Concurrent Detection of Cognitive Impairment and Amyloid Positivity with a Next-Generation Digital Cognitive Assessment Article Swipe
Ali Jannati
,
Karl M. Thompson
,
Claudio Toro‐Serey
,
Joyce Gomes‐Osman
,
Russell Banks
,
Connor Higgins
,
John Showalter
,
David W. Bates
,
Sean Tobyne
,
Álvaro Pascual‐Leone
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6768373/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6768373/v1
Background: Early identification of cognitive impairment andbrain pathology associated with Alzheimer’s disease (AD) is essential to maximize benefits from lifestyle interventions and emerging pharmacologic disease-modifying treatments (DMT). Digital cognitive assessments (DCAs) can quickly capture an array of metrics that can be used to train machine learning models to concurrently evaluate different outcomes. DCAs have the potential to optimize clinical workflows and enable efficient assessment of cognitive function and the likelihood of a given underlying pathology. Methods: We assessed the ability of a next-generation DCA, the Digital Clock and Recall (DCR), to concurrently estimate brain amyloid-beta (Aβ) status and detect cognitive impairment, as compared with traditional cognitive assessments, including the MMSE, RAVLT, a DCA, Cognivue®, and blood-based biomarkers in 930 participants from the Bio-Hermes-001 clinical study. Results: Aβ42/40, pTau-181, and pTau-217 poorly classified cognitive impairment (AUCs: 0.63; 0.66; 0.72, respectively), but accurately classified Aβ status (AUCs: 0.81; 0.78; 0.89, respectively). MMSE, RAVLT, and Cognivue poorly classified Aβ status (AUCs: 0.71, 0.72, 0.70, respectively). However, separate multimodal, DCR-based machine-learning classification models, run in parallel, accurately classified both cognitive impairment (AUC=0.85) and Aβ status (AUC=0.83). Conclusions: DCAs that leverage digital technologies to generate advanced metrics, such as the DCR, enable accurate and efficient detection of cognitive impairment associated with AD pathology. They have the potential to empower health systems and primary care providers to help their patients make timely treatment decisions.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-6768373/v1
- https://www.researchsquare.com/article/rs-6768373/latest.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410888312
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410888312Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-6768373/v1Digital Object Identifier
- Title
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Concurrent Detection of Cognitive Impairment and Amyloid Positivity with a Next-Generation Digital Cognitive AssessmentWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-30Full publication date if available
- Authors
-
Ali Jannati, Karl M. Thompson, Claudio Toro‐Serey, Joyce Gomes‐Osman, Russell Banks, Connor Higgins, John Showalter, David W. Bates, Sean Tobyne, Álvaro Pascual‐LeoneList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-6768373/v1Publisher landing page
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https://www.researchsquare.com/article/rs-6768373/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-6768373/latest.pdfDirect OA link when available
- Concepts
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Cognitive impairment, Cognition, Amyloid (mycology), Psychology, Medicine, Neuroscience, Cognitive psychology, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.DCAs | 53 |
| abstract_inverted_index.DCR, | 196 |
| abstract_inverted_index.They | 209 |
| abstract_inverted_index.both | 175 |
| abstract_inverted_index.care | 219 |
| abstract_inverted_index.from | 19, 121 |
| abstract_inverted_index.have | 54, 210 |
| abstract_inverted_index.help | 222 |
| abstract_inverted_index.make | 225 |
| abstract_inverted_index.such | 193 |
| abstract_inverted_index.that | 39, 185 |
| abstract_inverted_index.used | 42 |
| abstract_inverted_index.with | 10, 104, 206 |
| abstract_inverted_index.(Aβ) | 96 |
| abstract_inverted_index.0.63; | 136 |
| abstract_inverted_index.0.66; | 137 |
| abstract_inverted_index.0.70, | 161 |
| abstract_inverted_index.0.71, | 159 |
| abstract_inverted_index.0.72, | 138, 160 |
| abstract_inverted_index.0.78; | 147 |
| abstract_inverted_index.0.81; | 146 |
| abstract_inverted_index.0.89, | 148 |
| abstract_inverted_index.Clock | 87 |
| abstract_inverted_index.MMSE, | 110, 150 |
| abstract_inverted_index.array | 36 |
| abstract_inverted_index.brain | 94 |
| abstract_inverted_index.given | 73 |
| abstract_inverted_index.their | 223 |
| abstract_inverted_index.train | 44 |
| abstract_inverted_index.(AUCs: | 135, 145, 158 |
| abstract_inverted_index.(DCAs) | 31 |
| abstract_inverted_index.(DCR), | 90 |
| abstract_inverted_index.(DMT). | 27 |
| abstract_inverted_index.RAVLT, | 111, 151 |
| abstract_inverted_index.Recall | 89 |
| abstract_inverted_index.detect | 99 |
| abstract_inverted_index.enable | 62, 197 |
| abstract_inverted_index.health | 215 |
| abstract_inverted_index.models | 47 |
| abstract_inverted_index.poorly | 131, 154 |
| abstract_inverted_index.status | 97, 144, 157, 181 |
| abstract_inverted_index.study. | 125 |
| abstract_inverted_index.timely | 226 |
| abstract_inverted_index.Digital | 28, 86 |
| abstract_inverted_index.ability | 80 |
| abstract_inverted_index.capture | 34 |
| abstract_inverted_index.digital | 187 |
| abstract_inverted_index.disease | 12 |
| abstract_inverted_index.empower | 214 |
| abstract_inverted_index.machine | 45 |
| abstract_inverted_index.metrics | 38 |
| abstract_inverted_index.models, | 169 |
| abstract_inverted_index.primary | 218 |
| abstract_inverted_index.quickly | 33 |
| abstract_inverted_index.systems | 216 |
| abstract_inverted_index.Cognivue | 153 |
| abstract_inverted_index.However, | 163 |
| abstract_inverted_index.accurate | 198 |
| abstract_inverted_index.advanced | 191 |
| abstract_inverted_index.assessed | 78 |
| abstract_inverted_index.benefits | 18 |
| abstract_inverted_index.clinical | 59, 124 |
| abstract_inverted_index.compared | 103 |
| abstract_inverted_index.emerging | 23 |
| abstract_inverted_index.estimate | 93 |
| abstract_inverted_index.evaluate | 50 |
| abstract_inverted_index.function | 67 |
| abstract_inverted_index.generate | 190 |
| abstract_inverted_index.learning | 46 |
| abstract_inverted_index.leverage | 186 |
| abstract_inverted_index.maximize | 17 |
| abstract_inverted_index.metrics, | 192 |
| abstract_inverted_index.optimize | 58 |
| abstract_inverted_index.pTau-217 | 130 |
| abstract_inverted_index.patients | 224 |
| abstract_inverted_index.separate | 164 |
| abstract_inverted_index.</bold>We | 77 |
| abstract_inverted_index.DCR-based | 166 |
| abstract_inverted_index.cognitive | 5, 29, 66, 100, 106, 133, 176, 203 |
| abstract_inverted_index.detection | 201 |
| abstract_inverted_index.different | 51 |
| abstract_inverted_index.efficient | 63, 200 |
| abstract_inverted_index.essential | 15 |
| abstract_inverted_index.including | 108 |
| abstract_inverted_index.lifestyle | 20 |
| abstract_inverted_index.outcomes. | 52 |
| abstract_inverted_index.pTau-181, | 128 |
| abstract_inverted_index.parallel, | 172 |
| abstract_inverted_index.pathology | 8 |
| abstract_inverted_index.potential | 56, 212 |
| abstract_inverted_index.providers | 220 |
| abstract_inverted_index.treatment | 227 |
| abstract_inverted_index.workflows | 60 |
| abstract_inverted_index.(AUC=0.85) | 178 |
| abstract_inverted_index.accurately | 141, 173 |
| abstract_inverted_index.assessment | 64 |
| abstract_inverted_index.associated | 9, 205 |
| abstract_inverted_index.biomarkers | 117 |
| abstract_inverted_index.classified | 132, 142, 155, 174 |
| abstract_inverted_index.decisions. | 228 |
| abstract_inverted_index.impairment | 6, 134, 177, 204 |
| abstract_inverted_index.likelihood | 70 |
| abstract_inverted_index.pathology. | 75, 208 |
| abstract_inverted_index.treatments | 26 |
| abstract_inverted_index.underlying | 74 |
| abstract_inverted_index.(AUC=0.83). | 182 |
| abstract_inverted_index.</bold>DCAs | 184 |
| abstract_inverted_index.Cognivue®, | 114 |
| abstract_inverted_index.assessments | 30 |
| abstract_inverted_index.blood-based | 116 |
| abstract_inverted_index.impairment, | 101 |
| abstract_inverted_index.multimodal, | 165 |
| abstract_inverted_index.traditional | 105 |
| abstract_inverted_index.</bold>Early | 2 |
| abstract_inverted_index.amyloid-beta | 95 |
| abstract_inverted_index.assessments, | 107 |
| abstract_inverted_index.concurrently | 49, 92 |
| abstract_inverted_index.participants | 120 |
| abstract_inverted_index.technologies | 188 |
| abstract_inverted_index.Alzheimer’s | 11 |
| abstract_inverted_index.interventions | 21 |
| abstract_inverted_index.pharmacologic | 24 |
| abstract_inverted_index.<bold>Methods: | 76 |
| abstract_inverted_index.<bold>Results: | 126 |
| abstract_inverted_index.Bio-Hermes-001 | 123 |
| abstract_inverted_index.classification | 168 |
| abstract_inverted_index.identification | 3 |
| abstract_inverted_index.respectively), | 139 |
| abstract_inverted_index.respectively). | 149, 162 |
| abstract_inverted_index.next-generation | 83 |
| abstract_inverted_index.</bold>Aβ42/40, | 127 |
| abstract_inverted_index.machine-learning | 167 |
| abstract_inverted_index.<bold>Background: | 1 |
| abstract_inverted_index.disease-modifying | 25 |
| abstract_inverted_index.<bold>Conclusions: | 183 |
| abstract_inverted_index.<italic>and</italic> | 179 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| abstract_inverted_index.<italic>and</italic>brain | 7 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.2576867 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |