Premium
IC–P–032: Consistency of resting state networks across healthy subjects measured with fMRI
Author(s) -
Damoiseaux Jessica S.,
Rombouts Serge A.R.B.,
Barkhof Frederik,
Scheltens Philip,
Stam Cornelis J.,
Smith Stephen M.,
Beckmann Christian F.
Publication year - 2006
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2006.05.2237
Subject(s) - resting state fmri , default mode network , psychology , brain activity and meditation , precuneus , functional connectivity , brain mapping , functional magnetic resonance imaging , consistency (knowledge bases) , posterior cingulate , audiology , neuroscience , artificial intelligence , electroencephalography , computer science , medicine
Background: FMRI can be applied to study networks of connectivity during a resting-state, termed Resting State Networks (RSNs). Fluctuations in the BOLD-signal during rest reflect the neuronal baseline activity of the brain, representing the state the human brain is in when deliberate neuronal action and external input are absent. Several studies have described ‘similar’ RSNs. The spatial consistency however, has not been evaluated yet. To investigate this a model-free analysis technique that does proper group analyses is needed. Objective(s): In this study we applied Tensor-PICA to resting-state FMRI data to find grey matter RSNs that are consistent across subjects and sessions. Methods: 10 healthy subjects (age 28.1 6, 5 male) were scanned twice at ‘rest’; lying awake with eyes closed. The two group data sets were decomposed separately into the spatial, frequency and subject domain, using tensor-PICA. Maps were thresholded at alternative hypothesis probability of p 0.5. In order to characterize the consistency, we followed a bootstrapping approach and estimated mean RSNs out of 100 surrogate data sets. Results: Data analysis resulted in 10 functionally relevant networks, consisting of regions involved in motor function, visual processing, executive functioning, auditory processing, memory and the ‘default’ network (figure). The percentage BOLD signal change was calculated showing values reaching up to 3%. In general, areas with a high mean percentage BOLD signal change are also the areas showing the least variation around this mean i.e. are the most consistent. Conclusions: Our findings show that the baseline activity of the brain is very consistent and dynamic, with percentages signal change comparable to those found in task related experiments. It is of great interest to investigate whether these RSNs are present to the same extent under different conditions. An advantage of using restingstate FMRI to investigate the influence of disease on the brain is that no complicated setup is required and no task needs to be practiced beforehand. This is a major benefit especially when studying patients who may have difficulties performing a task, e.g. patients with Alzheimer’s disease. Acknowlegdments: This study was supported by ISOA grant number: 231002 and NWO grant number: 916.36.117.