In a study published in American Journal of Psychiatry, researchers from Dr. WANG Zheng’s Lab at the Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences (CAS), Dr. HE Ran’s group at the Institute of Automation of CAS, Dr. XU Xiu at Children’s hospital of Fudan University, and the collaborators, reported a novel cross-species translational approach for diagnostic classification in human mental disorders.

Autism spectrum disorder (ASD) represents a heterogeneous group of neurodevelopmental disorders and often occurs with co-morbidities such as obsessive-compulsive disorder (OCD) and attention deficit–hyperactivity disorder (ADHD). The substantial phenotypic heterogeneity and high frequency of co-morbidity present a formidable challenge for accurate diagnosis.

The success of new gene-editing technologies has stirred a growing interest in the use of nonhuman primates to investigate the biomechanisms of brain diseases. Animal models with a clear genetic basis provide valuable opportunities for probing the gene-circuit-behavior mechanisms, and promoting development and preclinical testing of new therapeutics.

Considering the evolutionarily conserved features of the brain connectome across primate species, researchers in this study proposed that a subset of brain regions that particularly relevant to the core neuropathology of autism are potentially useful for cross-species mapping in the present framework.

Thus, they identified a set of nine core regions based on functional connectome data in the monkey cohort by deploying a machine learning algorithm.

After one-to-one mapping of these core regions to the human brain, functional connections between them were extracted and tested in four independent human cohorts: Autism Brain Imaging Data Exchange (ABIDE)-I (N = 1112), ABIDE-II (N = 1114), ADHD (N = 776) and OCD (N = 186).

The sparse logistic regression classifiers were trained for discriminative classification of subjects in each individual cohort. After cross-validation, the monkey-derived classifier achieved an accuracy of 82.14% in the ABIDE-I cohort, and an accuracy of 75.17% in the ABIDE-II cohort, both of which significantly outperformed the human-derived classifiers.Moreover, the same set of core regions was useful for improved classification in the OCD cohort (78.36%), but not in the ADHD cohort.

Further analysis of these superior monkey-derived classifiers revealed that distinct connections from the ventral lateral prefrontal cortex predicts differential dimensional symptom severity of ASD and OCD, suggesting dual neuropathology-dependent mechanistic roles in these two diseases.

In short, specific brain regions or circuits derived from genetically-edited animal models may serve as a basis to define a biologically-defined circuit endophenotype cut across current diagnostic categories.