State-of-the-art Imaging Biomarkers
for Neuropsychiatric Diseases

QMENTA has amassed the most advanced and widely used imaging biomarkers on its platform to enable experts to measure the efficacy of treatments under trial or research in a single environment.

Imaging Biomarkers

Neuropsychiatric Diseases

Advance neuroimage analysis techniques can be used to obtain biomarkers from functional and structural brain images, which leads to a better understanding of psychiatric disorders by measuring and evaluating specific characteristics of the brain, such as volume information from brain regions or connectivity information between areas.

Patterns of functional connectivity


This tool based on resting state functional connectivity, can give insights on how different regions of the brain are functionally correlated. It can be useful to measure how certain diseases might disrupt or affect the exchange of information between specific regions or how certain functional networks might be disrupted.(26, 27, 28)

Grey matter volume and thickness


These tools measure brain morphology and volumes of different regions. It can be used to study whether a particular psychiatric disease may target specific brain regions (or their connections which might also result in a change on their morphology).(29)

Patterns of Whole Brain Structural Connectivity


Graph theoretical analysis of structural connectivity MRI data has provided new measures of human brain organization in vivo. Degree distribution of brain networks demonstrates highly connected hubs that are crucial for the whole network functioning, such topological definition brings new insights into a better understanding of pathophysiology of many psychiatric diseases affecting specific local or global brain networks such as schizophrenia.(30, 31, 32)
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26. Lynall, Mary-Ellen, et al. "Functional connectivity and brain networks in schizophrenia." Journal of Neuroscience 30.28 (2010): 9477-9487.
27. Harrison, Ben J., et al. "Altered corticostriatal functional connectivity in obsessive-compulsive disorder." Archives of general psychiatry 66.11 (2009): 1189-1200.
28. Sheline, Yvette I., et al. "Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus." Proceedings of the National Academy of Sciences 107.24 (2010): 11020-11025.
29. Moradi, Elaheh, et al. "Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data." NeuroImage 144 (2017): 128-141.
30. Guye, Maxime, et al. "Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks." Magnetic Resonance Materials in Physics, Biology and Medicine 23.5-6 (2010): 409-421.
31. Ameis, Stephanie H., et al. "Impaired structural connectivity of socio-emotional circuits in autism spectrum disorders: a diffusion tensor imaging study." PloS one 6.11 (2011): e28044.
32. Cao, Qingjiu, et al. "Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder." Journal of Neuroscience 33.26 (2013): 10676-10687.