standard | 199 |
| abstract_inverted_index.syllable | 148 |
| abstract_inverted_index.temporal | 92 |
| abstract_inverted_index.Score,” | 134 |
| abstract_inverted_index.assessing | 173 |
| abstract_inverted_index.behaviors | 14 |
| abstract_inverted_index.capturing | 57 |
| abstract_inverted_index.criteria. | 29 |
| abstract_inverted_index.discrete, | 52 |
| abstract_inverted_index.emergence | 80 |
| abstract_inverted_index.findings. | 128 |
| abstract_inverted_index.framework | 43 |
| abstract_inverted_index.function, | 7 |
| abstract_inverted_index.informing | 176 |
| abstract_inverted_index.involving | 10 |
| abstract_inverted_index.patterns. | 150 |
| abstract_inverted_index.potential | 164 |
| abstract_inverted_index.preinjury | 78 |
| abstract_inverted_index.recovery. | 68 |
| abstract_inverted_index.sequences | 64, 96 |
| abstract_inverted_index.syllables | 110, 157, 168 |
| abstract_inverted_index.aggregated | 140 |
| abstract_inverted_index.behavioral | 54, 59, 63, 95, 170 |
| abstract_inverted_index.behaviors, | 79, 83 |
| abstract_inverted_index.biomarkers | 171 |
| abstract_inverted_index.correlated | 112 |
| abstract_inverted_index.functional | 72 |
| abstract_inverted_index.highlights | 162 |
| abstract_inverted_index.identified | 154 |
| abstract_inverted_index.individual | 86 |
| abstract_inverted_index.introduced | 131 |
| abstract_inverted_index.predefined | 27 |
| abstract_inverted_index.quantifies | 144 |
| abstract_inverted_index.sequencing | 38 |
| abstract_inverted_index.suggesting | 102 |
| abstract_inverted_index.therapies. | 177 |
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| abstract_inverted_index.aggregation | 189 |
| abstract_inverted_index.assessment. | 202 |
| abstract_inverted_index.assessments | 24 |
| abstract_inverted_index.correlation | 123 |
| abstract_inverted_index.differences | 87 |
| abstract_inverted_index.effectively | 56 |
| abstract_inverted_index.incorporate | 181 |
| abstract_inverted_index.reemergence | 76 |
| abstract_inverted_index.repertoires | 60 |
| abstract_inverted_index.severities, | 101 |
| abstract_inverted_index.traditional | 21 |
| abstract_inverted_index.“Recovery | 133 |
| abstract_inverted_index.consistently | 98 |
| abstract_inverted_index.constraints, | 34 |
| abstract_inverted_index.experimental | 184 |
| abstract_inverted_index.histological | 127 |
| abstract_inverted_index.improvements | 73 |
| abstract_inverted_index.organization | 93 |
| abstract_inverted_index.outperformed | 120 |
| abstract_inverted_index.automatically | 45 |
| abstract_inverted_index.Interestingly, | 90 |
| abstract_inverted_index.injury-related | 126 |
| abstract_inverted_index.learning-driven | 42 |
| abstract_inverted_index.reorganization. | 108 |
| abstract_inverted_index.“syllables”, | 55 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 25 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.76688175 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |