Jessica S. Damoiseaux

Consistent resting-state networks across healthy subjects

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.

Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity

It is commonly assumed that functional brain connectivity reflects structural brain connectivity. The exact relationship between structure and function, however, might not be straightforward. In this review we aim to examine how our understanding of the relationship between structure and function in the ‘resting’ brain has advanced over the last several years. We discuss eight articles that directly compare resting-state functional connectivity with structural connectivity and three clinical case studies of patients with limited white matter connections between the cerebral hemispheres. All studies examined show largely convergent results: the strength of resting-state functional connectivity is positively correlated with structural connectivity strength. However, functional connectivity is also observed between regions where there is little or no structural connectivity, which most likely indicates functional correlations mediated by indirect structural connections (i.e. via a third region). As the methodologies for measuring structural and functional connectivity continue to improve and their complementary strengths are applied in parallel, we can expect important advances in our diagnostic and prognostic capacities in diseases like Alzheimer’s, multiple sclerosis, and stroke.

Loss of 'Small-World' Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity

Background Local network connectivity disruptions in Alzheimers disease patients have been found using graph analysis in BOLD fMRI. Other studies using MEG and cortical thickness measures, however, show more global long distance connectivity changes, both in functional and structural imaging data. The form and role of functional connectivity changes thus remains ambiguous. The current study shows more conclusive data on connectivity changes in early AD using graph analysis on resting-state condition fMRI data. Methodology/Principal Findings 18 mild AD patients and 21 healthy age-matched control subjects without memory complaints were investigated in resting-state condition with MRI at 1.5 Tesla. Functional coupling between brain regions was calculated on the basis of pair-wise synchronizations between regional time-series. Local (cluster coefficient) and global (path length) network measures were quantitatively defined. Compared to controls, the characteristic path length of AD functional networks is closer to the theoretical values of random networks, while no significant differences were found in cluster coefficient. The whole-brain average synchronization does not differ between Alzheimer and healthy control groups. Post-hoc analysis of the regional synchronization reveals increased AD synchronization involving the frontal cortices and generalized decreases located at the parietal and occipital regions. This effectively translates in a global reduction of functional long-distance links between frontal and caudal brain regions. Conclusions/Significance We present evidence of AD-induced changes in global brain functional connectivity specifically affecting long-distance connectivity. This finding is highly relevant for it supports the anterior-posterior disconnection theory and its role in AD. Our results can be interpreted as reflecting the randomization of the brain functional networks in AD, further suggesting a loss of global information integration in disease.

Functional connectivity tracks clinical deterioration in Alzheimer's disease.

While resting state functional connectivity has been shown to decrease in patients with mild and/or moderate Alzheimers disease, it is not yet known how functional connectivity changes in patients as the disease progresses. Furthermore, it has been noted that the default mode network is not as homogenous as previously assumed and several fractionations of the network have been proposed. Here, we separately investigated the modulation of 3 default mode subnetworks, as identified with group independent component analysis, by comparing Alzheimers disease patients to healthy controls and by assessing connectivity changes over time. Our results showed decreased connectivity at baseline in patients versus controls in the posterior default mode network, and increased connectivity in the anterior and ventral default mode networks. At follow-up, functional connectivity decreased across all default mode systems in patients. Our results suggest that earlier in the disease, regions of the posterior default mode network start to disengage whereas regions within the anterior and ventral networks enhance their connectivity. However, as the disease progresses, connectivity within all systems eventually deteriorates.

White matter tract integrity in aging and Alzheimer's disease

The pattern of degenerative changes in the brain white matter (WM) in aging, mild cognitive impairment (MCI), and Alzheimers disease (AD) has been under debate. Methods of image analysis are an important factor affecting the outcomes of various studies. Here we used diffusion tensor imaging (DTI) to obtain fractional anisotropy (FA) measures of the WM in healthy young (n = 8), healthy elderly (n = 22), MCI (n = 8), and AD patients (n = 16). We then applied “tract‐based spatial statistics” (TBSS) to study the effects of aging, MCI, and AD on WM integrity. Our results show that changes in WM integrity (that is, decreases in FA) are different between healthy aging and AD: in healthy older subjects compared with healthy young subjects decreased FA was primarily observed in frontal, parietal, and subcortical areas whereas in AD, compared with healthy older subjects, decreased FA was only observed in the left anterior temporal lobe. This different pattern of decreased anatomical connectivity in normal aging and AD suggests that AD is not merely accelerated aging. Hum Brain Mapp, 2009.

Combining shape and connectivity analysis: an MRI study of thalamic degeneration in Alzheimer's disease.

Alzheimers disease (AD) is associated with neuronal loss not only in the hippocampus and amygdala but also in the thalamus. Anterodorsal, centromedial, and pulvinar nuclei are the main sites of degeneration in AD. Here we combined shape analysis and diffusion tensor imaging (DTI) tractography to study degeneration in AD in the thalamus and its connections. Structural and diffusion tensor MRI scans were obtained from 16 AD patients and 22 demographically similar healthy volunteers. The thalamus, hippocampus, and amygdala were automatically segmented using our locally developed algorithm, and group comparisons were carried out for each surface vertex. We also employed probabilistic diffusion tractography to obtain connectivity measures between individual thalamic voxels and hippocampus/amygdala voxels and to segment the internal medullary lamina (IML). Shape analysis showed significant bilateral regional atrophy in the dorsal-medial part of the thalamus in AD patients compared to controls. Probabilistic tractography demonstrated that these regions are mainly connected with the hippocampus, temporal, and prefrontal cortex. Intrathalamic FA comparisons showed reductions in the anterodorsal region of thalamus. Intrathalamic tractography from this region revealed that the IML was significantly smaller in AD patients than in controls. We suggest that these changes can be attributed to the degeneration of the anterodorsal and intralaminar nuclei, respectively. In addition, based on previous neuropathological reports, ventral and dorsal-medial shape change in the thalamus in AD patients is likely to be driven by IML atrophy. This combined shape and connectivity analysis provides MRI evidence of regional thalamic degeneration in AD.

Model-free group analysis shows altered BOLD FMRI networks in dementia

FMRI research in Alzheimers disease (AD) and mild cognitive impairment (MCI) typically is aimed at determining regional changes in brain function, most commonly by creating a model of the expected BOLD‐response and estimating its magnitude using a general linear model (GLM) analysis. This crucially depends on the suitability of the temporal assumptions of the model and on assumptions about normality of group distributions. Exploratory data analysis techniques such as independent component analysis (ICA) do not depend on these assumptions and are able to detect unknown, yet structured spatiotemporal processes in neuroimaging data. Tensorial probabilistic ICA (T‐PICA) is a model free technique that can be used for analyzing multiple subjects and groups, extracting signals of interest (components) in the spatial, temporal, and also subject domain of FMRI data. We applied T‐PICA and model‐based GLM to study FMRI signal during face encoding in 18 AD, 28 MCI patients, and 41 healthy elderly controls. T‐PICA showed activation in regions associated with motor, visual, and cognitive processing, and deactivation in the default mode network. Six networks showed a significantly decreased response in patients. For two networks the T‐PICA technique was significantly more sensitive to detect group differences than the standard model‐based technique. We conclude that T‐PICA is a promising tool to identify and detect differences in (de)activated brain networks in elderly controls and dementia patients. The technique is more sensitive than the commonly applied model‐based method. Consistent with other research, we show that networks of activation and deactivation show decreased reactivity in dementia. Hum Brain Mapp 2009.

Gender modulates the APOE ε4 effect in healthy older adults: convergent evidence from functional brain connectivity and spinal fluid tau levels.

We examined whether the effect of the apolipoprotein E (APOE) genotype on functional brain connectivity is modulated by gender in healthy older human adults. Our results confirm significantly decreased connectivity in the default mode network in healthy older APOE ε4 carriers compared with ε3 homozygotes. More important, further testing revealed a significant interaction between APOE genotype and gender in the precuneus, a major default mode hub. Female ε4 carriers showed significantly reduced default mode connectivity compared with either female ε3 homozygotes or male ε4 carriers, whereas male ε4 carriers differed minimally from male ε3 homozygotes. An additional analysis in an independent sample of healthy elderly using an independent marker of Alzheimers disease, i.e., spinal fluid levels of tau, provided corresponding evidence for this gender-by-APOE interaction. Together, these results converge with previous work showing a higher prevalence of the ε4 allele among women with Alzheimers disease and, critically, demonstrate that this interaction between APOE genotype and gender is detectable in the preclinical period.

Resting-state fMRI as a biomarker for Alzheimer's disease?

Previous work indicates that resting-state functional magnetic resonance imaging (fMRI) is sensitive to functional brain changes related to Alzheimers disease (AD) pathology across the clinical spectrum. Cross-sectional studies have found functional connectivity differences in the brains default mode network in aging, mild cognitive impairment, and AD. In addition, two recent longitudinal studies have shown that functional connectivity changes track AD progression. This earlier work suggests that resting-state fMRI may be a promising biomarker for AD. However, some key issues still need to be addressed before resting-state fMRI can be successfully applied clinically. In a previous issue of Alzheimers Research & Therapy, Vemuri and colleagues discuss the use of resting-state fMRI in the study of AD. In this commentary, I will highlight and expand upon some of their main conclusions.

Disordered reward processing and functional connectivity in trichotillomania: A pilot study

BACKGROUND The neurobiology of Trichotillomania is poorly understood, although there is increasing evidence to suggest that TTM may involve alterations of reward processing. The current study represents the first exploration of reward processing in TTM and the first resting state fMRI study in TTM. We incorporate both event-related fMRI using a monetary incentive delay (MID) task, and resting state fMRI, using two complementary resting state analysis methodologies (functional connectivity to the nucleus accumbens and dual regression within a reward network) in a pilot study to investigate differences in reward processing between TTM and healthy controls (HC). METHODS 21 unmedicated subjects with TTM and 14 HC subjects underwent resting state fMRI scans. A subset (13 TTM and 12 HC) also performed the MID task. RESULTS For the MID task, TTM subjects showed relatively decreased nucleus accumbens (NAcc) activation to reward anticipation, but relative over-activity of the NAcc to both gain and loss outcomes. Resting state functional connectivity analysis showed decreased connectivity of the dorsal anterior cingulate (dACC) to the NAcc in TTM. Dual regression analysis of a reward network identified through independent component analysis (ICA) also showed decreased dACC connectivity and more prominently decreased basolateral amygdala connectivity within the reward network in TTM. CONCLUSIONS Disordered reward processing at the level of NAcc, also involving decreased modulatory input from the dACC and the basolateral amygdala may play a role in the pathophysiology of TTM.