Author
Edith Pomarol-Clotet
Other affiliations: Johns Hopkins University, University of Pennsylvania, University of Cambridge ...read more
Bio: Edith Pomarol-Clotet is an academic researcher from University of Barcelona. The author has contributed to research in topics: Medicine & Schizophrenia (object-oriented programming). The author has an hindex of 40, co-authored 169 publications receiving 5678 citations. Previous affiliations of Edith Pomarol-Clotet include Johns Hopkins University & University of Pennsylvania.
Papers published on a yearly basis
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University of Southern California1, Oslo University Hospital2, University of Oslo3, University of Pittsburgh4, Oxford Health NHS Foundation Trust5, University of Cape Town6, University of Texas Health Science Center at Houston7, Heidelberg University8, Neuroscience Research Australia9, Karolinska Institutet10, King's College London11, City University London12, University of Münster13, Icahn School of Medicine at Mount Sinai14, University of Barcelona15, Brown University16, French Institute of Health and Medical Research17, University of Pennsylvania18, Utrecht University19, University of Göttingen20, University of Amsterdam21, Yale University22, Hartford Hospital23, National University of Ireland, Galway24, University of São Paulo25, University of Edinburgh26, West Los Angeles College27, University of California, Los Angeles28, University of California, Irvine29, Medical College of Wisconsin30, McGovern Institute for Brain Research31, Radboud University Nijmegen32, Northumberland, Tyne and Wear NHS Foundation Trust33, National Institutes of Health34, University of California, San Diego35, Dresden University of Technology36, University of Adelaide37, Semel Institute for Neuroscience and Human Behavior38, Pasteur Institute39, University of Birmingham40, VU University Medical Center41, University of Cincinnati Academic Health Center42, University of New South Wales43, Poznan University of Medical Sciences44, Dalhousie University45, Karolinska University Hospital46, University of Gothenburg47, University of Exeter48, Janssen Pharmaceutica49, Cardiff University50
TL;DR: The largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of bipolar disorder patients is performed, revealing previously undetected associations and providing an extensive analysis of potential confounding variables in neuroimaging studies of BD.
Abstract: Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10-21), left fusiform gyrus (d=-0.288; P=8.25 × 10-21) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10-19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.
525 citations
TL;DR: Patients with schizophrenia show both failure to activate and failure to deactivate during performance of a working memory task, including an area in the anterior prefrontal/anterior cingulate cortex that corresponds to one of the two midline components of the ‘default mode network’ implicated in functions related to maintaining one's sense of self.
Abstract: BackgroundFunctional imaging studies using working memory tasks have documented both prefrontal cortex (PFC) hypo- and hyperactivation in schizophrenia. However, these studies have often failed to consider the potential role of task-related deactivation.MethodThirty-two patients with chronic schizophrenia and 32 age- and sex-matched normal controls underwent functional magnetic resonance imaging (fMRI) scanning while performing baseline, 1-back and 2-back versions of the n-back task. Linear models were used to obtain maps of activations and deactivations in the groups.ResultsThe controls showed activation in the expected frontal regions. There were also clusters of deactivation, particularly in the anterior cingulate/ventromedial PFC and the posterior cingulate cortex/precuneus. Compared to the controls, the schizophrenic patients showed reduced activation in the right dorsolateral prefrontal cortex (DLPFC) and other frontal areas. There was also an area in the anterior cingulate/ventromedial PFC where the patients showed apparently greater activation than the controls. This represented a failure of deactivation in the schizophrenic patients. Failure to activate was a function of the patients' impaired performance on the n-back task, whereas the failure to deactivate was less performance dependent.ConclusionsPatients with schizophrenia show both failure to activate and failure to deactivate during performance of a working memory task. The area of failure of deactivation is in the anterior prefrontal/anterior cingulate cortex and corresponds to one of the two midline components of the ‘default mode network’ implicated in functions related to maintaining one's sense of self.
305 citations
TL;DR: Anisotropic kernels are introduced, which assign different values to the different neighboring voxels based on the spatial correlation between them, specifically developed for effect-size signed differential mapping (ES-SDM), though might be easily implemented in other meta-analysis packages such as activation likelihood estimation (ALE).
Abstract: Peak-based meta-analyses of neuroimaging studies create, for each study, a brain map of effect size or peak likelihood by convolving a kernel with each reported peak A kernel is a small matrix applied in order that voxels surrounding the peak have a value similar to, but slightly lower than that of the peak Current kernels are isotropic, ie, the value of a voxel close to a peak only depends on the Euclidean distance between the voxel and the peak However, such perfect spheres of effect size or likelihood around the peak are rather implausible: a voxel that correlates with the peak across individuals is more likely to be part of the cluster of significant activation or difference than voxels uncorrelated with the peak This paper introduces anisotropic kernels, which assign different values to the different neighboring voxels based on the spatial correlation between them They are specifically developed for effect-size signed differential mapping (ES-SDM), though might be easily implemented in other meta-analysis packages such as activation likelihood estimation (ALE) The paper also describes the creation of the required correlation templates for gray matter/BOLD response, white matter, cerebrospinal fluid, and fractional anisotropy Finally, the new method is validated by quantifying the accuracy of the recreation of effect size maps from peak information This empirical validation showed that the optimal degree of anisotropy and full-width at half-maximum (FWHM) might vary largely depending on the specific data meta-analyzed However, it also showed that the recreation substantially improved and did not depend on the FWHM when full anisotropy was used Based on these results, we recommend the use of fully anisotropic kernels in ES-SDM and ALE, unless optimal meta-analysis-specific parameters can be estimated based on the recreation of available statistical maps The new method and templates are freely available at http://wwwsdmprojectcom/
284 citations
TL;DR: The addition of risperidone to clozapine did not improve symptoms in patients with severe schizophrenia, and there was no statistically significant difference in symptomatic benefit between augmentation with ris peridone and placebo.
Abstract: Background The treatment of schizophrenia with multiple antipsychotic drugs is common, but the benefits and risks are not known. Methods In a randomized, double-blind study, we evaluated patients with schizophrenia and a poor response to treatment with clozapine. The patients continued to take clozapine and were randomly assigned to receive eight weeks of daily augmentation with 3 mg of risperidone or with placebo. This course of treatment was followed by an optional 18 weeks of augmentation with risperidone. The primary outcome was reduction in the total score for severity of symptoms on the Positive and Negative Syndrome Scale (PANSS). The secondary outcomes included cognitive functioning. Results
252 citations
TL;DR: Modafinil had some cognitive enhancing properties in schizophrenia similar to those observed in healthy adults and adult patients with ADHD, and may have potential as an important therapy for cognitive impairment in patients with schizophrenia, particularly because of its beneficial effects on attentional set shifting.
244 citations
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.
Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or
7,563 citations
TL;DR: Recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity are reviewed.
Abstract: The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.
6,135 citations
TL;DR: To identify regions of high connectivity in the human cerebral cortex, a computationally efficient approach was applied to map the degree of intrinsic functional connectivity across the brain and explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer's disease (AD).
Abstract: Recent evidence suggests that some brain areas act as hubs interconnecting distinct, functionally specialized systems. These nexuses are intriguing because of their potential role in integration and also because they may augment metabolic cascades relevant to brain disease. To identify regions of high connectivity in the human cerebral cortex, we applied a computationally efficient approach to map the degree of intrinsic functional connectivity across the brain. Analysis of two separate functional magnetic resonance imaging datasets (each n = 24) demonstrated hubs throughout heteromodal areas of association cortex. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Network analysis revealed that many, but not all, hubs were located within regions previously implicated as components of the default network. A third dataset (n = 12) demonstrated that the locations of hubs were present across passive and active task states, suggesting that they reflect a stable property of cortical network architecture. To obtain an accurate reference map, data were combined across 127 participants to yield a consensus estimate of cortical hubs. Using this consensus estimate, we explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer's disease (AD) because some models suggest that regions of high activity and metabolism accelerate pathology. Positron emission tomography amyloid imaging in AD (n = 10) compared with older controls (n = 29) showed high amyloid-beta deposition in the locations of cortical hubs consistent with the possibility that hubs, while acting as critical way stations for information processing, may also augment the underlying pathological cascade in AD.
2,569 citations
TL;DR: Evidence that certain brain disorders, such as schizophrenia, epilepsy, autism, Alzheimer's disease, and Parkinson's are associated with abnormal neural synchronization is reviewed to suggest close correlations between abnormalities in neuronal synchronization and cognitive dysfunctions.
1,956 citations