One of the key missions of the study of brain imaging data is to be able to read into the landscape of the brain itself, and literally see whether or not it has fundamental weaknesses that might lead to brain diseases. A new article published in Science Magazine and co-authored by several HCP Investigators — Steve Petersen, Deanna Barch, Jonathan Power, Gagan Wig, and led by Brad Schlaggar — tries to advance this field by asking: can a single fMRI scan give us enough information to classify and make predictions about that individual?
The work described here had two major objectives. The first aim was to develop an approach for making accurate predictions about individuals on the basis of single fMRI scans. The second aim, building on the first, was to further illuminate typical brain development, a prerequisite for studying developmental disorders and pediatric-onset neuropsychiatric diseases (2, 3).
These researchers used multivariate pattern analysis tools (MVPA), a successfully established method of parsing brain activity during task-based activities: mapping the process of memory retrieval, or understanding how the mind associates meaning with nouns.
However, they faced an interesting challenge when working with a pre-adolescent subject group — the unpredictablity of task performance — and are pushing the envelope of MVPA by applying it to resting-state functional connectivity MRI (rs-fcMRI), which can be gathered quickly and easily during times of rest.
The visual results of this pattern analysis are both striking and informative, as the researchers use functional connectivity data to determine measure the subject’s “brain age,” and to chart the maturation process from birth to full adulthood (age 30). This type of data visualization (Fig 1) allows researchers to characterize the typical trajectory of maturation as a biological growth curve.
However, a new view of brain activity emerges if we map the functional connectivity MVPA data over the actual landscape of the brain itself (Fig 2), and we get to see first-hand how the brain changes functionally as we mature.
Read the full article: Science Magazine: Prediction of Individual Brain Maturity Using fMRI
The member universities of the Human Connectome Project take privacy very seriously, whether dealing with participant data or the data of those visiting this website.
The participant data from our research into the Human Connectome that is stored in our XNAT server is de-identified, and contains no personal health information (PHI).
Our website collects names and email addresses via our contact form. This information is used solely by the administrators and members of the HCP website and is not shared, traded or sold to third parties under any circumstances.
Our website may also collect non-personal data about site visits, sessions, and IP addresses. This information is only used for diagnostic or debugging purposes, to help us optimize our website's performance, and is not shared externally. This is a standard practice for most websites, and this data is never linked with personally identifiable information.
This website contains links to other websites whose content we think is relevant. However, the HCP website is not responsible for maintaining or updating the content of these other sites. If any of these sites are found to contain irrelevant or offensive information, please contact us.
By using humanconnectome.org, you signify your agreement to our privacy policy as stated above. Note that this policy may be revised periodically without notice. Please re-read this policy prior to submitting any personal information if you have concerns about how your information is being collected and used.