459 research outputs found
Understanding Stroke in the Connected Human Brain
Although structural damage from stroke is focal, remote dysfunction can occur in regions of the brain distant from the area of damage. Lesions in both gray and white matter can disrupt the flow of information in areas connected to or by the area of infarct. This is because the brain is not an assortment of specialized parts but an assembly of distributed networks that interact to support cognitive function. Functional connectivity analyses using resting functional magnetic resonance imaging (fMRI) have shown us that the cortex is organized into distributed brain networks. The primary goal of this work is to characterize the effects of stroke on distributed brain systems and to use this information to better understand neural correlates of deficit and recovery following stroke. We measured resting functional connectivity, lesion topography, and behavior in multiple domains (attention, visual memory, verbal memory, language, motor, and visual) in a cohort of 132 stroke patients. Patients were followed longitudinally with full behavioral and imaging batteries acquired at 2 weeks, 3 months, and 1 year post-stroke. Thirty age- and demographic- matched controls were scanned twice at an interval of three months.
In chapter 1, we explore a central question motivating this work: how is behavior represented in the brain? We review progressing prospective – from basic functional localization to newer theories connecting inter-related brain networks to cognitive operations. In so doing, we attempt to build a foundation that motivates the hypotheses and experimental approaches explored in this work.
Chapters 2 and 3 serve primarily to validate approaches and considerations for using resting fMRI to measure functional connectivity in stroke patients. In chapter 2, we investigate hemodynamic lags after stroke. ‘Hemodynamic lag’ is a local delay in the blood oxygen level dependent (BOLD) response to neural activity, measured using cross-correlation of local fMRI signal with some reference brain signal. This work tests assumptions of the BOLD response to neural activity after stroke, but also provides novel and clinically relevant insight into perilesional disruption to hemodynamics. Significant lags are observed in 30% of stroke patients sub-acutely and 10% of patients at one-year. Hemodynamic lag corresponds to gross aberrancy in functional connectivity measures, performance deficits and local and global perfusion deficits. Yet, relationships between functional connectivity and behavior reviewed in chapter 1 persist after hemodynamic delays is corrected for. Chapter 3 provides a more extended discussion of approaches and considerations for using resting fMRI to measure functional connectivity in stroke patients. Like chapter 1, the goal is to motivate experimental approaches taken in later chapters. But here, more technical challenges relating to brain co-registration, neurovascular coupling, and clinical population selection are considered.
In chapter 4, we uncover the relationships between local damage, network wide functional disconnection, and neurological deficit. We find that visual memory and verbal memory are better predicted by connectivity, whereas visual and motor deficits are better predicted by lesion topography. Attention and language deficits are well predicted by both. We identify a general pattern of physiological network dysfunction consisting of decrease of inter-hemispheric integration and decrease in intra-hemispheric segregation, which strongly related to behavioral impairment in multiple domains.
In chapter 5, we explore a case study of abulia – severe apathy. This work ties together principles of local damage, network disruption, and network-related deficit and demonstrates how they can be useful in understanding and developing targeted treatments (such as transcranial magnetic stimulation) for individual stroke patients.
In chapter 6, we explore longitudinal changes in functional connectivity that parallel recovery. We find that the topology and boundaries of cortical regions remains unchanged across recovery, empirically validating our parcel-wise connectivity approach. In contrast, we find that the modularity of brain systems i.e. the degree of integration within and segregation between networks, is significantly reduced after a stroke, but partially recovered over time. Importantly, the return of modular network structure parallels recovery of language and attention, but not motor function. This work establishes the importance of normalization of large-scale modular brain systems in stroke recovery.
In chapter 7, we discuss some fundamental revisions of past lesion-deficit frameworks necessitated by recent findings. Firstly, anatomical priors of structural and functional connections are needed to explain why certain lesions across distant locations should share behavioral consequences. Secondly, functional priors of connectomics are needed to explain how local injury can produce widespread disruption to brain connectivity and behavior that have been observed
PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention
Generating 3D point clouds is challenging yet highly desired. This work
presents a novel autoregressive model, PointGrow, which can generate diverse
and realistic point cloud samples from scratch or conditioned on semantic
contexts. This model operates recurrently, with each point sampled according to
a conditional distribution given its previously-generated points, allowing
inter-point correlations to be well-exploited and 3D shape generative processes
to be better interpreted. Since point cloud object shapes are typically encoded
by long-range dependencies, we augment our model with dedicated self-attention
modules to capture such relations. Extensive evaluations show that PointGrow
achieves satisfying performance on both unconditional and conditional point
cloud generation tasks, with respect to realism and diversity. Several
important applications, such as unsupervised feature learning and shape
arithmetic operations, are also demonstrated
The effects of hemodynamic lag on functional connectivity and behavior after stroke
Stroke disrupts the brain's vascular supply, not only within but also outside areas of infarction. We investigated temporal delays (lag) in resting state functional magnetic resonance imaging signals in 130 stroke patients scanned two weeks, three months and 12 months post stroke onset. Thirty controls were scanned twice at an interval of three months. Hemodynamic lag was determined using cross-correlation with the global gray matter signal. Behavioral performance in multiple domains was assessed in all patients. Regional cerebral blood flow and carotid patency were assessed in subsets of the cohort using arterial spin labeling and carotid Doppler ultrasonography. Significant hemodynamic lag was observed in 30% of stroke patients sub-acutely. Approximately 10% of patients showed lag at one-year post-stroke. Hemodynamic lag corresponded to gross aberrancy in functional connectivity measures, performance deficits in multiple domains and local and global perfusion deficits. Correcting for lag partially normalized abnormalities in measured functional connectivity. Yet post-stroke FC-behavior relationships in the motor and attention systems persisted even after hemodynamic delays were corrected. Resting state fMRI can reliably identify areas of hemodynamic delay following stroke. Our data reveal that hemodynamic delay is common sub-acutely, alters functional connectivity, and may be of clinical importance
Ponderings on the Possible Preponderance of Perpendicular Planets
Misalignments between planetary orbits and the equatorial planes of their
host stars are clues about the formation and evolution of planetary systems.
Earlier work found evidence for a peak near in the distribution of
stellar obliquities, based on frequentist tests. We performed hierarchical
Bayesian inference on a sample of 174 planets for which either the full
three-dimensional stellar obliquity has been measured (72 planets) or for which
only the sky-projected stellar obliquity has been measured (102 planets). We
investigated whether the obliquities are best described by a Rayleigh
distribution, or by a mixture of a Rayleigh distribution representing
well-aligned systems and a different distribution representing misaligned
systems. The mixture models are strongly favored over the single-component
distribution. For the misaligned component, we tried an isotropic distribution
and a distribution peaked at 90, and found the evidence to be
essentially the same for both models. Thus, our Bayesian inference engine did
not find strong evidence favoring a "perpendicular peak,'' unlike the
frequentist tests. We also investigated selection biases that affect the
inferred obliquity distribution, such as the bias of the gravity-darkening
method against obliquities near or . Further progress in
characterizing the obliquity distribution will probably require the
construction of a more homogeneous and complete sample of measurements.Comment: 15 pages, accepted to ApJ Letter
Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)
Automated vehicles' neural networks suffer from overfit, poor
generalizability, and untrained edge cases due to limited data availability.
Researchers synthesize randomized edge-case scenarios to assist in the training
process, though simulation introduces potential for overfit to latent rules and
features. Automating worst-case scenario generation could yield informative
data for improving self driving. To this end, we introduce a "Physically
Adversarial Intelligent Network" (PAIN), wherein self-driving vehicles interact
aggressively in the CARLA simulation environment. We train two agents, a
protagonist and an adversary, using dueling double deep Q networks (DDDQNs)
with prioritized experience replay. The coupled networks alternately
seek-to-collide and to avoid collisions such that the "defensive" avoidance
algorithm increases the mean-time-to-failure and distance traveled under
non-hostile operating conditions. The trained protagonist becomes more
resilient to environmental uncertainty and less prone to corner case failures
resulting in collisions than the agent trained without an adversary
Developing a Taxonomy of Elements Adversarial to Autonomous Vehicles
As highly automated vehicles reach higher deployment rates, they find
themselves in increasingly dangerous situations. Knowing that the consequence
of a crash is significant for the health of occupants, bystanders, and
properties, as well as to the viability of autonomy and adjacent businesses, we
must search for more efficacious ways to comprehensively and reliably train
autonomous vehicles to better navigate the complex scenarios with which they
struggle. We therefore introduce a taxonomy of potentially adversarial elements
that may contribute to poor performance or system failures as a means of
identifying and elucidating lesser-seen risks. This taxonomy may be used to
characterize failures of automation, as well as to support simulation and
real-world training efforts by providing a more comprehensive classification
system for events resulting in disengagement, collision, or other negative
consequences. This taxonomy is created from and tested against real collision
events to ensure comprehensive coverage with minimal class overlap and few
omissions. It is intended to be used both for the identification of
harm-contributing adversarial events and in the generation thereof (to create
extreme edge- and corner-case scenarios) in training procedures.Comment: 18 pages total, 4 pages of references, initial page left blank for
IEEE submission statement. Includes 4 figures and 2 tables. Written using
IEEEtran document clas
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