Transcranial direct current stimulation (tDCS) has been proposed for experimental and therapeutic modulation of regional brain function. Specifically, anodal tDCS of the dorsolateral prefrontal cortex (DLPFC) together with cathodal tDCS of the supraorbital region have been associated with improvement of cognition and mood, and have been suggested for the treatment of several neurological and psychiatric disorders. Although modeled mathematically, the distribution, direction, and extent of tDCSmediated effects on brain physiology are not well understood. The current study investigates whether tDCS of the human prefrontal cortex modulates resting-state network (RSN) connectivity measured by functional magnetic resonance imaging (fMRI). Thirteen healthy subjects underwent real and sham tDCS in random order on separate days. tDCS was applied for 20 min at 2 mA with the anode positioned over the left DLPFC and the cathode over the right supraorbital region. Patterns of resting-state brain connectivity were assessed before and after tDCS with 3 T fMRI, and changes were analyzed for relevant networks related to the stimulation-electrode localizations. At baseline, four RSNs were detected, corresponding to the default mode network (DMN), the left and right frontal-parietal networks (FPNs) and the self-referential network. After real tDCS and compared with sham tDCS, significant changes of regional brain connectivity were found for the DMN and the FPNs both close to the primary stimulation site and in connected brain regions. These findings show that prefrontal tDCS modulates resting-state functional connectivity in distinct functional networks of the human brain.

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.

Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

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Cognitive function requires a high level of functional interaction between regions of a network supporting cognition. Assuming that brain activation changes denote an advanced state of disease progression, changes in functional connectivity may precede changes in brain activation. The objective of this study was to investigate changes in functional connectivity of the right middle fusiform gyrus (FG) in subjects with mild cognitive impairment (MCI) during performance of a face-matching task. The right middle FG is a key area for processing face stimuli. Brain activity was measured using functional MRI. There were 16 MCI subjects and 19 age-matched healthy controls. The linear correlation coefficient was utilized as a measure of functional connectivity between the right middle FG and all other voxels in the brain. There were no statistical differences found in task performance or activation between groups. The right middle FG of the healthy control and MCI groups showed strong bilateral positive linear correlation with the visual cortex, inferior and superior parietal lobules, dorsolateral prefrontal cortex (DLPFC) and anterior cingulate. The healthy controls showed higher positive linear correlation of the right middle FG to the visual cortex, parietal lobes and right DLPFC than the MCI group, whereas the latter had higher positive linear correlation in the left cuneus. In the healthy controls, the right middle FG had negative linear correlation with right medial frontal gyrus and superior temporal gyrus and with left inferior parietal lobule (IPL), angular gyrus, superior frontal gyrus and anterior cingulate gyrus, but the MCI group had negative linear correlation with the left IPL, angular gyrus, precuneus, anterior cingulate, and to right middle temporal gyrus and posterior cingulate gyrus. In the negatively linearly correlated regions, the MCI group had reduced functional connectivity to the frontal areas, right superior temporal gyrus and left IPL. Different regions of the cuneus and IPL had increased functional connectivity in either group. The putative presence of Alzheimer's disease neuropathology in MCI affects functional connectivity from the right middle FG to the visual areas and medial frontal areas. In addition, higher linear correlation in the MCI group in the parietal lobe may indicate the initial appearance of compensatory processes. The results demonstrate that functional connectivity can be an effective marker for the detection of changes in brain function in MCI subjects.

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Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) time-series reveals distinct coactivation patterns in the resting brain representing spatially coherent spontaneous fluctuations of the fMRI signal. Among these patterns, the so-called default-mode network (DMN) has been attributed to the ongoing mental activity of the brain during wakeful resting state. Studies suggest that many neuropsychiatric diseases disconnect brain areas belonging to the DMN. The potential use of the DMN as functional imaging marker for individuals at risk for these diseases, however, requires that the components of the DMN are reproducible over time in healthy individuals. In this study, we assessed the reproducibility of the DMN components within and between imaging sessions in 18 healthy young subjects (mean age, 27.5 years) who were scanned three times with two resting state scans during each session at 3.0 T field strength. Statistical analysis of fMRI time-series was done using ICA implemented with BrainVoyager QX. At all three sessions the essential components of the DMN could be identified in each individual. Spatial extent of DMN activity and size of overlap within and between sessions were most reproducible for the anterior and posterior cingulate gyrus. The degree of reproducibility of the DMN agrees with the degree of reproducibility found with motor paradigms. We conclude that DMN coactivation patterns are reproducible in healthy young subjects. Therefore, these data can serve as basis to further explore the effects of aging and neuropsychiatric diseases on the DMN of the brain.

White matter microstructure underlying default mode network connectivity in the human brain, NeuroImage (2009), doi:10.1016/j.neuroimage.2009.10.067 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT2 AbstractResting state functional magnetic resonance imaging (fMRI) reveals a distinct network of correlated brain function representing a default mode state of the human brain. The underlying structural basis of this functional connectivity pattern is still widely unexplored.We combined fractional anisotropy measures of fiber tract integrity derived from diffusion tensor imaging (DTI) and resting state fMRI data obtained at 3 Tesla from 20 healthy elderly subjects (56 to 83 years of age) to determine white matter microstructure underlying default mode connectivity. We hypothesized that the functional connectivity between the posterior cingulate and hippocampus from resting state fMRI data would be associated with the white matter microstructure in the cingulate bundle and fiber tracts connecting posterior cingulate gyrus with lateral temporal lobes, medial temporal lobes and precuneus. This was demonstrated at the p < 0.001 level using a voxel-based multivariate analysis of covariance (MANCOVA) approach. In addition, we used a data driven technique of joint independent component analysis (ICA) that uncovers spatial pattern that are linked across modalities. It revealed a pattern of white matter tracts including cingulate bundle and associated fiber tracts resembling the findings from the hypothesis-driven analysis and was linked to the pattern of default mode network (DMN) connectivity in the resting state fMRI data. Our findings support the notion that the functional connectivity between the posterior cingulate and hippocampus and the functional connectivity across the entire DMN is based on distinct pattern of anatomical connectivity within the cerebral white matter.