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Research article
Seyed-Mahdi Khaligh-Razavi
Cognetivity Neurosciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5700-1704
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background Cognitive impairment is common in patients with MS. Accurate and repeatable measures of cognition have the potential to be used as a marker of disease activity. Methods We developed a 5-minute computerized test to measure cognitive dysfunction in patients with MS. The proposed test –named Integrated Cognitive Assessment (ICA)– is self-administered and language-independent. 91 MS patients and 83 healthy controls (HC) took part in substudy 1, in which each participant took the ICA test and the Brief International Cognitive Assessment for MS (BICAMS). We assessed ICA’s test-retest reliability, its correlation with BICAMS, its sensitivity to discriminate patients with MS from the HC group, and its accuracy in detecting cognitive dysfunction. In substudy 2, we recruited 48 MS patients, and examined the association between the level of serum neurofilament light (NfL) in these patients and their ICA scores.
Results ICA demonstrated excellent test-retest reliability (r=0.94), with no learning bias (i.e. no significant practice effect); and had high level of convergent validity with BICAMS. ICA was sensitive in discriminating the MS patients from the HC group, and demonstrated a high accuracy (AUC = 95%) in discriminating cognitively normal from cognitively impaired participants. Additionally, we found a strong association (r=-0.79) between ICA score and the level of NfL in MS patients.
Conclusions ICA has the potential to be used as a digital biomarker for assessment and monitoring of cognitive performance in MS patients. In comparison to standard cognitive tools for MS (e.g. BICAMS), ICA is shorter in duration, does not show a learning bias, is independent of language, and takes advantage of artificial intelligence (AI) to identify cognitive status of patients more accurately. Being a digital test, it further has the potential for easier electronic health record or research database integration.
Keywords
Multiple sclerosis, BICAMS, digital biomarkers, Integrated Cognitive Assessment (ICA), language-independent, Artificial Intelligence (AI)
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CURRENT STATUS: Published
View the published article at BMC Neurology.
No community comments so far
Editorial decision: Accept
On 17 Apr, 2020
No comments provided
Editorial decision: Minor revision
On 15 Apr, 2020
Editor assigned
On 14 Apr, 2020
Submission checks complete
On 13 Apr, 2020
Editor invited
On 13 Apr, 2020
No comments provided
Editorial decision: Minor revision
On 07 Apr, 2020
Review #1 received
Received 10 Feb, 2020
Reviewers invited
Invitations sent on 05 Feb, 2020
Reviewer #1 agreed
On 05 Feb, 2020
Editor assigned
On 04 Feb, 2020
Submission checks complete
On 03 Feb, 2020
Editor invited
On 03 Feb, 2020
Version 2
Posted 04 Dec, 2019
No comments provided
Editorial decision: Major revision
On 16 Dec, 2019
Review #1 received
Received 15 Dec, 2019
Reviewer #2 agreed
On 03 Dec, 2019
Review #2 received
Received 03 Dec, 2019
Reviewer #1 agreed
On 02 Dec, 2019
Reviewers invited
Invitations sent on 28 Nov, 2019
Editor assigned
On 27 Nov, 2019
Submission checks complete
On 26 Nov, 2019
Editor invited
On 26 Nov, 2019
No comments provided
Review #2 received
Received 14 Oct, 2019
Editorial decision: Major revision
On 14 Oct, 2019
Reviewer #2 agreed
On 02 Oct, 2019
Review #1 received
Received 25 Jul, 2019
Reviewer #1 agreed
On 10 Jul, 2019
Reviewers invited
Invitations sent on 09 Jul, 2019
Submission checks complete
On 24 Jun, 2019
Editor invited
On 24 Jun, 2019
Editor assigned
On 24 Jun, 2019
Metrics
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Subject Areas
Neurology
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A new preprint design is coming!
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See the published version of this preprint at BMC Neurology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Seyed-Mahdi Khaligh-Razavi
Cognetivity Neurosciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5700-1704
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Neurology.
No community comments so far
Editorial decision: Accept
On 17 Apr, 2020
No comments provided
Editorial decision: Minor revision
On 15 Apr, 2020
Editor assigned
On 14 Apr, 2020
Submission checks complete
On 13 Apr, 2020
Editor invited
On 13 Apr, 2020
No comments provided
Editorial decision: Minor revision
On 07 Apr, 2020
Review #1 received
Received 10 Feb, 2020
Reviewers invited
Invitations sent on 05 Feb, 2020
Reviewer #1 agreed
On 05 Feb, 2020
Editor assigned
On 04 Feb, 2020
Submission checks complete
On 03 Feb, 2020
Editor invited
On 03 Feb, 2020
Version 2
Posted 04 Dec, 2019
No comments provided
Editorial decision: Major revision
On 16 Dec, 2019
Review #1 received
Received 15 Dec, 2019
Reviewer #2 agreed
On 03 Dec, 2019
Review #2 received
Received 03 Dec, 2019
Reviewer #1 agreed
On 02 Dec, 2019
Reviewers invited
Invitations sent on 28 Nov, 2019
Editor assigned
On 27 Nov, 2019
Submission checks complete
On 26 Nov, 2019
Editor invited
On 26 Nov, 2019
No comments provided
Review #2 received
Received 14 Oct, 2019
Editorial decision: Major revision
On 14 Oct, 2019
Reviewer #2 agreed
On 02 Oct, 2019
Review #1 received
Received 25 Jul, 2019
Reviewer #1 agreed
On 10 Jul, 2019
Reviewers invited
Invitations sent on 09 Jul, 2019
Submission checks complete
On 24 Jun, 2019
Editor invited
On 24 Jun, 2019
Editor assigned
On 24 Jun, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Neurology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Neurology.
Background Cognitive impairment is common in patients with MS. Accurate and repeatable measures of cognition have the potential to be used as a marker of disease activity. Methods We developed a 5-minute computerized test to measure cognitive dysfunction in patients with MS. The proposed test –named Integrated Cognitive Assessment (ICA)– is self-administered and language-independent. 91 MS patients and 83 healthy controls (HC) took part in substudy 1, in which each participant took the ICA test and the Brief International Cognitive Assessment for MS (BICAMS). We assessed ICA’s test-retest reliability, its correlation with BICAMS, its sensitivity to discriminate patients with MS from the HC group, and its accuracy in detecting cognitive dysfunction. In substudy 2, we recruited 48 MS patients, and examined the association between the level of serum neurofilament light (NfL) in these patients and their ICA scores.
Results ICA demonstrated excellent test-retest reliability (r=0.94), with no learning bias (i.e. no significant practice effect); and had high level of convergent validity with BICAMS. ICA was sensitive in discriminating the MS patients from the HC group, and demonstrated a high accuracy (AUC = 95%) in discriminating cognitively normal from cognitively impaired participants. Additionally, we found a strong association (r=-0.79) between ICA score and the level of NfL in MS patients.
Conclusions ICA has the potential to be used as a digital biomarker for assessment and monitoring of cognitive performance in MS patients. In comparison to standard cognitive tools for MS (e.g. BICAMS), ICA is shorter in duration, does not show a learning bias, is independent of language, and takes advantage of artificial intelligence (AI) to identify cognitive status of patients more accurately. Being a digital test, it further has the potential for easier electronic health record or research database integration.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
This is a list of supplementary files associated with this preprint. Click to download.
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