“More Is Different” in Functional Magnetic Resonance Imaging: A Review of Recent Data Analysis Techniques

Colocalized White Matter Plasticity and Increased Cerebral Blood Flow Mediate the Beneficial Effect of Cardiovascular Exercise on Long-Term Motor Learning

Cardiovascular exercise (CE) is a promising intervention strategy to facilitate cognition and motor learning in healthy and diseased populations of all ages. CE elevates humoral parameters, such as growth factors, and stimulates brain changes potentially relevant for learning and behavioral adaptations. However, the causal relationship between CE-induced brain changes and human's ability to learn remains unclear. We tested the hypothesis that CE elicits a positive effect on learning via alterations in brain structure (morphological changes of gray and white matter) and function (functional connectivity and cerebral blood flow in resting state). We conducted a randomized controlled trial with healthy male and female human participants to compare the effects of a 2 week CE intervention against a non-CE control group on subsequent learning of a challenging new motor task (dynamic balancing; DBT) over 6 consecutive weeks. We used multimodal neuroimaging [T1-weighted magnetic resonance imaging (MRI), diffusion-weighted MRI, perfusion-weighted MRI, and resting state functional MRI] to investigate the neural mechanisms mediating between CE and learning. As expected, subjects receiving CE subsequently learned the DBT at a higher rate. Using a modified nonparametric combination approach along with multiple mediator analysis, we show that this learning boost was conveyed by CE-induced increases in cerebral blood flow in frontal brain regions and changes in white matter microstructure in frontotemporal fiber tracts. Our study revealed neural mechanisms for the CE-learning link within the brain, probably allowing for a higher flexibility to adapt to highly novel environmental stimuli, such as learning a complex task.SIGNIFICANCE STATEMENT It is established that cardiovascular exercise (CE) is an effective approach to promote learning and memory, yet little is known about the underlying neural transfer mechanisms through which CE acts on learning. We provide evidence that CE facilitates learning in human participants via plasticity in prefrontal white matter tracts and a colocalized increase in cerebral blood flow. Our findings are among the first to demonstrate a transfer potential of experience-induced brain plasticity. In addition to practical implications for health professionals and coaches, our work paves the way for future studies investigating effects of CE in patients suffering from prefrontal hypoperfusion or white matter diseases.

Study protocol: Insight 46 - a neuroscience sub-study of the MRC National Survey of Health and Development

BACKGROUND

Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment - including β-amyloid depostion, vascular disease, network breakdown and atrophy - to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms.

METHODS/DESIGN

This paper outlines the clinical, cognitive and imaging protocol of "Insight 46", a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that ~1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, β-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73).

DISCUSSION

Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

The cortical thickness correlates of clinical manifestations in the mid-stage sporadic Parkinson's disease

The cortical thickness has gained an extensive attention as a pathological alteration of sporadic Parkinson's disease (sPD), the alteration of pathological cortical thickness may distinctly contribute to the consistent clinical manifestations. Therefore, we investigated the cortical thickness correlates of clinical manifestations in the mid-stage sPD from the Han population of Chinese mainland (HPCM). A sample of 67 mid-stage sPD patients and 35 matched controls from HPCM were performed a corticometry of magnetic resonance imaging (MRI) and the assessment of clinical manifestations including the demographic and disease-related characteristics, and underwent the final analysis of the cortical thickness correlates with the clinical manifestations. In our result, we demonstrated that no significant differences in the demographic characteristics were found among the two groups. The tests of clinical disease-related characteristics demonstrated that the significant differences in the Hoehn and Yahr scale, the UPDRS Part I-IV, the symptom-dominant side (right/left/double), the tremor subscoree off (e), the tremor subscoref on (f), Webster, MMSE, HDS-R, DF, DB, SVFT, SDS, HAMD17, HAMD 24, CDT, CDR, LEDD and PDSI were observed between the mid-stage sPD patients and the controls. The analysis about the cortical thickness correlates with the clinical manifestations revealed that a significant correlation between UPDRS-I and Frontal-Sup-Orb-R and Rectus-R; DB and Frontal-Sup-Orb-R and Frontal-Inf-Orb-R; SDS and Frontal-Sup-Orb-R, Frontal-Mid-Orb-R, Rectus-R and Cingulum-Ant-R respectively in the mid-stage sPD patients from HPCM. Our data showed that the cortical thinning in the right frontal Orb, rectus and cingulum were the pathological base of some clinical manifestations including the cognitive impairment, hallucinations, psychosis, the depressed mood, the anxious mood, apathy, the sleep problems, the nighttime or/and daytime sleepiness, the short term memory stores and the central execution, as well as the sexual desire disorder in the mid-stage sPD patients, suggesting that the dysfunctions of brain regions of some cortical thinning are closely correlated with some clinical manifestations of the mid-stage sPD.

Functional neuroimaging in obesity and the potential for development of novel treatments

Recently, exciting progress has been made in understanding the role of the CNS in controlling eating behaviour and in the development of overeating. Regions and networks of the human brain involved in eating behaviour and appetite control have been identified with neuroimaging techniques such as functional MRI, PET, electroencephalography, and magnetoencephalography. Hormones that regulate our drive to eat (eg, leptin, insulin, and glucagon-like peptide-1) can affect brain function. Defects in central hunger signalling are present in many pathologies. On the basis of an understanding of brain mechanisms that lead to overeating, powerful neuroimaging protocols could be a future clinical approach to allow individually tailored treatment options for patients with obesity. The aim of our Review is to provide an overview of neuroimaging approaches for obesity (ie, neuroimaging study design, questions which can be answered by neuroimaging, and limitations of neuroimaging techniques), examine current models of central nervous processes regulating eating behaviour, summarise and review important neuroimaging studies investigating therapeutic approaches to treat obesity or to control eating behaviour, and to provide a perspective on how neuroimaging might lead to new therapeutic approaches to obesity.

Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

Cerebral cartography and connectomics

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.

Different Ways of Linking Behavioral and Neural Data via Computational Cognitive Models

Cognitive neuroscientists sometimes apply formal models to investigate how the brain implements cognitive processes. These models describe behavioral data in terms of underlying, latent variables linked to hypothesized cognitive processes. A goal of model-based cognitive neuroscience is to link these variables to brain measurements, which can advance progress in both cognitive and neuroscientific research. However, the details and the philosophical approach for this linking problem can vary greatly. We propose a continuum of approaches that differ in the degree of tight, quantitative, and explicit hypothesizing. We describe this continuum using four points along it, which we dub qualitative structural, qualitative predictive, quantitative predictive, and single model linking approaches. We further illustrate by providing examples from three research fields (decision making, reinforcement learning, and symbolic reasoning) for the different linking approaches.

Functional network centrality in obesity: A resting-state and task fMRI study

Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Relative to normal-weight controls, participants with obesity exhibited reduced degree centrality in the right middle frontal gyrus in the resting-state condition. During the task fMRI condition, obese participants exhibited less degree centrality in the left middle frontal gyrus and the lateral occipital cortex along with reduced eigenvector centrality in the lateral occipital cortex and occipital pole. Our results highlight the central role of the middle frontal gyrus in the pathophysiology of obesity, a structure involved in several brain circuits signaling attention, executive functions and motor functions. Additionally, our analysis suggests the existence of task-dependent reduced centrality in occipital areas; regions with a role in perceptual processes and that are profoundly modulated by attention.

Comparing like with like: the power of knowing where you are

Magnetic resonance imaging can now provide human brain images of structure, function, and connectivity with isotropic voxels smaller than one millimeter, and thus much smaller than the cortical thickness. This resolution, achievable in a scan time of less than 1 h, enables visualization of myeloarchitectural layer structure, intracortical variations in functional activity--recorded in changes in blood oxygenation level dependent signal or cerebral blood volume CBV--and intracortical axonal orientational structure via diffusion-weighted magnetic resonance imaging. While recent improvements in radiofrequency receiver coils now enable excellent image data to be obtained at 3T, scanning at the ultra-high field of 7 T offers further gains in signal-to-noise ratio and speed of image acquisition, with a structural image resolution of about 300 μm. These improvements throw into sharp question the strategies that have become conventional for the analysis of functional imaging data, especially the practice of spatial smoothing of raw functional data before further analysis. Creation of a native cortical map for each human subject that provides a reliable individual parcellation into cortical areas related to Brodmann Areas enables a strikingly different approach to functional image analysis. This proposed approach involves surface registration of the cortices of groups of subjects using maps of the longitudinal relaxation time T1 as an index of myelination, and methods for inferring statistical significance that do not entail spatial smoothing. The outcome should be a far more precise comparison of like-with-like cortical areas across subjects, with the potential to greatly increase experimental power, to discriminate activity in neighboring cortical areas, and to enable correlation of function and connectivity with specific cytoarchitecture. Such analyses should enable a far more convincing modeling of brain mechanisms than current graph-based methods that require gross over-simplification of brain activity patterns in order to be computationally tractable.

Network hubs in the human brain

Virtually all domains of cognitive function require the integration of distributed neural activity. Network analysis of human brain connectivity has consistently identified sets of regions that are critically important for enabling efficient neuronal signaling and communication. The central embedding of these candidate 'brain hubs' in anatomical networks supports their diverse functional roles across a broad range of cognitive tasks and widespread dynamic coupling within and across functional networks. The high level of centrality of brain hubs also renders them points of vulnerability that are susceptible to disconnection and dysfunction in brain disorders. Combining data from numerous empirical and computational studies, network approaches strongly suggest that brain hubs play important roles in information integration underpinning numerous aspects of complex cognitive function.

Deficient approaches to human neuroimaging

Functional magnetic resonance imaging (fMRI) is the workhorse of imaging-based human cognitive neuroscience. The use of fMRI is ever-increasing; within the last 4 years more fMRI studies have been published than in the previous 17 years. This large body of research has mainly focused on the functional localization of condition- or stimulus-dependent changes in the blood-oxygenation-level dependent signal. In recent years, however, many aspects of the commonly practiced analysis frameworks and methodologies have been critically reassessed. Here we summarize these critiques, providing an overview of the major conceptual and practical deficiencies in widely used brain-mapping approaches, and exemplify some of these issues by the use of imaging data and simulations. In particular, we discuss the inherent pitfalls and shortcomings of methodologies for statistical parametric mapping. Our critique emphasizes recent reports of excessively high numbers of both false positive and false negative findings in fMRI brain mapping. We outline our view regarding the broader scientific implications of these methodological considerations and briefly discuss possible solutions.

Contributions and challenges for network models in cognitive neuroscience

The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity.

Defining nodes in complex brain networks

Network science holds great promise for expanding our understanding of the human brain in health, disease, development, and aging. Network analyses are quickly becoming the method of choice for analyzing functional MRI data. However, many technical issues have yet to be confronted in order to optimize results. One particular issue that remains controversial in functional brain network analyses is the definition of a network node. In functional brain networks a node represents some predefined collection of brain tissue, and an edge measures the functional connectivity between pairs of nodes. The characteristics of a node, chosen by the researcher, vary considerably in the literature. This manuscript reviews the current state of the art based on published manuscripts and highlights the strengths and weaknesses of three main methods for defining nodes. Voxel-wise networks are constructed by assigning a node to each, equally sized brain area (voxel). The fMRI time-series recorded from each voxel is then used to create the functional network. Anatomical methods utilize atlases to define the nodes based on brain structure. The fMRI time-series from all voxels within the anatomical area are averaged and subsequently used to generate the network. Functional activation methods rely on data from traditional fMRI activation studies, often from databases, to identify network nodes. Such methods identify the peaks or centers of mass from activation maps to determine the location of the nodes. Small (~10-20 millimeter diameter) spheres located at the coordinates of the activation foci are then applied to the data being used in the network analysis. The fMRI time-series from all voxels in the sphere are then averaged, and the resultant time series is used to generate the network. We attempt to clarify the discussion and move the study of complex brain networks forward. While the "correct" method to be used remains an open, possibly unsolvable question that deserves extensive debate and research, we argue that the best method available at the current time is the voxel-wise method.