Research Projects by Hadi Hosseini and the C-BRAIN Lab
The Computational Brain Research and Intervention (C-BRAIN) Lab at Stanford University, led by Hadi Hosseini, PhD, develops computational and neuroimaging approaches to understand brain disorders and create personalized interventions. Current research spans Alzheimer's disease, computational neuropsychiatry, connectome biomarkers, wearable fNIRS, ADHD, cognitive augmentation, and neurointervention.
Decoding the heterogeneity of Alzheimer's disease diagnosis and progression
With recent advances in neurobiology of Alzheimer’s disease (AD), our understanding of the disease has moved from one based on clinical symptoms to a biological construct that is multifactorial and heterogeneous. Leveraging existing AD-related datasets, we are developing a multi-dimensional network framework to aggregate data across modalities, to capture the heterogeneity of AD and to enhance accurate and early AD detection and progression.
Predicting AD progression in preclinical Alzheimer's disease
Alzheimer's disease (AD) pathology starts to develop in the brain decades before the clinical symptoms appear. However, not all of the older adults with Alzheimer's pathology would develop clinical symptoms. We integrate state of the art computational models and biomarker data to predict the development and progression of AD in older adults who are otherwise cognitively normal.
Connectome Markers Among Neuropsychiatric Disorders
Psychiatric conditions have traditionally been defined around behavioral symptoms that are often imprecise. With advances in brain imaging over the past decades, our understanding of brain structure and function have drastically improved. The Hadi Hosseini research group is one of the pioneers of connectome research in psychiatry by focusing on identifying unqiue connectome biomarkers of psychiatric conditions and movement towards redefining mental disorders based on brain networks pathology.
Neuromonitoring-Guided Cognitive Augmentation For ADHD
Our translational neuropsychiatry research involves developing noninvasive, brain-focused, personalized interventions that target affected brain networks in an organic way. We are developing a novel neuro-monitoring guided cognitive augmentation integrating computerized cognitive training with real-time, functional neuromonitoring for targeted enhancement of brain networks underlying executive functions in children with ADHD. These studies have been sponsored by National Institute of Mental Health, BBRF, and Stanford MCHRI. See related work on our publications page.
Neural Correlates of Cognitive Augmentation in MCI
Mild Cognitive Impairment is one factor increasing the risk for later development of Alzheimer's Disease. In a study funded by National Institute of Aging, we are utilizing neuropsychological assessments in conjunction with novel neuroimaging and computational techniques to study the effects of long-term, multi-domain, computerized cognitive training on brain networks in older adults with MCI. We are also examining factors influencing the response to cognitive training in order to optimize the training based on individual's brain and behavior profile.
Developing Wearable Neuroimaging Systems for Brain Imaging at Home
Our neuro-technology research is focused on developing and validating a consumer-grade, cost-effective functional brain imaging system that is wearable, wireless, smart-phone operated, and fun-to-use for personal and population-based neuroimaging and neuro-monitoring. Noninvasive, optical imaging technology is adopted to develop a system that is robust to motion and physiological artefacts and can be reliably used at home on a daily basis.
Multimodal cognitive training for improving brain networks in mild cognitive impairment
We have been developing computerized cognitive intervention protocols for training multiple cognitive domains inclduing memory, executive functioning, and processing speed in parallel in older adults with and without mild cognitive impairment and have been utilizing leading-edge MRI measures of brain microstruture to quantify the effect of these interventions on brain networks and to inform the development of brain-focused cognitive enhancement protocols for older adults.