The main aim of the LifespanHealth project was to improve our understanding of changes in the brain related to severe mental illness across the lifespan and to develop prediction tools based on brain imaging that may one day support clinical decision making.
How does mental illness develop in the brain of youths? Can we use brain imaging to detect mental illness at an early stage before the outbreak of severe symptoms? Can we predict the long-term outcome of those suffering from mental illness? Providing well-founded answers to questions like these is extremely important to improve clinical decision making, treatment and care and to transform today's psychiatry into a personalized medicine.
LifespanHealth utilized a big-data approach, where data was gathered from collaborators around the world. These rich data sets cover the full lifespan from childhood to old age and comprise data on brain structure and function, genes, environmental factors, cognition and clinical symptoms.
Results of the LifespanHealth project have shown that several common psychiatric disorders are associated with an apparent aging of the brain and that some of the same genes involved with brain aging in healthy individuals are also part of the genetics underlying the disorders. In addition, mapping the topological characteristics of the functional connectome revealed symptoms of depression and anxiety to be associated with altered information flow for visual, auditory, and sensorimotor brain networks.
LifespanHealth highlighted the benefit of a lifespan approach to brain imaging and genetics studies on mental health. The project contributed to delineating the genetic architecture of brain phenotypes relevant to mental health and developed new tools for utilizing genetic information in prediction frameworks. Further, it shed light into connectomic profiles associated with mental health across the lifespan. The results and tools will in the future be used for further studies and developments by project personnel and other researchers in the field.
Severe mental illness (SMI) accounts for about one third of all years lived-with-disabilities world-wide, with large unmet patient needs and substantial socio-economical costs. Improving our understanding of the pathophysiology of SMI is key in their treatment and prevention. Over the past decades, it has become increasingly clear that most SMIs originate in neurodevelopment and that the pathophysiology later in life may accelerate the ageing of the brain, yet these tremendous lifespan changes in the brain are largely understudied and the mechanisms remain elusive. The LifespanHealth project thus targets a lifespan perspective on SMI from delayed neurodevelopment to accelerated ageing. We aim at the discovery of biomarkers for early detection and prevention and at improving our understanding of the pathophysiology over the course of the illness, to allow for personalized treatment, prognosis and long-term care. The project PI has extensive experience in multivariate statistical analysis of brain imaging data and has formed an excellent, multidisciplinary team of researchers with access to the largest brain imaging genetics cohorts available to date. We will explore this rich set of data using novel biostatistical tools. Discovered biomarkers will be incorporated into multivariate statistical prediction models targeting biology-informed diagnosis scores, usable as an additional information channel for health care professionals. The unique data set, bundled expertise and truly interdisciplinary efforts of LifespanHealth yield an unprecedented opportunity to bridge the gap between neuroscientific core research and clinical utility. LifespanHealth therefore maps well into the global efforts toward predictive clinical methods in translational psychiatry, integrating multimodal estimates of brain structure and function, their genetic underpinnings and environmental factors into a novel precision medicine utility.