Neural basis of language switching in the brain: fMRI evidence from Korean-Chinese early bilinguals

Using fMRI, we conducted two types of property generation task that involved language switching, with early bilingual speakers of Korean and Chinese. The first is a more conventional task in which a single language (L1 or L2) was used within each trial, but switched randomly from trial to trial. The other consists of a novel experimental design where language switching happens within each trial, alternating in the direction of the L1/L2 translation required. Our findings support a recently introduced cognitive model, the 'hodological' view of language switching proposed by Moritz-Gasser and Duffau. The nodes of a distributed neural network that this model proposes are consistent with the informative regions that we extracted in this study, using both GLM methods and Multivariate Pattern Analyses: the supplementary motor area, caudate, supramarginal gyrus and fusiform gyrus and other cortical areas. 

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

New methodologies for directed functional connectivity applied to brain neuroimaging research

Tese de doutoramento, Engenharia Biomédica e Biofísica, Universidade de Lisboa, Faculdade de Ciências, 2015

Investigating the causal role of rightOFA in holistic detection of Mooney faces and objects: an fMRI-guided TMS study

Background The right occipital face area (rOFA) is known to be involved in face discrimination based on local featural information. Whether this region is involved in global, holistic stimulus processing is not known. Objective We used fMRI-guided transcranial magnetic stimulation (TMS) to investigate whether rOFA is causally implicated in stimulus detection based on holistic processing, by the use of Mooney stimuli. Methods Two studies were carried out: In Experiment 1, participants performed a detection task involving Mooney faces and Mooney objects; Mooney stimuli lack distinguishable local features and can be detected solely via holistic processing (i.e. at a global level) with top-down guidance from previously stored representations. Experiment 2 required participants to detect shapes which are recognized via bottom-up integration of local (collinear) Gabor elements and was performed to control for specificity of rightOFA's implication in holistic detection. Results In Experiment 1, TMS over rOFA and rLO impaired detection of all stimulus categories, with no category-specific effect. In Experiment 2, shape detection was impaired when TMS was applied over rLO but not over rOFA. Conclusions Our results demonstrate that rOFA is causally implicated in the type of top-down holistic detection required by Mooney stimuli and that such role is not face-selective. In contrast, rOFA does not appear to play a causal role in in detection of shapes based on bottom-up integration of local components, demonstrating that its involvement in processing non-face stimuli is specific for holistic processing.

Assessing residual reasoning ability in overtly non-communicative patients using fMRI

It is now well established that some patients who are diagnosed as being in a vegetative state or a minimally conscious state show reliable signs of volition that may only be detected by measuring neural responses. A pertinent question is whether these patients are also capable of logical thought. Here, we validate an fMRI paradigm that can detect the neural fingerprint of reasoning processes and moreover, can confirm whether a participant derives logical answers. We demonstrate the efficacy of this approach in a physically non-communicative patient who had been shown to engage in mental imagery in response to simple audi- tory instructions. Our results demonstrate that this individual retains a remarkable capacity for higher cogni- tion, engaging in the reasoning task and deducing logical answers. We suggest that this approach is suitable for detecting residual reasoning ability using neural responses and could readily be adapted to assess other aspects of cognition.

Análise da sonolência na condução rodoviária a partir de sinais EEG

A Sociedade Europeia de Pesquisa do Sono realizou muito recentemente um estudo, onde mostrou que a prevalência média de adormecimento ao volante nos últimos 2 anos foi de 17%. Além disto, tem sido provado por todo o mundo que a sonolência durante a condução é uma das principais causas de acidentes de trânsito. Torna-se assim conveniente, o desenvolvimento de sistemas que analisem a suscetibilidade de um determinado condutor para adormecer no trânsito, bem como de ferramentas que monitorem em tempo real o estado físico e mental do condutor, para alertarem nos momentos críticos. Apesar do estudo do sono se ter iniciado há vários anos, a maioria das investigações focaram-se no ciclo normal do sono, estudando os indivíduos de forma relaxada e de olhos fechados. Só mais recentemente, têm surgido os estudos que se focam nas situações de sonolência em atividade, como _e o caso da condução. Uma grande parte Dos estudos da sonolência em condução têm utilizado a eletroencefalografia (EEG), de forma a perceber se existem alterações nas diferentes bandas de frequência desta, que possam indicar o estado de sonolência do condutor. Além disso, a evolução da sonolência a partir de alterações no piscar dos olhos (que podem ser vistas nos sinais EEG) também tem sido alvo de grande pesquisa, tendo vindo a revelar resultados bastante promissores. Neste contexto e em parceria com a empresa HealthyRoad, esta tese está integrada no projeto HealthyDrive, que visa o desenvolvimento de um sistema de alerta e deteção de sinais de fadiga e sonolência nos condutores de veículos automóveis. A contribuição desta tese no projeto prendeu-se com o estudo da sonolência dos indivíduos em condução a partir de sinais EEG, para desta forma investigar possíveis indicadores dos diferentes níveis desta que possam ser utilizados pela empresa no projeto. Foram recolhidos e analisados 17 sinais EEG de indivíduos em simulação de condução. Além disso foram desenvolvidos dois métodos de análise destes sinais: O primeiro para a deteção e análise dos piscar de olhos a partir de EEG, o segundo para análise do espetro de potência. Ambos os métodos foram utilizados para analisar os sinais recolhidos e investigar que tipo de relação existe entre a sonolência do condutor e as alterações nos piscares dos olhos, bem como as alterações do espetro do EEG. Os resultados mostraram uma correlação entre a duração do piscar de olhos e a sonolência do condutor. Com o aumento da sonolência velicou-se um aumento da duração do piscar, desencadeado principalmente pelo aumento na duração de fecho, que chegou aos 51.2%. Em relação ao espectro de potência, os resultados sugerem que a potência relativa de todas as bandas analisadas fornecem informações relevantes sobre a sonolência do condutor. Além disso, o parâmetro (_+_)/_ demostrou estar relacionado com variações da sonolência, diminuindo com o seu avanço e aumentando significativamente (111%) no instante em que os condutores adormeceram.

Diagnostic yield of short-term video-EEG monitoring for epilepsy and PNESs: a European assessment.

INTRODUCTION: Although long-term video-EEG monitoring (LVEM) is routinely used to investigate paroxysmal events, short-term video-EEG monitoring (SVEM) lasting <24 h is increasingly recognized as a cost-effective tool. Since, however, relatively few studies addressed the yield of SVEM among different diagnostic groups, we undertook the present study to investigate this aspect. METHODS: We retrospectively analyzed 226 consecutive SVEM recordings over 6 years. All patients were referred because routine EEGs were inconclusive. Patients were classified into 3 suspected diagnostic groups: (1) group with epileptic seizures, (2) group with psychogenic nonepileptic seizures (PNESs), and (3) group with other or undetermined diagnoses. We assessed recording lengths, interictal epileptiform discharges, epileptic seizures, PNESs, and the definitive diagnoses obtained after SVEM. RESULTS: The mean age was 34 (±18.7) years, and the median recording length was 18.6 h. Among the 226 patients, 127 referred for suspected epilepsy - 73 had a diagnosis of epilepsy, none had a diagnosis of PNESs, and 54 had other or undetermined diagnoses post-SVEM. Of the 24 patients with pre-SVEM suspected PNESs, 1 had epilepsy, 12 had PNESs, and 11 had other or undetermined diagnoses. Of the 75 patients with other diagnoses pre-SVEM, 17 had epilepsy, 11 had PNESs, and 47 had other or undetermined diagnoses. After SVEM, 15 patients had definite diagnoses other than epilepsy or PNESs, while in 96 patients, diagnosis remained unclear. Overall, a definitive diagnosis could be reached in 129/226 (57%) patients. CONCLUSIONS: This study demonstrates that in nearly 3/5 patients without a definitive diagnosis after routine EEG, SVEM allowed us to reach a diagnosis. This procedure should be encouraged in this setting, given its time-effectiveness compared with LVEM.

Ear acupuncture and fMRI: a pilot study for assessing the specificity of auricular points.

In recent years research explored different acupuncture stimulation techniques but interest has focused primarily on somatic acupuncture and on a limited number of acupoints. As regards ear Acupuncture (EA) there is still some criticism about the clinical specificity of auricular points/areas representing organs or structures of the body. The aim of this study was to verify through (Functional magnetic resonance imaging) fMRI the hypothesis of EA point specificity using two auricular points having different topographical locations and clinical significance. Six healthy volunteers underwent two experimental fMRI sessions: the first was dedicated to the stimulation of Thumb Auricular Acupoint (TAA) and the second to the stimulation of Brain Stem Auricular Acupoint (BSAA). The stimulation of the needle placed in the TAA of the left ear produced an increase in activation bilaterally in the parietal operculum, region of the secondary somatosensory area SII. Stimulation of the needle placed in the BSAA of the left ear showed a pattern that largely overlapped regions belonging to the pain matrix, as shown to be involved in previous somatic acupuncture studies but with local differences in the left amygdala, anterior cingulate cortex, and cerebellum. The differences in activation patterns between TAA and BSAA stimulation support the specificity of the two acupoints. Moreover, the peculiarity of the regions involved in BSAA stimulation compared to those involved in the pain matrix, is in accordance with the therapeutic indications of this acupoint that include head pain, dizziness and vertigo. Our results provide preliminary evidence on the specificity of two auricular acupoints; further research is warranted by means of fMRI both in healthy volunteers and in patients carrying neurological/psychiatric syndromes.