Nico U.F. Dosenbach, M.D., Ph.D.

Assistant Professor
Neurology

Neurosciences Program

Research Abstract:

The goal of my research is to better understand functional network plasticity in the human brain, in order to improve rehabilitative treatments for patients with brain injuries. To this end, we are studying children with brain injury, as well as the effects of rehabilitative interventions using structural MRI, task-based functional MRI (fMRI) and resting state functional connectivity MRI (rs-fcMRI). We are focused on pushing functional MRI acquisition and analysis methodology to the level of individual patients. In order to create and annotate the connectomes of individuals (‘Individuomes’) we must first improve the signal-to-noise, spatial resolution and replicability of fMRI and rs-fcMRI data. High-fidelity ‘Individuomes’ are a requirement for rs-fcMRI based diagnostics and rs-fcMRI guided individualized rehabilitative treatments.
One of the main complications for rs-fcMRI and fMRI is excessive head movement, which introduces systematic distortions. Post-hoc data censoring methods can remove movement artifact from rs-fcMRI data, but post-hoc censoring leads to extremely high data loss rates. In order to decrease the time and resources required for MRI, we have developed a real-time head motion monitoring system called FIRMM (fMRI Integrated Real-time Motion Monitor) in collaboration with Oregon Health Sciences University. FIRMM allows scanner operators to adjust the scanning paradigm based on a participant’s head movement. FIRMM can also be used to train participants to keep their heads still while in the MRI scanner, using real-time feedback.
We are also working on custom data-acquisition, spatial registration and statistical approaches for single subject analyses. MRI processing pipelines tailor-made for single subjects will further improve our ability to characterize functional brain networks in individuals.
In order to further improve treatments for, we must find ways to relate underlying brain network changes to changes in behavior. Traditional means for assessing motor outcomes in children are often biased, time-consuming and unreliable. Therefore, I have started to utilize wearable biosensors (accelerometers) that allow us to accurately measure limb movement in patients with brain injury.
We have been conducting longitudinal, multimodal MRI research in children with hemiplegia undergoing Constraint-Induced Movement Therapy (CIMT), as well as healthy volunteers undergoing similar interventions. These studies have benefited from our recent methodological advances in motion monitoring, single-subject analyses and accelerometry. Therefore, we are now in a position to apply these novel approaches to the creation of those ‘Individuomes’ that can teach us the most about successful functional network plasticity. Adult survivors of large perinatal strokes with largely preserved sensory, cognitive and motor abilities represent the outer limits of beneficial network reorganization. Thus, we will be accruing the ‘Individuomes’ of perinatal stroke survivors to compare to each other and an existing cohort of healthy young adult ‘Individuomes’ we have already collected.

Selected Publications:

Laumann TO, Gordon EM, Adeyemo B, Snyder AZ, Joo SJ, Chen MY, Gilmore AW, McDermott KB, Nelson SM, Dosenbach NUF, Schlaggar BL, Mumford JA, Poldrack RA, Petersen SE. Functional system and areal organization of a highly sampled individual human brain. Neuron. 2015;5;87(3):657-70.

Pruett JR, Kandala S, Hoertel S, Snyder AZ, Elison JT, Nishino T, Feczko E, Dosenbach NUF, Nardos B, Power JD, Adeyemo B, Botteron KN, McKinstry RC Evans EC, Hazlett HC, Dager SR, Paterson S, Schultz RT, Collins DL, Fonov VS, Styner M, Gerig G, Das S, Kostopoulos P, Constantino JN, Estes AM, The IBIS Network; Petersen SE, Schlaggar BL, Piven J. Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data. Dev Cogn Neurosci. 2015;12(4):123-133.

Neta, M, Miezin FM, Nelson SM, Dubis JW, Dosenbach NUF, Schlaggar BL, Petersen SE. Spatial and Temporal Characteristics of Error-Related Activity in the Human Brain. J Neurosci. 2015;35(1)253-66.

Dosenbach NUF, Nardos B, Cohen AL, Fair DA, Power JD, Church JA, Nelson SM, Wig GS, Vogel AC, Lessov-Schlaggar CN, Barnes KA, Dubis JW, Feczko E, Coalson RS, Pruett JR, Jr., Barch DM, Petersen SE, and Schlaggar BL. Prediction of individual brain maturity using fMRI. Science. 2010;329(5997):1358-61.

Fair DA, Cohen AL, Power JD, Dosenbach NUF, Church JA, Miezin FM, Schlaggar BL, and Petersen SE. Functional brain networks develop from a "local to distributed" organization. PLoS Comput Biol. 2009;5(5):e1000381.

Church JA, Fair DA, Dosenbach NUF, Cohen AL, Miezin FM, Petersen SE, and Schlaggar BL. Control networks in paediatric Tourette syndrome show immature and anomalous patterns of functional connectivity. Brain. 2009;132(Pt 1):225-38.

Fair DA, Cohen AL, Dosenbach NUF, Church JA, Miezin FM, Barch DM, Raichle ME, Petersen SE, and Schlaggar BL. The maturing architecture of the brain`s default network. Proc Natl Acad Sci U S A. 2008;105(10):4028-32.

Dosenbach NUF, Fair DA, Cohen AL, Schlaggar BL, and Petersen SE. A dual-networks architecture of top-down control. Trends Cogn Sci. 2008;12(3):99-105.

Fair DA, Dosenbach NUF, Church JA, Cohen AL, Brahmbhatt S, Miezin FM, Barch DM, Raichle ME, Petersen SE, and Schlaggar BL. Development of distinct control networks through segregation and integration. Proc Natl Acad Sci U S A. 2007;104(33):13507-12.

Dosenbach NUF, Fair DA, Miezin FM, Cohen AL, Wenger KK, Dosenbach RA, Fox MD, Snyder AZ, Vincent JL, Raichle ME, Schlaggar BL, and Petersen SE. Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci U S A. 2007;104(26):11073-8.

Dosenbach NUF, Visscher KM, Palmer ED, Miezin FM, Wenger KK, Kang HC, Burgund ED, Grimes AL, Schlaggar BL, and Petersen SE. A core system for the implementation of task sets. Neuron. 2006;50(5):799-812.

Last Updated: 2/24/2016 11:41:20 AM

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