Network organization of sensory-biased and multi-sensory working memory and attention in human cortex with fMRI

The ability to attentively filter sensory information and manipulate it in working memory is critical for our ability to interact with the world. Although primary and secondary sensory cortical areas have been well-studied, frontal lobe contributions to sensory attention and working memory remain under-investigated. This dissertation investigates the topography and network organization of sensory-biased and multi-sensory regions in the healthy human brain using functional magnetic resonance imaging (fMRI). First, this research developed a series of functional connectivity analyses of data from the Human Connectome Project to validate and extend recently localized auditory-biased network structures, transverse gyrus intersecting the precentral sulcus (tgPCS) and caudal inferior frontal sulcus (cIFS), and visual-biased network structures, superior precentral sulcus (sPCS) and inferior precentral sulcus (iPCS), in lateral frontal cortex (LFC). Results replicated the original findings and extended them by revealing five additional bilateral LFC regions, including middle inferior frontal sulcus (midIFS) and frontal operculum (FO), differentially connected to either the visual- or auditory-biased networks. Due to inter-subject anatomical variability, identification of sPCS, tgPCS, iPCS and cIFS depends critically on within-subject analyses. Next, this work demonstrated that an individual’s unique pattern of resting-state functional connectivity can accurately identify their specific pattern of working memory (WM) and attention task activation in LFC using “connectome fingerprinting” (CF). CF predictions were superior to group-average predictions and matched the accuracy of within-subject task-based functional localization. This research developed and validated methods that use intrinsic functional connectivity information to perform functional brain analyses on highly idiosyncratic brain regions. Finally, a combined auditory, tactile and visual WM study revealed the joint organization of sensory-biased and multi-sensory regions within individual subjects. Hypothesized visual-biased midIFS and auditory-biased FO regions were functionally confirmed for the first time. Several bilateral tactile-biased regions, premotor dorsal, premotor ventral, anterior middle frontal gyrus, middle insula, postcentral sulcus, posterior middle temporal gyrus and pre-supplemental motor area, abutting previously described visual- and auditory-biased regions were identified. Several multi-sensory WM regions, recruited in each stimulus modality, were observed to partially overlap with visual-biased regions. Intrinsic functional connectivity analyses revealed that regions segregate into networks largely based upon their modality preferences.2020-06-14T00:00:00

Towards a learning fingerprint: new methods and paradigms for complex motor skill learning in fMRI

Functional Magnetic Resonance Imaging (fMRI) research in sensorimotor learning focus on two separate paradigms: (1) task-based (tfMRI), where brain changes are evaluated ac- cording to activity elicited by performance of the task, or (2) task-free, i.e., resting-state (rsfMRI), where changes are reflected in spontaneous, internally generated brain activity. While the former paradigm allows careful control and manipulation of the task, the later allows unrestrained motor learning tasks to take place beyond the limitations of the scanner environment. Machine learning approaches attempting to model these two types of measure- ments together to explain physiological effects of learning remained unexplored. Although these paradigms yield results showing considerable overlap between their topographical pat- terns, they are usually treated separately. Consequently, their relationship, and how or if any behaviorally relevant neural information processing mediates it, remains unclear. To resolve this ambiguity, new methodology was developed guided by questions of sensorimotor learning in motor tasks having dynamics completely specified mathematically. First, basic fMRI methodological considerations were made. Machine learning methods that claimed to predict individual tfMRI task maps from rsfMRI activity were improved. In reviewing previous methodology, most methods were found to underperform against trivial baseline model performances based on massive group averaging. New methods were devel- oped that remedies this problem to a great extent. Benchmark comparisons and model evaluation metrics demonstrating empirical properties related to this predictive mapping previously unconsidered were also further developed. With these newly formed empirical ob- servations, a relationship between individual prediction scores and behavioral performance measured during the task could be established. Second, a complex motor learning task performed during an fMRI measurement was designed to relate learning effects observed in both types of measurements from a single longitudinal learning session. Participants measured while performing the task show they learn to exploit a property that drives brain activity in certain regions towards a state requiring less active control and error correction. Reconfiguration of functional activity in task-evoked and task- free activity from these behavioral learning effects were investigated, applying methodology developed earlier in an attempt to relate them together. Predictions of individual task- evoked responses from rsfMRI provide a relative measure of dependence, however, remain limited for reasons understood from the methodological study. No rsfMRI reconfiguration due to learning was detected, yet changes over the course of learning in task-evoked activity appear significant. Increasing recruitment of the Default Mode Network (DMN) during the task explain these changes. These results support that minimal reconfiguration of the cortex suggestive of plasticity effects are needed to find task solutions in a passively stable space

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize pointwise tissue microstructure and positional information from all points within a fiber tract. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, we propose a Critical Region Localization algorithm to identify highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. The localized critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.Comment: 28 pages, 7 figure

Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets

The use of machine learning for whole-brain classification of magnetic resonance imaging (MRI) data is of clear interest, both for understanding phenotypic differences in brain structure and function and for diagnostic applications. Developments of deep learning models in the past decade have revolutionized photographic image and speech recognition, bringing promise to do the same to other fields of science. However, there are many practical and theoretical challenges in the translation of such methods to the unique context of MRIs of the brain. This thesis presents a theoretical underpinning for whole-brain classification of extremely large datasets of multi-site MRIs, including machine learning model architecture, dataset curation methods, machine learning visualization methods, encoding of MRI data, and feature extraction. To replicate large sample sizes typically applied to deep learning models, a dataset of over 50,000 functional and structural MRIs was amassed from nine different databases, and the undertaken analyses were conducted on three covariates commonly found across these collections: sex, resting state/task, and autism spectrum disorder. I find that deep learning is not only a method that has promise for clinical application in the future, but also a powerful statistical tool for analyzing complex, nonlinear relationships in brain data where conventional statistics may fail. However, results are also dependent on factors such as dataset imbalances, confounding factors such as motion and head size, selected methods of encoding MRI data, variability of machine learning models and selected methods of visualizing the machine learning results. In this thesis, I present the following methodological innovations: (1) a method of balancing datasets as a means of regressing out measurable confounding factors; (2) a means of removing spatial biases from deep learning visualization methods; (3) methods of encoding functional and structural datasets as connectivity matrices; (4) the use of ensemble models and convolutional neural network architectures to improve classification accuracy and consistency; (5) adaptation of deep learning visualization methods to study brain connections utilized in the classification process. Additionally, I discuss interpretations, limitations, and future directions of this research.Gates Cambridge Scholarshi

Relating Spontaneous Activity and Cognitive States via NeuroDynamic Modeling

Stimulus-free brain dynamics form the basis of current knowledge concerning functional integration and segregation within the human brain. These relationships are typically described in terms of resting-state brain networks—regions which spontaneously coactivate. However, despite the interest in the anatomical mechanisms and biobehavioral correlates of stimulus-free brain dynamics, little is known regarding the relation between spontaneous brain dynamics and task-evoked activity. In particular, no computational framework has been previously proposed to unite spontaneous and task dynamics under a single, data-driven model. Model development in this domain will provide new insight regarding the mechanisms by which exogeneous stimuli and intrinsic neural circuitry interact to shape human cognition. The current work bridges this gap by deriving and validating a new technique, termed Mesoscale Individualized NeuroDynamic (MINDy) modeling, to estimate large-scale neural population models for individual human subjects using resting-state fMRI. A combination of ground-truth simulations and test-retest data are used to demonstrate that the approach is robust to various forms of noise, motion, and data processing choices. The MINDy formalism is then extended to simultaneously estimating neural population models and the neurovascular coupling which gives rise to BOLD fMRI. In doing so, I develop and validate a new optimization framework for simultaneously estimating system states and parameters. Lastly, MINDy models derived from resting-state data are used to predict task-based activity and remove the effects of intrinsic dynamics. Removing the MINDy model predictions from task fMRI, enables separation of exogenously-driven components of activity from their indirect consequences (the model predictions). Results demonstrate that removing the predicted intrinsic dynamics improves detection of event-triggered and sustained responses across four cognitive tasks. Together, these findings validate the MINDy framework and demonstrate that MINDy models predict brain dynamics across contexts. These dynamics contribute to the variance of task-evoked brain activity between subjects. Removing the influence of intrinsic dynamics improves the estimation of task effects

Network targets for therapeutic brain stimulation: towards personalized therapy for pain

Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain

Individual variability in value-based decision making: behavior, cognition, and functional brain topography

Decisions often require weighing the costs and benefits of available prospects. Value-based decision making depends on the coordination of multiple cognitive faculties, making it potentially susceptible to at least two forms of variability. First, there is heterogeneity in brain organization across individuals in areas of association cortex that exhibit decision-related activity. Second, a person’s preferences can fluctuate even for repetitive decision scenarios. Using functional magnetic resonance imaging (fMRI) and behavioral experiments in humans, this project explored how these distinct sources of variability impact choice evaluation, localization of valuation in the brain, and the links between valuation and other cognitive phenomena. Group-level findings suggest that valuation processes share a neural representation with the “default network” (DN) in medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). Study 1 examined brain network variability in an open dataset of resting-state fMRI (n=100) by quantitatively testing the hypothesis that the spatial layout of the DN is unique to each person. Functional network topography was well-aligned across individuals in PCC, but highly idiosyncratic in mPFC. These results highlighted that the apparent overlap of cognitive functions in these areas should be evaluated within individuals. Study 2 examined variability in the integration of rewards with subjective costs of time and effort. Two computerized behavioral experiments (total n=132) tested how accept-or-reject foraging decisions were influenced by demands for physical effort, cognitive effort, and unfilled delay. The results showed that people’s willingness to incur the three types of costs differed when they experienced a single type of demand, but gradually converged when all three were interleaved. The results could be accounted for by a computational model in which contextual factors altered the perceived cost of temporal delay. Finally, Study 3 asked whether the apparent cortical overlap between valuation effects and the DN persisted after accounting for individual variability in brain topography and behavior. Using fMRI scans designed to evoke valuation and DN-like effects (n=18), we reproduced the idiosyncratic network topography from Study 1, and observed valuation-related effects in individually identified DN regions. Collectively, these findings advance our taxonomic understanding of higher-order cognitive processes, suggesting that seemingly dissimilar valuation and DN-related functions engage overlapping cortical mechanisms

Variabilität funktionell definierter gesichtssensitiver Hirnregionen und Einfluss von DIRAS2 auf die neuronalen Korrelate emotionaler Wahrnehmung – Zwei fMRT-Untersuchungen an Erwachsenen mit und ohne Aufmerksamkeitsdefizit-Hyperaktivitätsstörung

Diese Dissertationsschrift berichtet über zwei wissenschaftliche Studien, welche einen Beitrag zur fMRT-Forschung auf zwei Ebenen leisten sollen – die erste in der Grundlagenforschung an Gesunden mit einer genaueren Untersuchung zum „Face Localizer“ (wörtlich „Gesichts-Orter“), einem Paradigma zur Eingrenzung gesichtssensitiver Hirnregionen, welches gewöhnlich „nur“ als Hilfsexperiment dient; die zweite in der Grundlagenforschung zur Emotionswahrnehmung und den Auswirkungen eines Risiko-Einzelnukleotidpolymorphismus (SNP, „single nucleotide polymorphism“) an erwachsenen Patienten mit Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS) unter Verwendung unter anderem des in der ersten Studie genauer untersuchten „Face Localizers“. Hierbei wurde insbesondere (1) die intraindividuelle Variabilität und zeitliche Stabilität der verschiedenen gesichtssensitiven Regionen bis zur Eignung für "fMRI-fingerprinting" und (2) Auswirkungen von Diagnose und Genotyp auf die Fähigkeit, Emotionen aus Sprachmelodie und Mimik zu erkennen, sowie auf die Gehirnaktivität hierbei, untersucht. Es zeigten sich die rechte fusiforme und die rechte okzipitale Gesichtsregionen für fingerprinting geeignet, während der posteriore superiore temporale Sulcus zeitlich instabile Aktivierung aufwies. Es zeigten sich weiterhin erwachsene ADHS-Patienten in Erkennung von Emotionen beeinträchtigt, unabhängig vom Genotyp, und umgekehrt veränderte Aktivierung in Thalamus und temporaler Stimmregion bei Risikoallelträgern von rs1412005 in DIRAS2, besonders bei von ADHS Betroffenen

AN EDGE-CENTRIC PERSPECTIVE FOR BRAIN NETWORK COMMUNITIES

Thesis (Ph.D.) - Indiana University, Department of Psychological and Brain Sciences and Program in Neuroscience, 2021The brain is a complex system organized on multiple scales and operating in both a local and distributed manner. Individual neurons and brain regions participate in specific functions, while at the same time existing in the context of a larger network, supporting a range of different functionalities. Building brain networks comprised of distinct neural elements (nodes) and their interrelationships (edges), allows us to model the brain from both local and global perspectives, and to deploy a wide array of computational network tools. A popular network analysis approach is community detection, which aims to subdivide a network’s nodes into clusters that can used to represent and evaluate network organization. Prevailing community detection approaches applied to brain networks are designed to find densely interconnected sets of nodes, leading to the notion that the brain is organized in an exclusively modular manner. Furthermore, many brain network analyses tend to focus on the nodes, evidenced by the search for modular groupings of neural elements that might serve a common function. In this thesis, we describe the application of community detection algorithms that are sensitive to alternative cluster configurations, enhancing our understanding of brain network organization. We apply a framework called the stochastic block model, which we use to uncover evidence of non-modular organization in human anatomical brain networks across the life span, and in the informatically-collated rat cerebral cortex. We also propose a framework to cluster functional brain network edges in human data, which naturally results in an overlapping organization at the level of nodes that bridges canonical functional systems. These alternative methods utilize the connection patterns of brain network edges in ways that prevailing approaches do not. Thus, we motivate an alternative outlook which focuses on the importance of information provided by the brain’s interconnections, or edges. We call this an edge-centric perspective. The edge-centric approaches developed here offer new ways to characterize distributed brain organization and contribute to a fundamental change in perspective in our thinking about the brain