Eugene P. Duff

Resting-state fMRI in the Human Connectome Project

Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1h of whole-brain rfMRI data at 3 T, with a spatial resolution of 2×2×2 mm and a temporal resolution of 0.7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.

The Power of Spectral Density Analysis for Mapping Endogenous BOLD Signal Fluctuations

FMRI has revealed the presence of correlated low‐frequency cerebro‐vascular oscillations within functional brain systems, which are thought to reflect an intrinsic feature of large‐scale neural activity. The spatial correlations shown by these fluctuations has been their identifying feature, distinguishing them from fluctuations associated with other processes. Major analysis methods characterize these correlations, identifying networks and their interactions with various factors. However, other analysis approaches are required to fully characterize the regional signal dynamics contributing to these correlations between regions. In this study we show that analysis of the power spectral density (PSD) of regional signals can identify changes in oscillatory dynamics across conditions, and is able to characterize the nature and spatial extent of signal changes underlying changes in measures of connectivity. We analyzed spectral density changes in sessions consisting of both resting‐state scans and scans recording 2 min blocks of continuous unilateral finger tapping and rest. We assessed the relationship of PSD and connectivity measures by additionally tracking correlations between selected motor regions. Spectral density gradually increased in gray and white matter during the experiment. Finger tapping produced widespread decreases in low‐frequency spectral density. This change was symmetric across the cortex, and extended beyond both the lateralized task‐related signal increases, and the established “resting‐state” motor network. Correlations between motor regions also reduced with task performance. In conclusion, analysis of PSD is a sensitive method for detecting and characterizing BOLD signal oscillations that can enhance the analysis of network connectivity. Hum Brain Mapp 2008.

A common brain network links development, aging, and vulnerability to disease.

Significance Many evolutionary–developmental models have attempted to relate development and aging, with one popular hypothesis proposing that healthy age-related brain decline mirrors developmental maturation. But this elegant hypothesis has so far lacked clear and direct data to support it. Here, we describe intrinsic, entirely data-driven evidence that healthy brain degeneration and developmental process mirror one another in certain brain regions. Specifically, a data-driven decomposition of structural brain images in 484 healthy participants reveals a network of mainly higher-order regions that develop relatively late during adolescence, demonstrate accelerated degeneration in old age, and show heightened vulnerability to disorders that impact on brain structure during adolescence and aging. These results provide a fundamental link between development, aging, and disease processes in the brain. Several theories link processes of development and aging in humans. In neuroscience, one model posits for instance that healthy age-related brain degeneration mirrors development, with the areas of the brain thought to develop later also degenerating earlier. However, intrinsic evidence for such a link between healthy aging and development in brain structure remains elusive. Here, we show that a data-driven analysis of brain structural variation across 484 healthy participants (8–85 y) reveals a largely—but not only—transmodal network whose lifespan pattern of age-related change intrinsically supports this model of mirroring development and aging. We further demonstrate that this network of brain regions, which develops relatively late during adolescence and shows accelerated degeneration in old age compared with the rest of the brain, characterizes areas of heightened vulnerability to unhealthy developmental and aging processes, as exemplified by schizophrenia and Alzheimer’s disease, respectively. Specifically, this network, while derived solely from healthy subjects, spatially recapitulates the pattern of brain abnormalities observed in both schizophrenia and Alzheimer’s disease. This network is further associated in our large-scale healthy population with intellectual ability and episodic memory, whose impairment contributes to key symptoms of schizophrenia and Alzheimer’s disease. Taken together, our results suggest that the common spatial pattern of abnormalities observed in these two disorders, which emerge at opposite ends of the life spectrum, might be influenced by the timing of their separate and distinct pathological processes in disrupting healthy cerebral development and aging, respectively.

Functional connectivity in the basal ganglia network differentiates PD patients from controls.

Objective: To examine functional connectivity within the basal ganglia network (BGN) in a group of cognitively normal patients with early Parkinson disease (PD) on and off medication compared to age- and sex-matched healthy controls (HC), and to validate the findings in a separate cohort of participants with PD. Methods: Participants were scanned with resting-state fMRI (RS-fMRI) at 3T field strength. Resting-state networks were isolated using independent component analysis. A BGN template was derived from 80 elderly HC participants. BGN maps were compared between 19 patients with PD on and off medication in the discovery group and 19 age- and sex-matched controls to identify a threshold for optimal group separation. The threshold was applied to 13 patients with PD (including 5 drug-naive) in the validation group to establish reproducibility of findings. Results: Participants with PD showed reduced functional connectivity with the BGN in a wide range of areas. Administration of medication significantly improved connectivity. Average BGN connectivity differentiated participants with PD from controls with 100% sensitivity and 89.5% specificity. The connectivity threshold was tested on the validation cohort and achieved 85% accuracy. Conclusions: We demonstrate that resting functional connectivity, measured with MRI using an observer-independent method, is reproducibly reduced in the BGN in cognitively intact patients with PD, and increases upon administration of dopaminergic medication. Our results hold promise for RS-fMRI connectivity as a biomarker in early PD. Classification of evidence: This study provides Class III evidence that average connectivity in the BGN as measured by RS-fMRI distinguishes patients with PD from age- and sex-matched controls.

The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

Long-term motor training induced changes in regional cerebral blood flow in both task and resting states.

Neuroimaging studies of functional activation often only reflect differentiated involvement of brain regions compared between task performance and control states. Signals common for both states are typically not revealed. Previous motor learning studies have shown that extensive motor skill training can induce profound changes in regional activity in both task and control states. To address the issue of brain activity changes in the resting-state, we explored long-term motor training induced neuronal and physiological changes in normal human subjects using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). Ten healthy subjects performed a finger movement task daily for four weeks, during which three sessions of fMRI images and two sessions of PET images were acquired. Using a classical data analysis strategy, we found that the brain activation increased first and then returned to the pre-training, replicating previous findings. Interestingly, we also observed that motor skill training induced significant increases in regional cerebral blood flow (rCBF) in both task and resting states as the practice progressed. The apparent decrease in activation may actually result from a greater increase in activity in the resting state, rather than a decrease in the task state. By showing that training can affect the resting state, our findings have profound implications for the interpretation of functional activations in neuroimaging studies. Combining changes in resting state with activation data should greatly enhance our understanding of the mechanisms of motor-skill learning.

The effects of APOE on brain activity do not simply reflect the risk of Alzheimer's disease.

Possession of the APOE-ε4 allele is the best established genetic risk factor for sporadic Alzheimers disease (AD), while the ε2 allele may confer protection against the disease. Previous functional magnetic resonance imaging (fMRI) studies have shown an effect of APOE genotype on brain function, typically by comparing only ε4 carriers with noncarriers. Here we included a wide range of genotype groups to determine how closely the effects of APOE on brain function are related to differences in relative risk for AD. We used functional magnetic resonance imaging (fMRI) to compare the pattern of activation during an episodic encoding task and during a counting Stroop task in 76 adults, aged 32 to 55, with different APOE genotypes (23 ε2/ε3, 20 ε3/ε3, 26 ε3/ε4, and 7 ε4/ε4). Strikingly, participants with an increased risk (ε4 carriers) and with a decreased risk (ε2 carriers) for AD both showed increased activation, relative to ε3 homozygotes, during both tasks. The increased activation was due to decreased deactivation or paradoxical activation of nontask-related regions of the brain, which suggests an intrinsic effect of APOE on the differentiation of functional cortical networks. These results question the often assumed link between APOE, the blood oxygenation level dependent (BOLD) response, and AD risk.

Nonlinear estimation of the BOLD signal.

Signal variations in functional Magnetic Resonance Imaging experiments essentially reflect the vascular system response to increased demand for oxygen caused by neuronal activity, termed the blood oxygenation level dependent (BOLD) effect. The most comprehensive model to date of the BOLD signal is formulated as a mixed continuous-discrete-time system of nonlinear stochastic differential equations. Previous approaches to the analysis of this system have been based on linearised approximations of the dynamics, which are limited in their ability to capture the inherent nonlinearities in the physiological system. In this paper we present a nonlinear filtering method for simultaneous estimation of the hidden physiological states and the system parameters, based on an iterative coordinate descent framework. State estimates of the cerebral blood flow, cerebral blood volume and deoxyhaemoglobin content are determined using a particle filter, demonstrated via simulation to be accurate, robust and efficient in comparison to linearisation-based techniques. The adaptive state and parameter estimation algorithm generates physiologically reasonable parameter estimates for experimental fMRI data. It is anticipated that signal processing techniques for modelling and estimation will become increasingly important in fMRI analyses as limitations of linear and linearised modelling are reached.

Disintegration of Sensorimotor Brain Networks in Schizophrenia

BACKGROUND Schizophrenia is a severe mental disorder associated with derogated function across various domains, including perception, language, motor, emotional, and social behavior. Due to its complex symptomatology, schizophrenia is often regarded a disorder of cognitive processes. Yet due to the frequent involvement of sensory and perceptual symptoms, it has been hypothesized that functional disintegration between sensory and cognitive processes mediates the heterogeneous and comprehensive schizophrenia symptomatology. METHODS Here, using resting-state functional magnetic resonance imaging in 71 patients and 196 healthy controls, we characterized the standard deviation in BOLD (blood-oxygen-level-dependent) signal amplitude and the functional connectivity across a range of functional brain networks. We investigated connectivity on the edge and node level using network modeling based on independent component analysis and utilized the brain network features in cross-validated classification procedures. RESULTS Both amplitude and connectivity were significantly altered in patients, largely involving sensory networks. Reduced standard deviation in amplitude was observed in a range of visual, sensorimotor, and auditory nodes in patients. The strongest differences in connectivity implicated within-sensorimotor and sensorimotor-thalamic connections. Furthermore, sensory nodes displayed widespread alterations in the connectivity with higher-order nodes. We demonstrated robustness of effects across subjects by significantly classifying diagnostic group on the individual level based on cross-validated multivariate connectivity features. CONCLUSION Taken together, the findings support the hypothesis of disintegrated sensory and cognitive processes in schizophrenia, and the foci of effects emphasize that targeting the sensory and perceptual domains may be key to enhance our understanding of schizophrenia pathophysiology.

Learning to identify CNS drug action and efficacy using multistudy fMRI data

Existing functional brain imaging data sets were used to identify neural signatures that confirm pharmacological action and predict clinical efficacy of test compounds. Brain patterns determine drug efficacy There are many drugs out there that affect the central nervous system (CNS), from drugs for chronic pain to schizophrenia to obesity. A brain imaging technique called functional magnetic resonance imaging (fMRI) has shown promise for distinguishing an effective compound from an ineffective one, but the real unmet need is to be able to predict whether a new CNS drug will have clinical efficacy. To this end, Duff et al. evaluated existing fMRI data sets for patients who were exposed to painful stimuli (such as heat or a squeeze) and given either an analgesic compound or a placebo. From these brain “maps,” or neural signatures, the authors were able to create a general “stop/go” decision-making framework—which included quality assurance, pharmacodynamic effect, and evidence for clinical efficacy steps—that allowed them to determine whether the signature of a new compound provided evidence for analgesic properties. Other than evaluating potential drug efficacy, the authors revealed insights into pain response mechanisms. This multistudy approach by Duff et al. may translate to a powerful tool in synthesizing and learning from neuroimaging data to improve—and perhaps speed up—CNS drug discovery and repurposing. The therapeutic effects of centrally acting pharmaceuticals can manifest gradually and unreliably in patients, making the drug discovery process slow and expensive. Biological markers providing early evidence for clinical efficacy could help prioritize development of the more promising drug candidates. A potential source of such markers is functional magnetic resonance imaging (fMRI), a noninvasive imaging technique that can complement molecular imaging. fMRI has been used to characterize how drugs cause changes in brain activity. However, variation in study protocols and analysis techniques has made it difficult to identify consistent associations between subtle modulations of brain activity and clinical efficacy. We present and validate a general protocol for functional imaging–based assessment of drug activity in the central nervous system. The protocol uses machine learning methods and data from multiple published studies to identify reliable associations between drug-related activity modulations and drug efficacy, which can then be used to assess new data. A proof-of-concept version of this approach was developed and is shown here for analgesics (pain medication), and validated with eight separate studies of analgesic compounds. Our results show that the systematic integration of multistudy data permits the generalized inferences required for drug discovery. Multistudy integrative strategies of this type could help optimize the drug discovery and validation pipeline.