AI Tool Maps Genetic Blueprint of Brain's Communication Bridge, Unlocking Mental Health Insights

AI Maps the Brain's Hidden Bridge, Revealing Genetic Links to Mental Health - Neuroscience News

Summary: For the first time, scientists have mapped the genetic architecture of the brain's communication bridge -- the corpus callosum -- using AI and MRI data from over 50,000 people. The study uncovered dozens of genes that shape this vital structure's size and thickness, many active during prenatal development when the brain's wiring is established. Differences in these genes may explain why changes in the corpus callosum are associated with mental and neurological disorders. The team's open-source AI tool now enables global researchers to analyze brain structure faster and with unprecedented precision. For the first time, a research team led by the Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) at the Keck School of Medicine of USC has mapped the genetic architecture of a crucial part of the human brain known as the corpus callosum -- the thick band of nerve fibers that connects the brain's left and right hemispheres. The findings open new pathways for discoveries about mental illness, neurological disorders and other diseases related to defects in this part of the brain. The corpus callosum is critical for nearly everything the brain does, from coordinating the movement of our limbs in sync to integrating sights and sounds, to higher-order thinking and decision-making. Abnormalities in its shape and size have long been linked to disorders such as ADHD, bipolar disorder, and Parkinson's disease. Until now, the genetic underpinnings of this vital structure remained largely unknown. In the new study, published in Nature Communications, the team analyzed brain scans and genetic data from over 50,000 people, ranging from childhood to late adulthood, with the help of a new tool the team created that leverages artificial intelligence. "We developed an AI tool that finds the corpus callosum in different types of brain MRI scans and automatically takes its measurements," said Shruti P. Gadewar, co-first author of the study and research specialist at the Stevens INI. Using this tool, the researchers identified dozens of genetic regions that influence the size and thickness of the corpus callosum and its subregions. "These findings provide a genetic blueprint for one of the brain's most essential communication pathways," said Ravi R. Bhatt, PhD, co-first author of the study and a post-doctoral scholar at the Stevens INI's Imaging Genetics Center. "By uncovering how specific genes shape the corpus callosum and its subregions, we can start to understand why differences in this structure are linked to various mental health and neurological conditions at a molecular level." The study revealed that different sets of genes govern the area versus the thickness of the corpus callosum -- two features that change across the lifespan and play distinct roles in brain function. Several of the implicated genes are active during prenatal brain development, particularly in processes like cell growth, programmed cell death, and the wiring of nerve fibers across hemispheres. "This work demonstrates the power of using AI and large-scale databases to uncover the genetic factors driving brain development," said Neda Jahanshad, PhD, associate professor of neurology and senior author. "By linking genetics to brain structure, we gain critical insight into the biological pathways that may underlie psychiatric and neurological diseases." Notably, the study found genetic overlap between the corpus callosum and the cerebral cortex -- the outer layer of the brain responsible for memory, attention, and language -- as well as with conditions such as ADHD and bipolar disorder. "These connections underscore that the same genetic factors shaping the brain's communication bridge may also contribute to vulnerabilities for certain disorders," Jahanshad added. Arthur W. Toga, PhD, director of the Stevens INI, emphasized the broader implications of this research. "This study is a landmark in understanding how our brains are built. It not only sheds light on normal brain development but also helps us identify new avenues for diagnosing and potentially treating disorders that affect millions worldwide." The researchers have made their new AI-based tool publicly available to accelerate future discoveries. The software, developed at the Stevens INI, uses advanced machine learning to identify and measure the corpus callosum from MRI scans automatically. This approach allows scientists to analyze brain structure at an unprecedented scale and level of precision, reducing years of manual work to just hours. The Stevens INI has become a global leader in applying artificial intelligence to neuroscience, developing tools that are freely shared with the research community. By combining massive datasets with cutting-edge computational methods, the institute is transforming how scientists study brain health and disease. "Artificial intelligence is revolutionizing brain research, and Stevens INI is at the forefront of that revolution," said Toga. "By pioneering AI tools and making them widely available, we're empowering scientists around the world to unlock new discoveries about the brain far faster than ever before." About the study In addition to Bhatt, Gadewar and Jahanshad, the study's other authors include Ankush Shetty, Iyad Ba Gari, Elizabeth Haddad,Shayan Javid, Abhinaav Ramesh, Elnaz Nourollahimoghadam, Alyssa H. Zhu, Christiaan de Leeuw,Paul M. Thompson,and Sarah E. Medland. Funding: This work was supported by the National Institutes of Health (Grant Nos. R01 MH134004 and R01 AG059874 [NJ], National Science Foundation Graduate Research Fellowship Program (Grant No. 2020290241 [RRB], R01 MH126213, R01NS105746, the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), and UK Biobank (Resource Application No. 11559). SEM was supported by NHMRC grants APP1172917 and APP1158127. Research reported in this publication was supported by the Office of the Director, National Institutes of Health under Award Number S10OD032285. The Genetic Architecture of the Human Corpus Callosum and its Subregions The corpus callosum (CC) is the largest set of white matter fibers connecting the two hemispheres of the brain. In humans, it is essential for coordinating sensorimotor responses and performing associative or executive functions. Identifying which genetic variants underpin CC morphometry can provide molecular insights into the CC's role in mediating cognitive processes. We developed and used an artificial intelligence based tool to extract the midsagittal CC's total and regional area and thickness in two large public datasets. We performed a genome-wide association study (GWAS) meta-analysis of European participants (combined N = 46,685) with generalization to the non-European participants (combined N = 7040). Post-GWAS analyses implicated prenatal intracellular organization and cell growth patterns, and high heritability in regions of open chromatin. Results suggest programmed cell death mediated by the immune system drives the thinning of the posterior body and isthmus. Genetic overlap, and causal genetic liability, between the CC, cerebral cortex features, and neuropsychiatric disorders such as attention-deficit/hyperactivity, bipolar disorders, and Parkinson's disease were identified.

AI tool uncovers genetic blueprint of the brain's largest communication bridge

For the first time, a research team led by the Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) at the Keck School of Medicine of USC has mapped the genetic architecture of a crucial part of the human brain known as the corpus callosum -- the thick band of nerve fibers that connects the brain's left and right hemispheres. The findings open new pathways for discoveries about mental illness, neurological disorders and other diseases related to defects in this part of the brain. The corpus callosum is critical for nearly everything the brain does, from coordinating the movement of our limbs in sync to integrating sights and sounds, to higher-order thinking and decision-making. Abnormalities in its shape and size have long been linked to disorders such as ADHD, bipolar disorder, and Parkinson's disease. Until now, the genetic underpinnings of this vital structure had remained largely unknown. In the new study, published in Nature Communications, the team analyzed brain scans and genetic data from over 50,000 people, ranging from childhood to late adulthood, with the help of a new tool the team created that leverages artificial intelligence. "We developed an AI tool that finds the corpus callosum in different types of brain MRI scans and automatically takes its measurements," said Shruti P. Gadewar, co-first author of the study and research specialist at the Stevens INI. Using this tool, the researchers identified dozens of genetic regions that influence the size and thickness of the corpus callosum and its subregions. "These findings provide a genetic blueprint for one of the brain's most essential communication pathways," said Ravi R. Bhatt, Ph.D., co-first author of the study and a postdoctoral scholar at the Stevens INI's Imaging Genetics Center. "By uncovering how specific genes shape the corpus callosum and its subregions, we can start to understand why differences in this structure are linked to various mental health and neurological conditions at a molecular level." The study revealed that different sets of genes govern the area versus the thickness of the corpus callosum -- two features that change across the lifespan and play distinct roles in brain function. Several of the implicated genes are active during prenatal brain development, particularly in processes like cell growth, programmed cell death, and the wiring of nerve fibers across hemispheres. "This work demonstrates the power of using AI and large-scale databases to uncover the genetic factors driving brain development," said Neda Jahanshad, Ph.D., associate professor of neurology and senior author. "By linking genetics to brain structure, we gain critical insight into the biological pathways that may underlie psychiatric and neurological diseases." Notably, the study found genetic overlap between the corpus callosum and the cerebral cortex -- the outer layer of the brain responsible for memory, attention, and language -- as well as with conditions such as ADHD and bipolar disorder. "These connections underscore that the same genetic factors shaping the brain's communication bridge may also contribute to vulnerabilities for certain disorders," Jahanshad added. Arthur W. Toga, Ph.D., director of the Stevens INI, emphasized the broader implications of this research, stating, "This study is a landmark in understanding how our brains are built. It not only sheds light on normal brain development but also helps us identify new avenues for diagnosing and potentially treating disorders that affect millions worldwide." The researchers have made their new AI-based tool publicly available to accelerate future discoveries. The software, developed at the Stevens INI, uses advanced machine learning to identify and measure the corpus callosum from MRI scans automatically. This approach allows scientists to analyze brain structure at an unprecedented scale and level of precision, reducing years of manual work to just hours. The Stevens INI has become a global leader in applying artificial intelligence to neuroscience, developing tools that are freely shared with the research community. By combining massive datasets with cutting-edge computational methods, the institute is transforming how scientists study brain health and disease. "Artificial intelligence is revolutionizing brain research, and Stevens INI is at the forefront of that revolution," said Toga. "By pioneering AI tools and making them widely available, we're empowering scientists around the world to unlock new discoveries about the brain far faster than ever before."