Scientists have known for

Transcription

1 The Brain-Network Paradigm: Using Functional Imaging Data to Study How the Brain Works Gowtham Atluri, Angus MacDonald III, Kelvin O. Lim, and Vipin Kumar, University of Minnesota Scientists have known for decades that the human brain is an interconnected web containing billions of neurons that fire in space and time in response to various external stimuli. These firings enable a diverse set of functionalities through which humans memorize, learn, reason, perceive, make decisions, and imagine. However, much is still unclear about how different neurons interconnect, what determines their firing patterns, and how firing patterns enable the brain to function in these ways. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative, launched by US President Barack Obama s administration in 213, aims to address these questions by providing a dynamic picture of the brain that, for the first time, shows how individual cells and complex neural circuits interact in both time and space. To better understand brain function, researchers have explored both invasive and noninvasive brainimaging technologies for more than two decades. Of these, functional magnetic resonance imaging (fmri) has emerged as an ideal technology for studying patterns and relationships in whole-brain activity. The sidebar Tradeoffs in Spatial and Temporal Resolution Functional magnetic resonance imaging (fmri) is highly suited for studying patterns and relationships in whole-brain activity. A network-based paradigm for analyzing large volumes of fmri data is a promising framework to understand brain function, but challenges in data analytics must be addressed to enable neuroscience breakthroughs. describes alternative imaging technologies and their spatial and temporal characteristics. Researchers have proposed a variety of approaches to analyze fmri data for use in different study contexts. Several studies compare fmri scans of subjects when they are at rest versus when they are performing a task, such as memorizing and recollecting numbers, words, or faces; watching videos; or listening to music. Studies also use fmri scans to discover which brain locations are activated by a specific stimulus, or to distinguish activation regions resulting from two stimuli, such as an image that the subject can interpret versus one that is too garbled to discern. Approaches in such studies consider the degree of activation at each brain location in response to the stimulus and then build models to COMPUTER /16/$ IEEE PUBLISHED BY THE IEEE COMPUTER SOCIETY OCTOBER

2 TRADEOFFS IN SPATIAL AND TEMPORAL RESOLUTION Invasive methods, typically used to study animal brains, have high temporal and spatial resolution, but only limited spatial coverage. Although high spatial and temporal resolution are desirable properties for capturing brain activity at a finer scale, poor spatial coverage makes these properties unsuitable for studying the patterns and relationships in activity involving the entire brain. The more familiar noninvasive technologies electro encephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fmri) have different advantages and disadvantages in measuring brain activity. EEG and MEG provide superior temporal resolution, but their low spatial resolution makes them unsuitable for effectively studying brain activity patterns. fmri, on the other hand, provides superior spatial resolution and coverage with reasonable temporal resolution and is thus ideal for use in studying patterns and relationships in whole-brain activity. determine which locations the stimulus activates. 1 To discover which locations in the brain are activated by a particular activity, such as finger tapping, researchers use fmri scans collected while the subject performs the activity. Generalized linear models and independent component analysis (ICA) are common tools in these efforts. 2 Studies conducted to discover differences between healthy subjects and those with mental disorders have found significant differences in the strength of interaction among brain regions for the two subject classes when comparing them at rest versus performing a task. 3 This work provides compelling evidence that the brain s functionality stems from interacting brain regions. 4 As a result, scientists are increasingly interested in the brain network paradigm network-based approaches to analyze relationships among brain regions, wherein brain regions are considered as nodes and similarity in the activity over time for a pair of brain regions serves as the strength of an edge. To help explore opportunities for advancing data science in fmri studies of human brain functionality, we investigated recent network-based approaches that construct and analyze brain networks and identified obstacles that must overcome before neuroscience can advance. fmri DATA CHARACTERISTICS The firing of neurons in space and time is referred to as neuronal activity. Throughout neuronal activity, neurons utilize oxygen from oxygenated hemoglobin from red blood cells traveling through local blood vessels. fmri measures the change in oxygenated hemoglobin from every location in the brain, which is referred to as a voxel (analogous to a pixel in a 2D image). 5 fmri data is typically represented as a 4D matrix a 3D space (x, y, z) multiplied by time. For example, in fmri data of size , represents 3D space and 18 represents the number of time points. As Figure 1 shows, fmri data can be visualized two ways. In Figure 1a, it is shown as snapshot images of brain activity for five successive time points. In Figure 1b, it is shown as a time series from three contiguous locations in the brain. By measuring the change in all brain locations over time, fmri can provide data that scientists can analyze to reveal differences between neuronal activity in a resting brain versus that in an active brain. Studying fmri data collected from healthy subjects and from subjects affected by mental disorders can shed light on the neuronal dysfunctionality associated with those disorders. In addition, fmri data collected from subjects with the same disorder before and after administering treatment can reveal how such treatments including antipsychotic drugs, cognitive remediation, and neuromodulation affect the patient s brain activity as well as the treatments downstream effect on brain functionality. DATA MINING CHALLENGES fmri scans can produce massive amounts of high spatial- and temporalresolution data, requiring suitable data analytics infrastructure and algorithms. For example, fmri data collected from one subject in the federally funded Human Connectome project 6 requires more than 1 Gbytes of storage. Multiply that by the thousands of subjects being scanned in this project and other large-scale federally funded initiatives (such as the 1, Functional Connectome project 7 ) and the scale approaches that of big data. Although recent advances in data analytics applied to mining social media and e-commerce data have great potential for mining fmri scans, their application to fmri data analytics is less straightforward. Using fmri data to study brain networks requires defining nodes, edges, and approaches to mine the resultant networks. In traditional network studies, such as social and computer network analyses, 66 COMPUTER

3 (a) Units T = 1 T = 2 T = 3 T = 4 T = x = 4, y = 4, z = 4 x = 39, y = 4, z = 4 x = 4, y = 39, z = (b) FIGURE 1. Two ways to visualize the results of a six-minute functional magnetic resonance imaging (fmri) scan from a healthy subject. (a) A series of brain activity images in sagittal view for five time points (the sagittal plane divides the brain into left and right sides). The dimension of each spatial location is mm, and each time point is 2 s. (b) Time series from three contiguous brain locations. The high similarity between activity measured at adjacent voxels is referred to as spatial autocorrelation. the notion of nodes and edges is clearly established and directly measurable. In a social network, for example, nodes are people and edges are the communications among them. Direct measurements might include how many tweets two people exchange. In contrast, brain networks must be constructed from spatiotemporal data with no predefined notion of nodes and edges, and edges cannot be measured directly. Thus, the resultant networks are inherently different and could exhibit different topological properties that must be understood. Coupled with these differences is the need to answer pressing neuroscientific research questions: Which regions of a healthy brain interact in a resting state? Which regions interact when a healthy subject is working on a given task? What is the interaction sequence that occurs among brain regions when a healthy subject accomplishes a particular task? Which of those interactions is disrupted in a subject with a mental disorder? What states does a brain transition through when a healthy subject is resting or performing a task? NETWORK CONSTRUCTION The brain-network paradigm could be the basis for answering these questions and the foundation of a framework that neuroscientists can use to discern the principles of a healthy subject s brain and how its functionality differs from that of a subject with mental disorders, as well as to identify effective treatment methodologies. Some key data science challenges are how to effectively construct networks from spatiotemporal data, how to define network properties that account for the data s spatiotemporal nature and use them to determine differences between two network classes, and how to study dynamics in networks constructed from spatiotemporal data. To meet these data science challenges, researchers are taking two main directions: building and analyzing brain networks to better understand relationships among brain regions, and building and analyzing dynamic networks for studying brain activity. Building a brain network In a brain network, each voxel could be a node, but the spatial autocorrelation (a high similarity between measurements collected from adjacent locations) in spatiotemporal datasets can cause multiple nodes to have similar connectivity and the resulting networks will have redundant nodes. To reduce this redundancy, researchers rely on brain atlases brain regions that domain experts have anatomically defined. Anatomical atlases. Anatomical atlases are generally coarse, with the number of brain regions being somewhere between 3 and 1. Figure 2a shows a subset of the 9 brain regions from an automated anatomical labeling (AAL) atlas, 8 in which voxels are grouped into OCTOBER

4 2 1 4 Regions (a) (c) Units Regions (b) (d) FIGURE 2. Using regions in a brain atlas to construct brain-network nodes. (a) A subset of brain regions from an automated anatomical labeling (AAL) atlas; (b) region-level time series for the regions in (a) computed as the mean time series for the activity of all voxels within a region; (c) pairwise correlation matrix of the time series in (b) treated as a brain network with continuous-value edge weights; and (d) two coherent sets of voxels (red and blue) from the right middle frontal region, which exhibit drastically different signals. brain regions. The AAL atlas can be used to compute the mean time series within a given region, as in Figure 2b, which shows the mean time series for the regions in Figure 2a. Anatomical brain atlases have two limitations. First, brain regions are typically derived from a single subject s neuroanatomy, 8 so the regional definition might not be accurate for other subjects, as neuroanatomies tend to vary widely. Second, anatomically defined regions do not necessarily capture the natural regions of coherent brain activity, as illustrated in Figure 2d. To overcome these limitations, brain atlases must be based on coherent activity in fmri data; that is, voxels within a brain region must exhibit high similarity in the voxel time series. However, developing algorithms to learn brain regions in a data-driven fashion is challenging because spatial autocorrelations make it hard to determine region boundaries, high dimensionality (millions of voxels) poses computational challenges, and the atlas must be consistent for all subjects scanned in a study. Thus, a novel computational framework is required that can handle both computational and data science challenges. Defining edges. An edge weight between two brain regions is traditionally computed as a correlation between the time series from the corresponding brain regions. Figure 2c shows the matrix of pairwise correlations the linear relationship between a pair of regions computed using the regionlevel time series in Figure 2b. Although nonlinear relationships based on mutual information have been explored, more research is needed to understand what they capture and how they complement correlation- based relationships. Although all pairwise relationships can be computed, not all pairs exhibit nontrivial relationships that are deemed interesting. Discerning which relationships are nontrivial from those that arise from chance requires the development of statistical techniques to generate null models. Computation- intensive simulation experiments are needed to build these models and identify nontrivial relationships that do not arise from chance. Studying network characteristics. Traditional network characteristics can be used to study networks at different levels node degree, edge strength, 9 and an edge group s collective strength 1 as well as higher- order network properties, such as degree distribution, modularity, clustering coefficient, small-worldness, global efficiency, and robustness to random failures. 3 These traditional network characteristics cannot account for the space time characteristics of underlying fmri data and the indirect measurement of nodes and edges. It is important to ensure that the network configuration and its properties are not driven by spatiotemporal autocorrelation. Approaches to determine the derived properties statistical significance must be able to tolerate the noise inherent in fmri data and accommodate the relatively small sample sizes in typical studies. (Because imaging costs are high, studies typically have a 68 COMPUTER

5 fmri time series 2 1 Left middle frontal Right middle frontal (a) 1. small number of subjects.) In addition to handling spatiotemporal issues, these approaches must be able to represent and analyze networks with multiple edge types that reflect the linear and nonlinear relationships between brain region pairs. Serious computational challenges underlie these data science problems, as numerous synthetic datasets must be generated and studied to determine brain networks nonrandom properties. Capturing brain dynamics The brain network as described so far assumes that computed nodes and edges are valid during the entire scan. However, brain activity changes with time because of external stimuli or internal activity. 11 Hence, researchers are pursuing new directions to study the brain s dynamic nature. One approach is to emphasize the brain network s dynamic nature by capturing changes in edges and nodes with time as well as patterns in these changes. Correlation (b) FIGURE 3. Capturing dynamic connectivity in the brain. (a) Time series from the left and right middle frontal brain regions. Bold colored lines indicate time intervals during which correlation is more than.8. (b) Change in correlation between the two time series in (a), computed for every 3-s interval (15 measurements because of the 2-s resolution). The brain regions scanned are homologous, yet correlation varies considerably. fmri time series Left middle frontal Right middle frontal Right superior frontal FIGURE 4. Time series from three brain-region pairs. As in Figure 3a, pairwise correlations among all three region pairs are greater than.8 in some intervals (bold colored lines). Transient pairwise relationships. An edge between two brain regions is traditionally computed as the correlation between two time series for the entire scan duration. Recent studies have found that, for a resting subject, correlations computed for small, fixedduration time intervals within a scan vary across intervals. 11 The graphs in Figure 3, which illustrate the dynamic connectivity between two homologous brain regions, support this observation about varied correlation. In the graphs, intervals are the result of choosing the first 3-s interval (1 to 15 measurements) and then moving the interval one 2-s time step to the right (1 measurement). Because the two regions in Figure 3a are homologous, they are expected to be highly correlated during the scan, yet correlation exceeds.8 in only some intervals, and in others, it is quite low or even negative. Transient higher-order relationships. Dynamic associations are also possible among groups with more than two regions. 12 For example, Figure 4 shows time series with transient high correlations among all three regions. The intervals in which the three regions are correlated closely match the well- correlated time intervals in Figure 3a, suggesting that the three regions interact simultaneously. An analysis of dynamic activity only between region pairs would miss this interesting transient synergistic relationship. Approaches to discover higher-order transient relationships have been proposed, 12 but more such methods are needed to recover synergistic relationships and other interesting associations. Methods to discover and characterize dynamic relationships among brain regions and discover meaningful patterns will shed light on the principles underlying dynamic brain activity. Dynamic relationships manifest in subsets of space and time, and discovering these subspaces requires exploring a OCTOBER

6 ABOUT THE AUTHORS GOWTHAM ATLURI is an assistant professor in the Department of Electrical Engineering and Computing Systems at the University of Cincinnati. While conducting the research reported in this article, Atluri was a postdoctoral Fellow in the Department of Computing Science at the University of Minnesota (UMN) Twin Cities. His research interests include data mining, network analysis, and neuroimaging. Atluri received a PhD in computer science from UMN. Contact him at atlurigm@ucmail.uc.edu. ANGUS MACDONALD III is a professor in the Department of Psychology at UMN Twin Cities. His research interests include clinical psychology and psychopathology. MacDonald received a PhD in clinical psychology from the University of Pittsburgh. Contact him at angus@umn.edu. KELVIN O. LIM is a professor in the Department of Psychiatry at UMN Twin Cities. His research interests include schizophrenia, aging, traumatic brain injury, and cocaine dependence. Lim received an MD from Johns Hopkins University. Contact him at kolim@umn.edu. VIPIN KUMAR is a Regents Professor and William Norris Chair in Large-Scale Computing in the Department of Computer Science and Engineering at UMN Twin Cities. His research interests include data mining and high-performance computing, and their application in climate, ecosystem, and biomedical domains. Kumar received a PhD in computer science from the University of Maryland. He is a Fellow of IEEE, ACM, and the American Association for the Advancement of Science. Contact him at kumar1@umn.edu. large number of possible brain-region and time-interval subsets. Computationally efficient approaches to identify these dynamic relationships must be robust to mitigate artificial patterns induced by head motion, heartbeat, respiration, and scanner instability (also referred to as physiological noise). Assessing the significance of transient relationships. Approaches are also needed to assess the statistical significance of dynamic properties such as transient connectivity, which could be an artifact of physiological noise, and spatial and temporal autocorrelations. In addition, there is a need to understand the potential differences in properties between dynamic networks derived from fmri data and those derived in other domains (such as social networks) in which transient edges are directly measured. Methods are also needed to discover relationship lags that is, when the time series from a region is correlated with a time series from another region with a lag of seconds and to mine the resulting networks. Additionally, approaches are needed to discover how the definition of brain regions and resulting brain networks change with time and to quantify those changes. Network-based approaches to model and mine fmri data have potential, but major network science advances are needed to translate this promise into insightful neuroscience that has societal impact. 13 A paradigm shift in data analytics is also required to account for the spatiotemporal properties inherent in the data, especially in the context of networks in which such properties are not traditionally considered. Tackling these data science challenges will have implications for application domains beyond neuroscience, such as climate science, epidemiology, and sociology areas that also deal in spatiotemporal data characteristics. Computer science can reap many benefits from fmri-enabled brain research. In The Computer and the Brain, John von Neumann predicted that a deeper study of the brain could one day change the way people think about computing. Indeed, recent design advances in brain-inspired computing at IBM gave us TrueNorth, a chip with a million neurons and 256 million synapses mimicking the brain s neuronal architecture. Similar efforts are underway at the Human Brain Project, funded by the European Commission s Future and Emerging Technologies program. ACKNOWLEDGMENTS This research is supported in part by an MnDRIVE Brain Conditions Postdoctoral Fellowship and by National Science Foundation grant REFERENCES 1. F. Pereira, T. Mitchell, and M. Botvinick, Machine Learning Classifiers and fmri: A Tutorial Overview, Neuroimage, vol. 45, no. 1, 29, pp. S199 S E.B. Erhardt et al., Comparison of Multisubject ICA Methods for Analysis of fmri Data, Human Brain Mapping, vol. 32, no. 12, 211, pp M. Rubinov and O. Sporns, Complex Network Measures of Brain Connectivity: Uses and Interpretations, 7 COMPUTER

7 Neuroimage, vol. 52, no. 3, 21, pp J.M. Fuster, The Module: Crisis of a Paradigm, Neuron, vol. 26, no. 1, 2, pp N.K. Logothetis, The Underpinnings of the BOLD Functional Magnetic Resonance Imaging Signal, J. Neuro science, vol. 23, no. 1, 23, pp K. Uğurbil et al., Pushing Spatial and Temporal Resolution for Functional and Diffusion MRI in the Human Connectome Project, Neuroimage, vol. 8, 213, pp C.-G. Yan et al., Standardizing the Intrinsic Brain: Towards Robust Measurement of Inter-Individual Variation in 1, Functional Connectomes, Neuroimage, vol. 8, 213, pp N. Tzourio-Mazoyer et al., Automated Anatomical Labeling of Activations in SPM using a Macroscopic Anatomical Parcellation of the MNI MRI Single- Subject Brain, Neuroimage, vol. 15, no. 1, 22, pp W. Pettersson-Yeo et al., Dysconnectivity in Schizophrenia: Where Are We Now?, Neuroscience & Biobehavioral Rev., vol. 35, no. 5, 211, pp G. Atluri et al., Connectivity Cluster Analysis for Discovering Discriminative Subnetworks in Schizophrenia, Human Brain Mapping, vol. 36, no. 2, 215, pp R.M. Hutchison et al., Dynamic Functional Connectivity: Promise, Issues, and Interpretations, Neuroimage, vol. 8, 213, pp G. Atluri et al., Discovering Groups of Time Series with Similar Behavior in Multiple Small Intervals of Time, Proc. SIAM Int l Conf. Data Mining (SDM 14), 214, pp G. Atluri et al., Complex Biomarker Discovery in Neuroimaging Data: Finding a Needle in a Haystack, Neuro- Image: Clinical, no.3, 213, pp Selected CS articles and columns are also available for free at IEEE Computer Society Software Engineering Institute Watts S. Humphrey Software Process Achievement Award Nomination Deadline: October 15, 216 Do you know a person or team that deserves recognition for their process-improvement activities? The IEEE Computer Society/Software Engineering Institute Watts S. Humphrey Software Process Achievement Award is presented to recognize outstanding achievements in improving the ability of an organization to create and evolve software. The award may be presented to an individual or a group, and the achievements can be the result of any type of process improvement activity. To nominate an individual or group for a Humphrey SPA Award, please visit computer.org/portal/web/awards/spa OCTOBER