There has been a dramatic increase in the number of studies using resting state functional magnetic resonance imaging (rs-fMRI), a recent addition to imaging analysis techniques. The technique ...analyzes ongoing low-frequency fluctuations in the blood oxygen level-dependent signal. Through patterns of spatial coherence, these fluctuations can be used to identify the networks within the brain. Multiple brain networks are present simultaneously, and the relationships within and between networks are in constant dynamic flux. Resting state fMRI functional connectivity analysis is increasingly used to detect subtle brain network differences and, in the case of pathophysiology, subtle abnormalities in illnesses such as Alzheimer’s disease (AD). The sequence of events leading up to dementia has been hypothesized to begin many years or decades before any clinical symptoms occur. Here we review the findings across rs-fMRI studies in the spectrum of preclinical AD to clinical AD. In addition, we discuss evidence for underlying preclinical AD mechanisms, including an important relationship between resting state functional connectivity and brain metabolism and how this results in a distinctive pattern of amyloid plaque deposition in default mode network regions.
While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted ...technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.
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With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such ...unwanted sources of variation, which we refer to as “scanner effects”, can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.
•Cortical thickness (CT) measurements are highly scanner specific.•Identifying scanner effects is crucial for inference and biomarker development.•We propose to use ComBat to harmonize cortical thickness values across scanners.
Acquiring resting‐state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, ...multi‐site neuroimaging studies have reported considerable nonbiological variability in fMRI measurements due to different scanner manufacturers and acquisition protocols. These undesirable sources of variability may limit power to detect effects of interest and may even result in erroneous findings. Until now, there has not been an approach that removes unwanted site effects. In this study, using a relatively large multi‐site (4 sites) fMRI dataset, we investigated the impact of site effects on functional connectivity and network measures estimated by widely used connectivity metrics and brain parcellations. The protocols and image acquisition of the dataset used in this study had been homogenized using identical MRI phantom acquisitions from each of the neuroimaging sites; however, intersite acquisition effects were not completely eliminated. Indeed, in this study, we found that the magnitude of site effects depended on the choice of connectivity metric and brain atlas. Therefore, to further remove site effects, we applied ComBat, a harmonization technique previously shown to eliminate site effects in multi‐site diffusion tensor imaging (DTI) and cortical thickness studies. In the current work, ComBat successfully removed site effects identified in connectivity and network measures and increased the power to detect age associations when using optimal combinations of connectivity metrics and brain atlases. Our proposed ComBat harmonization approach for fMRI‐derived connectivity measures facilitates reliable and efficient analysis of retrospective and prospective multi‐site fMRI neuroimaging studies.
To better understand intrinsic brain connections in major depression, we used a neuroimaging technique that measures resting state functional connectivity using functional MRI (fMRI). Three different ...brain networks—the cognitive control network, default mode network, and affective network—were investigated. Compared with controls, in depressed subjects each of these three networks had increased connectivity to the same bilateral dorsal medial prefrontal cortex region, an area that we term the dorsal nexus. The dorsal nexus demonstrated dramatically increased depression-associated fMRI connectivity with large portions of each of the three networks. The discovery that these regions are linked together through the dorsal nexus provides a potential mechanism to explain how symptoms of major depression thought to arise in distinct networks—decreased ability to focus on cognitive tasks, rumination, excessive self-focus, increased vigilance, and emotional, visceral, and autonomic dysregulation—could occur concurrently and behave synergistically. It suggests that the newly identified dorsal nexus plays a critical role in depressive symptomatology, in effect "hot wiring" networks together; it further suggests that reducing increased connectivity of the dorsal nexus presents a potential therapeutic target.
Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report ...disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs FPN, dorsal attention network (DAN), and cingulo-opercular network (CON), (ii) higher within-network connectivity in two intrinsic networks DMN and salience network (SAN), and (iii) higher within-network connectivity in two sensory networks sensorimotor network (SMN) and visual network (VIS). Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.
Objective:Despite significant advances in neuroscience and treatment development, no widely accepted biomarkers are available to inform diagnostics or identify preferred treatments for individuals ...with major depressive disorder.Method:In this critical review, the authors examine the extent to which multimodal neuroimaging techniques can identify biomarkers reflecting key pathophysiologic processes in depression and whether such biomarkers may act as predictors, moderators, and mediators of treatment response that might facilitate development of personalized treatments based on a better understanding of these processes.Results:The authors first highlight the most consistent findings from neuroimaging studies using different techniques in depression, including structural and functional abnormalities in two parallel neural circuits: serotonergically modulated implicit emotion regulation circuitry, centered on the amygdala and different regions in the medial prefrontal cortex; and dopaminergically modulated reward neural circuitry, centered on the ventral striatum and medial prefrontal cortex. They then describe key findings from the relatively small number of studies indicating that specific measures of regional function and, to a lesser extent, structure in these neural circuits predict treatment response in depression.Conclusions:Limitations of existing studies include small sample sizes, use of only one neuroimaging modality, and a focus on identifying predictors rather than moderators and mediators of differential treatment response. By addressing these limitations and, most importantly, capitalizing on the benefits of multimodal neuroimaging, future studies can yield moderators and mediators of treatment response in depression to facilitate significant improvements in shorter- and longer-term clinical and functional outcomes.
Studies of early-onset recurrent depression, late life depression associated with neurologic disorders, and bipolar illness have revealed structural brain changes within a neuroanatomical circuit. ...This circuit, originally described by
Nauta (1972), has been termed the limbic-cortical-striatal-pallidal-thalamic tract and is comprised of structures which are extensively interconnected. In three-dimensional magnetic resonance imaging studies of affective illness, many of the structures that comprise this tract have been found to have volume loss or structural abnormalities. Mechanisms proposed to explain volume loss in depression include glucocorticoid neurotoxicity, decreased brain-derived growth factor, decreased neurogenesis, and loss of plasticity.
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