📖 Read the full paper here →

🧠 We are now adding more cognitive domains to the assessment and testing the algorithm in a large group of individuals. Interested in building with us? Please reach out!.

📈 We used our Flow2 measurement system to record brain data during language and memory tasks, and built a machine learning classifier to assign each participant a single score encapsulating their likelihood of having a current MCI diagnosis.

✍️ Kernel just published a study in NPJ Dementia (@NaturePortfolio) demonstrating the ability to distinguish MCI patients from healthy age-matched controls. Using 15 minutes of brain scans, behavioral data, and a short survey, we were able to classify MCI with an AUC=0.92!

RT @bryan_johnson: You can now learn your BrainAge with a 7 minute brain scan. Pretty cool moment. I've spent the past 10 years building….

🧠Chronological age is out. BrainAge is in. Measure your BrainAge in just 7 minutes with Kernel’s advanced neuroimaging system. 📍 Now available in Los Angeles (more locations coming soon). ✨ Limited-time early bird offer available - $24.99. 🔗

You can catch Kernel's latest poster at the @APApsychiatric Annual Meeting in LA this weekend!. 🧠 Developing a Treatment Decision-Making Aid for Major Depressive Disorder Using Functional Brain Measures. 📅 Sat 5/17, 1:30-3pm. 📍 Exhibit Hall, LACC. #APAAM25

🇨🇦 If you’re planning to be in Toronto for @SOBP's annual meeting, be sure to stop by Kernel’s poster session!. 🧠 We will be presenting the results of our PREDICT study (Prediction of REsponse to #Depression Interventions Using Clinical and TD-fNIRS Measurements). #SOBP2025

RT @mitpress: Newly published in @ImagingNeurosci, a compact time-domain diffuse optical tomography system for cortical neuroimaging: https….

✍️ Kernel published a study in Imaging Neuroscience from @mitpress that describes our Flow2 technology for measuring cortical brain metrics. The combination of good system performance, low cost, and easy-to-use form factor of Flow2 is unprecedented.

Looking back to late 2024, #HLTH reminded us to “BE BOLD.” Ryan Field sat down with @LoganPlaster to talk about how @KernelCo is doing just that. Thank you to @startuphealth for sharing our story! 📺

ICYMI.🧠 Read here → .📥 Subscribe here →

ICYMI: Kernel’s Q3 Recap. 🧠 Read here → 📥 Subscribe here →

Stay tuned for more exciting news from @KernelCo . Special thanks to co-authors: @neurexplorer, J. Duffy, @RyanMField, @ErinMKoch, @Zahra_M_Aghajan, N. Miller, K. Perdue, G. Sahagian, M. Taylor.

Moving forward, we hope this approach can be used to help patients with MCI get the care they need to preserve their cognition by providing quantitative and timely assessments of brain functioning. Early identification of MCI will support the fight to end Alzheimer's! #ENDALZ

Our machine learning model for classifying if an individual is likely to have MCI had excellent performance with 85% accuracy, 80% sensitivity and 90% specificity. This is better performance than current standard screening tools used in clinical practice.

We combine patterns of brain activation with the task performance and a simple questionnaire into a machine learning model that classifies subjects as either being healthy or having MCI.

However, to be clinically relevant, we need to be looking at individuals and not groups. This is where machine learning comes in!

We also found that there were differences in brain activity between the two groups during both the memory (a) and language (b) challenges. Red regions indicate more brain activity in the HC group while blue indicates more in the MCI group.

We found for both the memory and language challenges, performance was different between the MCI and HC groups. This makes sense because cognitive testing performance is commonly used to diagnose MCI.