Neural basis of schizophrenia and bipolar disorder found in patient-derived brain organoids

Good news!

They even used support vector machines for this! SVMs are very early, old fashioned machine learning models.

"Pea-sized brains grown in a lab have for the first time revealed the unique way neurons might misfire due to schizophrenia and bipolar disorder, psychiatric ailments that affect millions of people worldwide but are difficult to diagnose because of the lack of understanding of their molecular basis. ...

"Schizophrenia and bipolar disorder are very hard to diagnose because no particular part of the brain goes off. No specific enzymes are going off like in Parkinson's ...

"Our hope is that in the future we can not only confirm a patient is schizophrenic or bipolar from brain organoids, but that we can also start testing drugs on the organoids to find out what drug concentrations might help them get to a healthy state." ..."

From the abstract:

"Neuropsychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BPD) remain challenging to diagnose due to the absence of objective biomarkers, with current assessments relying largely on subjective clinical evaluations.

In this study, we present a computational analysis pipeline designed to identify disease-specific electrophysiological signatures from multi-electrode array (MEA) recordings of patient-derived cerebral organoids (COs) and two-dimensional cortical interneuron cultures (2DNs).

Using a Support Vector Machine classifier optimized for high-dimensional data, we achieved 95.8% classification accuracy in distinguishing SCZ from control samples in 2DNs under both baseline and post-electrical-stimulation (PES) conditions with the extracted electrophysiological signatures.

In COs, classification accuracy improved from 83.3% at baseline to 91.6% following PES, enabling robust separation of control, SCZ, and BPD cohorts.

Key discriminative features included channel-specific measures of network activity, with PES significantly enhancing classification performance, particularly for BPD.

These results underscore the potential of MEA-based functional phenotyping, coupled with machine learning, to uncover reliable, stimulation-sensitive electrophysiological biomarkers, offering a path toward more objective diagnosis and personalized treatment strategies for neuropsychiatric disorders."

Neural basis of schizophrenia and bipolar disorder found in brain organoids | Hub "Using machine learning, Johns Hopkins researchers identified healthy and unhealthy patterns based on electrical activity"

Machine learning-enabled detection of electrophysiological signatures in iPSC-derived models of schizophrenia and bipolar disorder (open access)

FIG. 1.The workflow of the proposed analysis pipeline was designed from EEG analysis to uncover distinct electrophysiological signatures associated with schizophrenia (SCZ) and bipolar disorder (BPD).