956 research outputs found
A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
Epilepsy surgery is challenging and the use of 3D multimodality image integration (3DMMI) to aid presurgical planning is well-established. Multimodality image integration can be technically demanding, and is underutilised in clinical practice. We have developed a single software platform for image integration, 3D visualization and surgical planning. Here, our pipeline is described in step-by-step fashion, starting with image acquisition, proceeding through image co-registration, manual segmentation, brain and vessel extraction, 3D visualization and manual planning of stereoEEG (SEEG) implantations. With dissemination of the software this pipeline can be reproduced in other centres, allowing other groups to benefit from 3DMMI. We also describe the use of an automated, multi-trajectory planner to generate stereoEEG implantation plans. Preliminary studies suggest this is a rapid, safe and efficacious adjunct for planning SEEG implantations. Finally, a simple solution for the export of plans and models to commercial neuronavigation systems for implementation of plans in the operating theater is described. This software is a valuable tool that can support clinical decision making throughout the epilepsy surgery pathway
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Brain covariance selection: better individual functional connectivity models using population prior
Spontaneous brain activity, as observed in functional neuroimaging, has been
shown to display reproducible structure that expresses brain architecture and
carries markers of brain pathologies. An important view of modern neuroscience
is that such large-scale structure of coherent activity reflects modularity
properties of brain connectivity graphs. However, to date, there has been no
demonstration that the limited and noisy data available in spontaneous activity
observations could be used to learn full-brain probabilistic models that
generalize to new data. Learning such models entails two main challenges: i)
modeling full brain connectivity is a difficult estimation problem that faces
the curse of dimensionality and ii) variability between subjects, coupled with
the variability of functional signals between experimental runs, makes the use
of multiple datasets challenging. We describe subject-level brain functional
connectivity structure as a multivariate Gaussian process and introduce a new
strategy to estimate it from group data, by imposing a common structure on the
graphical model in the population. We show that individual models learned from
functional Magnetic Resonance Imaging (fMRI) data using this population prior
generalize better to unseen data than models based on alternative
regularization schemes. To our knowledge, this is the first report of a
cross-validated model of spontaneous brain activity. Finally, we use the
estimated graphical model to explore the large-scale characteristics of
functional architecture and show for the first time that known cognitive
networks appear as the integrated communities of functional connectivity graph.Comment: in Advances in Neural Information Processing Systems, Vancouver :
Canada (2010
Local Analysis of Human Cortex in MRI Brain Volume
This paper describes a method for subcortical identification and labeling of
3D medical MRI images. Indeed, the ability to identify similarities between the most characteristic subcortical structures such as sulci and gyri is helpful for human brain mapping studies in general and medical diagnosis in particular. However, these structures vary greatly from one individual to another because they have different geometric properties. For this purpose, we have developed an efficient tool that allows a user to start with brain imaging, to segment the border gray/white matter, to simplify the obtained cortex surface, and to describe this shape locally in order to identify homogeneous features. In this paper, a segmentation procedure using geometric curvature properties that provide an efficient discrimination for local shape is implemented on the brain cortical surface. Experimental results demonstrate the effectiveness and the validity of our approach
ROI-Based Analysis of Functional Imaging Data
In this technical report, we present fMRI analysis techniques that test functional hypotheses at the region of interest (ROI) level. An SPM-compatible Matlab toolbox has been developed which allows the creation of subject-specific ROI masks based on anatomical markers and the testing of functional hypotheses on the regional response using multivariate time-series analysis techniques. The combined application of subject-specific ROI definition and region-level functional analysis is shown to appropriately compensate for inter-subject anatomical variability, offering finer localization and increased sensitivity to task-related effects than standard techniques based on whole brain normalization and voxel or cluster-level functional analysis, while providing a more direct link between discrete brain region hypotheses and the statistical analyses used to test them.National Institute of Health (R29 DC02852, ROI DC02852
Inflammation causes mood changes through alterations in subgenual cingulate activity and mesolimbic connectivity
BACKGROUND: Inflammatory cytokines are implicated in the pathophysiology of depression. In rodents, systemically administered inflammatory cytokines induce depression-like behavior. Similarly in humans, therapeutic interferon-alpha induces clinical depression in a third of patients. Conversely, patients with depression also show elevated pro-inflammatory cytokines.
OBJECTIVES: To determine the neural mechanisms underlying inflammation-associated mood change and modulatory effects on circuits involved in mood homeostasis and affective processing.
METHODS: In a double-blind, randomized crossover study, 16 healthy male volunteers received typhoid vaccination or saline (placebo) injection in two experimental sessions. Mood questionnaires were completed at baseline and at 2 and 3 hours. Two hours after injection, participants performed an implicit emotional face perception task during functional magnetic resonance imaging. Analyses focused on neurobiological correlates of inflammation-associated mood change and affective processing within regions responsive to emotional expressions and implicated in the etiology of depression.
RESULTS: Typhoid but not placebo injection produced an inflammatory response indexed by increased circulating interleukin-6 and significant mood reduction at 3 hours. Inflammation-associated mood deterioration correlated with enhanced activity within subgenual anterior cingulate cortex (sACC) (a region implicated in the etiology of depression) during emotional face processing. Furthermore, inflammation-associated mood change reduced connectivity of sACC to amygdala, medial prefrontal cortex, nucleus accumbens, and superior temporal sulcus, which was modulated by peripheral interleukin-6.
CONCLUSIONS: Inflammation-associated mood deterioration is reflected in changes in sACC activity and functional connectivity during evoked responses to emotional stimuli. Peripheral cytokine
Learning and comparing functional connectomes across subjects
Functional connectomes capture brain interactions via synchronized
fluctuations in the functional magnetic resonance imaging signal. If measured
during rest, they map the intrinsic functional architecture of the brain. With
task-driven experiments they represent integration mechanisms between
specialized brain areas. Analyzing their variability across subjects and
conditions can reveal markers of brain pathologies and mechanisms underlying
cognition. Methods of estimating functional connectomes from the imaging signal
have undergone rapid developments and the literature is full of diverse
strategies for comparing them. This review aims to clarify links across
functional-connectivity methods as well as to expose different steps to perform
a group study of functional connectomes
Brain-machine interface using electrocorticography in humans
Paralysis has a severe impact on a patient’s quality of life and entails a high emotional burden and life-long social and financial costs. More than 5 million people in the USA suffer from some form of paralysis and about 50% of the people older than 65 experience difficulties or inabilities with movement. Restoring movement and communication for patients with neurological and motor disorders, stroke and spinal cord injuries remains a challenging clinical problem without an adequate solution.
A brain-machine interface (BMI) allows subjects to control a device, such as a computer cursor or an artificial hand, exclusively by their brain activity. BMIs can be used to control communication and prosthetic devices, thereby restoring the communication and movement capabilities of the paralyzed patients. So far, most powerful BMIs have been realized by extracting movement parameters from the activity of single neurons. To record such activity, electrodes have to penetrate the brain tissue, thereby generating risk of brain injury. In addition, recording instability, due to small movements of the electrodes within the brain and the neuronal tissue response to the electrode implant, is also an issue. In this thesis, I investigate whether electrocorticography (ECoG), an alternative recording technique, can be used to achieve BMIs with similar accuracy.
First, I demonstrate a BMI based on the approach of extracting movement parameters from ECoG signals. Such ECoG based BMI can further be improved using supervised adaptive algorithms. To implement such algorithms, it is necessary to continuously receive feedback from the subject whether the BMI-decoded trajectory was correct or incorrect. I show that, by using the same ECoG recordings, neuronal responses to trajectory errors can be recorded, detected and differentiated from other types of errors. Finally, I devise a method that could be used to improve the detection of error related neuronal responses
- …