This study is an elegant demonstration of alterations in the "cruise control" networks involved in default brain states. The strength of the authors’ methodology is that it is "model-free," that is, there are no assumptions about how individual events influence the brain. Even so, their conclusion supports our conclusion, which we reach by modeling our data, that is, posterior regions of the default mode network will be important areas for assessing clinical status of brain disorders, including dementia. These regions strongly influence how attention and memory are regulated and therefore are affected by Alzheimer's pathology and, more importantly, the prodromal stage of the disease, amnestic MCI.

View all comments by Jeffrey Petrella

Analyses of resting-state connectivity constitute a rapidly growing subfield of functional brain imaging. This technique is particularly appealing when studying neuropsychiatric patients, any of whom have difficulty performing tasks in the scanner. Depending on how thinly one slices resting-state fMRI data, there appear to be between six and 10 spatially distinct resting-state networks (RSNs) corresponding to canonical sensory and cognitive domains such as vision, sensory-motor function, executive function, and salience processing (1,2). The default-mode network (DMN) is perhaps the most heavily studied RSN. It has been implicated in episodic memory processing and self-referential thought (3-5). It has also been linked, by our group and several others, to Alzheimer disease (AD) (4-8). We had previously used independent component analysis (ICA) to demonstrate disrupted DMN connectivity in patients with mild AD compared to healthy elderly controls (4).

The elegant study by Sorg and colleagues in Munich applies this same RSN approach to an earlier segment of the AD pathology...  Read more

Analyses of resting-state connectivity constitute a rapidly growing subfield of functional brain imaging. This technique is particularly appealing when studying neuropsychiatric patients, any of whom have difficulty performing tasks in the scanner. Depending on how thinly one slices resting-state fMRI data, there appear to be between six and 10 spatially distinct resting-state networks (RSNs) corresponding to canonical sensory and cognitive domains such as vision, sensory-motor function, executive function, and salience processing (1,2). The default-mode network (DMN) is perhaps the most heavily studied RSN. It has been implicated in episodic memory processing and self-referential thought (3-5). It has also been linked, by our group and several others, to Alzheimer disease (AD) (4-8). We had previously used independent component analysis (ICA) to demonstrate disrupted DMN connectivity in patients with mild AD compared to healthy elderly controls (4).

The elegant study by Sorg and colleagues in Munich applies this same RSN approach to an earlier segment of the AD pathology spectrum, demonstrating reduced resting-state DMN connectivity in patients with amnestic MCI. This paper includes some particularly informative additional analyses. First, a voxel-based morphometry (VBM) analysis was used to demonstrate that reduced connectivity in the DMN was not driven by atrophy. Second, the authors supplemented their ICA with a region-of-interest (ROI)-based connectivity analysis to demonstrate reduced hippocampal-to-posterior cingulate connectivity in the MCI group (which was missed with ICA). Finally, by examining several RSNs, they demonstrate that the disrupted connectivity in MCI is relatively specific to the DMN.

It seems, therefore, that resting-state fMRI is a sensitive tool for detecting group-level effects of early AD pathology—this despite the fact that the current study only acquired 4 minutes of resting-state data (scanning one brain volume every 3 seconds) on a 1.5T scanner. We typically acquire twice as many time points (6 or 8 minutes of rest scanning one brain volume every 2 seconds) and have found, anecdotally, that 3T is far superior to 1.5T in detecting resting-state functional connectivity. As such I suspect that this approach will perform even better in MCI when applied at higher field with the acquisition of longer time series. Obvious but important next steps include seeing whether DMN connectivity predicts conversion from MCI to AD and whether DMN connectivity can provide an early objective marker of treatment efficacy in clinical trials (granting the weighty presumption that novel therapeutics are near at hand).

References:
1. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, Reiss AL, Greicius MD. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 2007;27:2349-56. Abstract

2. Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 2005;360:1001-13. Abstract

3. Johnson SC, Ries ML, Hess TM, Carlsson CM, Gleason CE, Alexander AL, Rowley HA, Asthana S, Sager MA. Effect of Alzheimer Disease Risk on Brain Function During Self-appraisal in Healthy Middle-aged Adults. Arch Gen Psychiatry 2007;64:1163-71. Abstract

4. Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A 2004;101:4637-42. Abstract

5. Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, Sheline YI, Klunk WE, Mathis CA, Morris JC, Mintun MA. Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 2005;25:7709-17. Abstract

6. Rombouts SA, Barkhof F, Goekoop R, Stam CJ, Scheltens P. Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: An fMRI study. Hum Brain Mapp 2005;26:231-9. Abstract

7. Lustig C, Snyder AZ, Bhakta M, O'Brien KC, McAvoy M, Raichle ME, Morris JC, Buckner RL. Functional deactivations: change with age and dementia of the Alzheimer type. Proc Natl Acad Sci U S A 2003;100:14504-9. Abstract

8. Celone KA, Calhoun VD, Dickerson BC, Atri A, Chua EF, Miller SL, DePeau K, Rentz DM, Selkoe DJ, Blacker D, Albert MS, Sperling RA. Alterations in memory networks in mild cognitive impairment and Alzheimer's disease: an independent component analysis. J Neurosci 2006;26:10222-31. Abstract

View all comments by Michael Greicius

This is fascinating information. I believe we are just beginning to see the potential of fMRI in the field of neuroscience. Much of my current research involves fMRI in the study of mirror neurons, and I encourage new scientists to consider further exploration of fMRI in AD research applications.

View all comments by J. Lucy Boyd