Biology subjects

Khanbagi, M.

Publications and source records attributed to Khanbagi, M..

4 recordsLinked to original sources

bioRxiv2021

Validity and cultural generalisability of a 5-minute AI-based, computerised cognitive assessment in Mild Cognitive Impairment and Alzheimer's Dementia

INTRODUCTIONEarly detection and monitoring of mild cognitive impairment (MCI) and Alzheimers Disease (AD) patients are key to tackling dementia and providing benefits to patients, caregivers, healthcare providers and society. We developed the Integrated Cognitive Assessment (ICA); a 5-minute, language independent computerised cognitive test that employs an Artificial Intelligence (AI) model to improve its accuracy in detecting cognitive impairment. In this study, we aimed to evaluate the generalisability of the ICA in detecting cognitive impairment in MCI and mild AD patients. METHODSWe studied the ICA in a total of 230 participants. 95 healthy volunteers, 80 MCI, and 55 participants with mild AD completed the ICA, the Montreal Cognitive Assessment (MoCA) and Addenbrookes Cognitive Examination (ACE) cognitive tests. RESULTSThe ICA demonstrated convergent validity with MoCA (Pearson r = 0.58, p<0.0001) and ACE (r = 0.62, p<0.0001). The ICA AI model was able to detect cognitive impairment with an AUC of 81% for MCI patients, and 88% for mild AD patients. The AI model demonstrated improved performance with increased training data and showed generalisability in performance from one population to another. The ICA correlation of 0.17 (p=0.01) with education years is considerably smaller than that of MoCA (r=0.34, p<0.0001) and ACE (r=0.41, p<0.0001) which displayed significant correlations. In a separate study the ICA demonstrated no significant practice effect observed over the duration of the study. DISCUSSIONThe ICA can support clinicians by aiding accurate diagnosis of MCI and AD and is appropriate for large-scale screening of cognitive impairment. The ICA is unbiased by differences in language, culture and education.

neuroscience↗

bioRxiv2020

Temporal dynamics of animacy categorization in the brain of patients with mild cognitive impairment

Electroencephalography (EEG) has been commonly used to measure brain alterations in Alzheimers Disease (AD). However, reported changes are limited to those obtained from using univariate measures, including activation level and frequency bands. To look beyond the activation level, we used multivariate pattern analysis (MVPA) to extract patterns of information from EEG responses to images in an animacy categorization task. Comparing healthy controls (HC) with patients with mild cognitive impairment (MCI), we found that the neural speed of animacy information processing is decreased in MCI patients. Moreover, we found critical time-points during which the representational pattern of animacy for MCI patients was significantly discriminable from that of HC, while the activation level remained unchanged. Together, these results suggest that the speed and pattern of animacy information processing provide clinically useful information as a potential biomarker for detecting early changes in MCI and AD patients.

neuroscience↗

bioRxiv2019

A self-administered, artificial intelligence (AI) platform for cognitive assessment in multiple sclerosis (MS)

BackgroundCognitive 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. 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.\n\nObjectiveTo determine ICAs validity as a digital biomarker for assessing cognitive performance in MS.\n\nMethods91 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 ICAs 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.\n\nResultsICA 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.\n\nConclusionsICA can 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.

neuroscience↗

bioRxiv2018

Integrated Cognitive Assessment: Speed and Accuracy of Visual Processing as a Reliable Proxy to Cognitive Performance

Various mental disorders are accompanied by some degree of cognitive impairment. Particularly in neurodegenerative disorders, cognitive impairment is the phenotypical hallmark of the disease. Effective, accurate and timely cognitive assessment is key to early diagnosis of this family of mental disorders. Current standard-of-care techniques for cognitive assessment are primarily paper-based, and need to be administered by a healthcare professional; they are additionally language and education-dependent and typically suffer from a learning bias. These tests are thus not ideal for large-scale pro-active cognitive screening and disease progression monitoring. We developed the Integrated Cognitive Assessment (ICA), a 5-minute computerized cognitive assessment tool based on a rapid visual categorization task, in which a series of carefully selected natural images of varied difficulty are presented to participants. Overall 448 participants, across a wide age-range with different levels of education took the ICA test. We compared participants ICA test results with a variety of standard pen-and-paper tests that are routinely used to assess cognitive performance. ICA had excellent test-retest reliability, and was significantly correlated with all the reference cognitive tests used here, demonstrating ICAs ability as one unified test that can assess various cognitive domains.

neuroscience↗