Preprint AI-based decoding of long covid cognitive impairments in mice using automated behavioral system and comparative transcriptomic analysis, 2025, Amer+

AI-based decoding of long covid cognitive impairments in mice using automated behavioral system and comparative transcriptomic analysis
Heba M Amer; Mohamed M Shamseldin; Sarah Faber; Mostafa Eltobgy; Amy Webb; Rabab El-Mergawy; Michelle Chamblee; Richard Perez; Owen Whitham; Asmaa Badr; Gauruv Gupta; Jihad Omran; Destiny Bissel; Jacob Yount; Estelle Cormet-Boyaka; Prosper N Boyaka; Jinarong Li; Xiaoli Zhang; Mark E Peeples; Mahesh KC; Maciej Pietrzak; Stephanie Seveau; Olga Kokiko-Cochran; Magdi Amer; Ruth M Barrientos; Andrew Schamess; Eugene Oltz; Amal O Amer

Long COVID (LC) following SARS-CoV-2 infection affects millions of individuals world-wide and manifests with a variety of symptoms including cognitive dysfunction also known as brain fog. This is characterized by difficulties in executive functions, planning, decision-making, working memory, impairments in complex attention, loss of ability to learn new skills and perform sophisticated brain tasks. No effective treatment options currently exist for LC-related cognitive dysfunction.

Here, we use the IntelliCage, which is an automated tracking system of cognitive functions, following SARS-CoV-2 infection in mice, measuring the ability of each mouse within a group to perform tasks that mimic complex human behaviors, such as planning, decision-making, cognitive flexibility, and working memory.

Artificial intelligence and machine learning analyses of the tracking data classified LC mice into distinct behavioral categories from non-infected control mice, permitting precise identification and quantification of complex cognitive dysfunction in a controlled, replicable manner. Importantly, we find that brains from LC mice with cognitive dysfunction exhibit transcriptomic alterations similar to those observed in humans suffering from LC-related cognitive impairments, including altered expression of genes involved in learning, executive functions, synaptic functions, neurotransmitters and memory.

Together, our findings establish a validated murine model and an automated unbiased approach to study LC-related cognitive dysfunction for the first time, and providing a valuable tool for screening potential treatments and therapeutic interventions.

Link | PDF (Preprint: BioRxiv) [Open Access]

 

In mice.

 

Also published today is this other paper, unfortunately in the European Journal of Neurology. But apparently the above mice are merely depressed and overestimating their cognitive dysfunction (and epigenetic changes, complement and coagulation dysregulation etc).

Post-COVID-Syndrome Patients Might Overestimate Own Cognitive Impairment

BACKGROUND
After a COVID-19 infection, some patients experience long-term consequences known as Post-Covid Syndrome, which often includes cognitive impairment. We investigated the congruence between subjectively experienced and objectively measured cognitive deficits after a COVID-19 infection in an unselected, successively admitted cohort of 46 patients reporting subjective cognitive complaints (SCC).

METHODS
We employed a comprehensive neuropsychological test battery to assess objective cognitive impairment across various cognitive domains. Three different cut-off criteria were applied, commonly used in the literature to define objective neurocognitive disorder (NCD).

RESULTS
We observed a notably low congruence between SCC and NCD in Post-Covid Syndrome, regardless of the cut-off criterion. Depending on the cognitive domain, only 4% to maximally 40% of the SCC could be objectified.

CONCLUSIONS
One possible explanation for this discrepancy could be the high rate of depressive symptoms observed in the group of patients studied, which may negatively influence the perception of one's cognitive abilities. These findings emphasize the need for careful evaluation of SCC in Post-Covid Syndrome and suggest that treating depressive symptoms may also alleviate some of the perceived cognitive deficits.