Liana C. L. Portugal

Bio: Liana C. L. Portugal is an academic researcher from Federal Fluminense University. The author has contributed to research in topics: Poison control & Pattern recognition (psychology). The author has an hindex of 12, co-authored 22 publications receiving 525 citations. Previous affiliations of Liana C. L. Portugal include University College London.

COVID-19 pandemic impact on children and adolescents' mental health: Biological, environmental, and social factors.

Abstract: Since the Coronavirus disease 2019 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was announced, we had an unprecedented change in the way we organize ourselves socially and in our daily routine. Children and adolescents were also greatly impacted by the abrupt withdrawal from school, social life and outdoor activities. Some of them also experienced domestic violence growing. The stress they are subjected to directly impacts their mental health on account of increased anxiety, changes in their diets and in school dynamics, fear or even failing to scale the problem. Our aim is to bring up a discussion under different aspects and to alert public health and government agents about the need for surveillance and care of these individuals. We hope that the damage to their mental health as a result of the side effect of this pandemic can be mitigated by adequate and timely intervention.

Sparse Network-Based Models for Patient Classification Using fMRI

Abstract: Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has been successful at discriminating psychiatric patients from healthy subjects. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. As is generally accepted, many psychiatric disorders, such as depression and schizophrenia, are brain connectivity disorders. Therefore, pattern recognition based on network models should provide more scientific insight and potentially more powerful predictions than voxel-based approaches. Here, we build a sparse network-based discriminative modelling framework, based on Gaussian graphical models and L1-norm regularised linear Support Vector Machines (SVM). The proposed framework provides easier pattern interpretation in terms of underlying network changes between groups, and we illustrate our technique by classifying patients with depression and controls, using fMRI data from a sad facial processing task.

Embedding anatomical or functional knowledge in whole-brain multiple kernel learning models

Abstract: Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In this work, we present a novel approach that uses group sparsity to model the whole brain multivariate pattern as a combination of regional patterns. More specifically, we use a sparse version of Multiple Kernel Learning (MKL) to simultaneously learn the contribution of each brain region, previously defined by an atlas, to the decision function. Our application of MKL provides two beneficial features: (1) it can lead to improved overall generalisation performance when the grouping structure imposed by the atlas is consistent with the data; (2) it can identify a subset of relevant brain regions for the predictive model. In order to investigate the effect of the grouping in the proposed MKL approach we compared the results of three different atlases using three different datasets. The method has been implemented in the new version of the open-source Pattern Recognition for Neuroimaging Toolbox (PRoNTo).

Beta-band oscillations--signalling the status quo?

Abstract: In this review, we consider the potential functional role of beta-band oscillations, which at present is not yet well understood. We discuss evidence from recent studies on top-down mechanisms involved in cognitive processing, on the motor system and on the pathophysiology of movement disorders that suggest a unifying hypothesis: beta-band activity seems related to the maintenance of the current sensorimotor or cognitive state. We hypothesize that beta oscillations and/or coupling in the beta-band are expressed more strongly if the maintenance of the status quo is intended or predicted, than if a change is expected. Moreover, we suggest that pathological enhancement of beta-band activity is likely to result in an abnormal persistence of the status quo and a deterioration of flexible behavioural and cognitive control.

Resting-state connectivity biomarkers define neurophysiological subtypes of depression

Abstract: Biomarkers have transformed modern medicine but remain largely elusive in psychiatry, partly because there is a weak correspondence between diagnostic labels and their neurobiological substrates. Like other neuropsychiatric disorders, depression is not a unitary disease, but rather a heterogeneous syndrome that encompasses varied, co-occurring symptoms and divergent responses to treatment. By using functional magnetic resonance imaging (fMRI) in a large multisite sample (n = 1,188), we show here that patients with depression can be subdivided into four neurophysiological subtypes (‘biotypes’) defined by distinct patterns of dysfunctional connectivity in limbic and frontostriatal networks. Clustering patients on this basis enabled the development of diagnostic classifiers (biomarkers) with high (82–93%) sensitivity and specificity for depression subtypes in multisite validation (n = 711) and out-of-sample replication (n = 477) data sets. These biotypes cannot be differentiated solely on the basis of clinical features, but they are associated with differing clinical-symptom profiles. They also predict responsiveness to transcranial magnetic stimulation therapy (n = 154). Our results define novel subtypes of depression that transcend current diagnostic boundaries and may be useful for identifying the individuals who are most likely to benefit from targeted neurostimulation therapies.

Similarities and Differences

Abstract: Compare your culture to one of the cultures discussed in this unit. On a sheet of paper, list the cultures you are comparing and make one column titled “similarities,” and a second column titled “differences.” Now, list as many similarities and differences between the two as you can think of. Are there more similarities or differences between the two cultures you selected? Have you ever met anyone from this culture? How can you use this information to build greater respect between cultures?

Depression and anxiety.

Abstract: Depression and anxiety are the most common psychiatric conditions in late life. Despite their prevalence, we know relatively little about their unique manifestation in older adults. And, Although the most common intervention for late-life depression and anxiety continues to be medication, research on psychosocial interventions for late-life depression and anxiety has burgeoned in the past several years. Unfortunately, this growing body of intervention research has yet to be widely translated into improved systems of care for late-life depression. This article is one step toward synthesizing the knowledge in this growing area of research. The review of literature presents the conclusions of several meta-analyses that have reviewed psychosocial interventions for late-life depression and anxiety. In addition, intervention studies concerning the effectiveness of cognitive behavioral therapy, interpersonal therapy, reminiscence therapy, and alternative therapies with depressed and/or anxious older adults are reviewed. A brief description of various approaches to psychosocial intervention with anxious and/or depressed older adults is also presented.