3,341 research outputs found
Detecting Epileptic Regions Based on Global Brain Connectivity Patterns
We present a method to detect epileptic regions based on functional connectivity differences between individual epilepsy patients and a healthy population. Our model assumes that the global functional characteristics of these differences are shared across patients, but it allows for the epileptic regions to vary between individuals. We evaluate the detection performance against intracranial EEG observations and compare our approach with two baseline methods that use standard statistics. The baseline techniques are sensitive to the choice of thresholds, whereas our algorithm automatically estimates the appropriate model parameters and compares favorably with the best baseline results. This suggests the promise of our approach for pre-surgical planning in epilepsy.MIT/Lincoln Laboratory CollaborationNational Alliance for Medical Image Computing (U.S.) (grant (NIH NIBIB NAMIC U54-EB005149))Neuroimaging Analysis Center (U.S.) (NIH NCRR NAC P41-RR13218)Neuroimaging Analysis Center (U.S.) (NIH NIBIB NAC P41-EB-015902)National Science Foundation (U.S.) (NSF CAREER Grant 0642971
Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches
Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient
Graph analysis of functional brain networks: practical issues in translational neuroscience
The brain can be regarded as a network: a connected system where nodes, or
units, represent different specialized regions and links, or connections,
represent communication pathways. From a functional perspective communication
is coded by temporal dependence between the activities of different brain
areas. In the last decade, the abstract representation of the brain as a graph
has allowed to visualize functional brain networks and describe their
non-trivial topological properties in a compact and objective way. Nowadays,
the use of graph analysis in translational neuroscience has become essential to
quantify brain dysfunctions in terms of aberrant reconfiguration of functional
brain networks. Despite its evident impact, graph analysis of functional brain
networks is not a simple toolbox that can be blindly applied to brain signals.
On the one hand, it requires a know-how of all the methodological steps of the
processing pipeline that manipulates the input brain signals and extract the
functional network properties. On the other hand, a knowledge of the neural
phenomenon under study is required to perform physiological-relevant analysis.
The aim of this review is to provide practical indications to make sense of
brain network analysis and contrast counterproductive attitudes
Temporal Lobe Epilepsy Alters Auditory-motor Integration For Voice Control
Temporal lobe epilepsy (TLE) is the most common drug-refractory focal epilepsy in adults. Previous research has shown that patients with TLE exhibit decreased performance in listening to speech sounds and deficits in the cortical processing of auditory information. Whether TLE compromises auditory-motor integration for voice control, however, remains largely unknown. To address this question, event-related potentials (ERPs) and vocal responses to vocal pitch errors (1/2 or 2 semitones upward) heard in auditory feedback were compared across 28 patients with TLE and 28 healthy controls. Patients with TLE produced significantly larger vocal responses but smaller P2 responses than healthy controls. Moreover, patients with TLE exhibited a positive correlation between vocal response magnitude and baseline voice variability and a negative correlation between P2 amplitude and disease duration. Graphical network analyses revealed a disrupted neuronal network for patients with TLE with a significant increase of clustering coefficients and path lengths as compared to healthy controls. These findings provide strong evidence that TLE is associated with an atypical integration of the auditory and motor systems for vocal pitch regulation, and that the functional networks that support the auditory-motor processing of pitch feedback errors differ between patients with TLE and healthy controls
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The role of HG in the analysis of temporal iteration and interaural correlation
The Potential of the Human Connectome as a Biomarker of Brain Disease
The human connectome at the level of fiber tracts between brain regions has
been shown to differ in patients with brain disorders compared to healthy
control groups. Nonetheless, there is a potentially large number of different
network organizations for individual patients that could lead to cognitive
deficits prohibiting correct diagnosis. Therefore changes that can distinguish
groups might not be sufficient to diagnose the disease that an individual
patient suffers from and to indicate the best treatment option for that
patient. We describe the challenges introduced by the large variability of
connectomes within healthy subjects and patients and outline three common
strategies to use connectomes as biomarkers of brain diseases. Finally, we
propose a fourth option in using models of simulated brain activity (the
dynamic connectome) based on structural connectivity rather than the structure
(connectome) itself as a biomarker of disease. Dynamic connectomes, in addition
to currently used structural, functional, or effective connectivity, could be
an important future biomarker for clinical applications.Comment: Perspective Article for special issue on Magnetic Resonance Imaging
of Healthy and Diseased Brain Network
Functional Imaging of Autonomic Regulation: Methods and Key Findings.
Central nervous system processing of autonomic function involves a network of regions throughout the brain which can be visualized and measured with neuroimaging techniques, notably functional magnetic resonance imaging (fMRI). The development of fMRI procedures has both confirmed and extended earlier findings from animal models, and human stroke and lesion studies. Assessments with fMRI can elucidate interactions between different central sites in regulating normal autonomic patterning, and demonstrate how disturbed systems can interact to produce aberrant regulation during autonomic challenges. Understanding autonomic dysfunction in various illnesses reveals mechanisms that potentially lead to interventions in the impairments. The objectives here are to: (1) describe the fMRI neuroimaging methodology for assessment of autonomic neural control, (2) outline the widespread, lateralized distribution of function in autonomic sites in the normal brain which includes structures from the neocortex through the medulla and cerebellum, (3) illustrate the importance of the time course of neural changes when coordinating responses, and how those patterns are impacted in conditions of sleep-disordered breathing, and (4) highlight opportunities for future research studies with emerging methodologies. Methodological considerations specific to autonomic testing include timing of challenges relative to the underlying fMRI signal, spatial resolution sufficient to identify autonomic brainstem nuclei, blood pressure, and blood oxygenation influences on the fMRI signal, and the sustained timing, often measured in minutes of challenge periods and recovery. Key findings include the lateralized nature of autonomic organization, which is reminiscent of asymmetric motor, sensory, and language pathways. Testing brain function during autonomic challenges demonstrate closely-integrated timing of responses in connected brain areas during autonomic challenges, and the involvement with brain regions mediating postural and motoric actions, including respiration, and cardiac output. The study of pathological processes associated with autonomic disruption shows susceptibilities of different brain structures to altered timing of neural function, notably in sleep disordered breathing, such as obstructive sleep apnea and congenital central hypoventilation syndrome. The cerebellum, in particular, serves coordination roles for vestibular stimuli and blood pressure changes, and shows both injury and substantially altered timing of responses to pressor challenges in sleep-disordered breathing conditions. The insights into central autonomic processing provided by neuroimaging have assisted understanding of such regulation, and may lead to new treatment options for conditions with disrupted autonomic function
A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients
Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction
From Caenorhabditis elegans to the Human Connectome: A Specific Modular Organisation Increases Metabolic, Functional, and Developmental Efficiency
The connectome, or the entire connectivity of a neural system represented by
network, ranges various scales from synaptic connections between individual
neurons to fibre tract connections between brain regions. Although the
modularity they commonly show has been extensively studied, it is unclear
whether connection specificity of such networks can already be fully explained
by the modularity alone. To answer this question, we study two networks, the
neuronal network of C. elegans and the fibre tract network of human brains
yielded through diffusion spectrum imaging (DSI). We compare them to their
respective benchmark networks with varying modularities, which are generated by
link swapping to have desired modularity values but otherwise maximally random.
We find several network properties that are specific to the neural networks and
cannot be fully explained by the modularity alone. First, the clustering
coefficient and the characteristic path length of C. elegans and human
connectomes are both higher than those of the benchmark networks with similar
modularity. High clustering coefficient indicates efficient local information
distribution and high characteristic path length suggests reduced global
integration. Second, the total wiring length is smaller than for the
alternative configurations with similar modularity. This is due to lower
dispersion of connections, which means each neuron in C. elegans connectome or
each region of interest (ROI) in human connectome reaches fewer ganglia or
cortical areas, respectively. Third, both neural networks show lower
algorithmic entropy compared to the alternative arrangements. This implies that
fewer rules are needed to encode for the organisation of neural systems
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