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1.
BMC Pregnancy Childbirth ; 20(1): 587, 2020 Oct 06.
Artigo em Inglês | MEDLINE | ID: mdl-33023500

RESUMO

BACKGROUND: There are no published cases of tonic-clonic seizures and posterior bilateral blindness during pregnancy and Severe Acute Respiratory Syndrome (SARS) Coronavirus (COV) 2 (SARS-COV-2) infection. We do not just face new and unknown manifestations, but also how different patient groups are affected by SARS-COV-2 infection, such as pregnant women. Coronavirus Disease 2019 (COVID-19), preeclampsia, eclampsia and posterior reversible leukoencephalopathy share endothelium damage and similar pathophysiology. CASE PRESENTATION: A 35-year-old pregnant woman was admitted for tonic-clonic seizures and SARS-COV-2 infection. She had a normal pregnancy control and no other symptoms before tonic-clonic seizures development. After a Caesarean section (C-section) she developed high blood pressure, and we initiated antihypertensive treatment with labetalol, amlodipine and captopril. Few hours later she developed symptoms of cortical blindness that resolved in 72 h with normal brain computed tomography (CT) angiography. CONCLUSION: The authors conclude that SARS COV-2 infection could promote brain endothelial damage and facilitate neurological complications during pregnancy.


Assuntos
Anti-Hipertensivos/administração & dosagem , Betacoronavirus/isolamento & purificação , Cegueira Cortical , Cesárea/métodos , Infecções por Coronavirus , Eclampsia , Fibrinolíticos/administração & dosagem , Pandemias , Pneumonia Viral , Complicações Infecciosas na Gravidez , Convulsões , Adulto , Cegueira Cortical/diagnóstico , Cegueira Cortical/virologia , Encéfalo/diagnóstico por imagem , Angiografia por Tomografia Computadorizada/métodos , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/fisiopatologia , Diagnóstico Diferencial , Eclampsia/diagnóstico , Eclampsia/terapia , Eclampsia/virologia , Feminino , Humanos , Exame Neurológico/métodos , Pneumonia Viral/diagnóstico , Pneumonia Viral/fisiopatologia , Gravidez , Complicações Infecciosas na Gravidez/diagnóstico , Complicações Infecciosas na Gravidez/etiologia , Complicações Infecciosas na Gravidez/fisiopatologia , Resultado da Gravidez , Convulsões/diagnóstico , Convulsões/etiologia , Convulsões/terapia , Tomografia Computadorizada por Raios X/métodos , Resultado do Tratamento
2.
Artigo em Chinês | MEDLINE | ID: mdl-33040499

RESUMO

Objective:To analyze the correlation of the degree of affective disorder and brain function changes by comparing the differences of resting-state functional Magnetic Resonance Imaging(rs-fMRI) between healthy volunteers without tinnitus and patients with tinnitus. Method:A analysis of 19 patients with tinnitus and 15 healthy volunteers without tinnitus. The patients were divided into mild group and severe group according to tinnitus handicap inventory(THI). Rs-fMRI was collected and the regional homogeneity(ReHo) analysis, amplitude of low-frequency fluctuation(ALFF) analysis, and fractional amplitude of low frequency fluctuation(fALFF) analysis of rs-fMRI were performed by DPABI software. Two-sample t-test of the ReHo value, ALFF value and fALFF value between the mild group and the control group, the severe group and the control group, were performed respectively. Result:The fALFF value of the left occipital gyrus in the mild group was higher than that in the control group, the difference was statistically significant(P<0.05), but there is no statistically significant difference of ALFF value and ReHo value between two groups. The ALFF value of the middle temporal gyrus(left), superior frontal gyrus(right), inferior frontal gyrus pars triangularis(left) and caudate nucleus(left) in the severe group were higher than those of the control group. But there was no significant difference in the fALFF value and the ReHo value. Conclusion:Different severity of affective disorder in patients with tinnitus have different areas of brain function abnormalities. Mild group was detected by fALFF analysis and the active brain area was the left middle occipital region. Severe group was detected by ALFF analysis. The active brain regions were left middle temporal gyrus, right superior frontal gyrus, left inferior frontal gyrus pars triangularis, and left caudate nucleus.


Assuntos
Zumbido , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Humanos , Imagem por Ressonância Magnética , Transtornos do Humor , Zumbido/diagnóstico por imagem
3.
Zh Nevrol Psikhiatr Im S S Korsakova ; 120(8. Vyp. 2): 24-29, 2020.
Artigo em Russo | MEDLINE | ID: mdl-33016673

RESUMO

OBJECTIVE: To identify the likelihood of developing systemic inflammation (SI) as a general pathological process in severe haemorrhagic intracerebral stroke with and without the phenomenon of ineffective cerebral blood flow. MATERIAL AND METHOD: Three groups were examined: 1) 89 blood donors (controls), 2) 15 patients with severe haemorrhagic stroke without the phenomenon of ineffective brain blood flow; 3) 26 patients with severe haemorrhagic stroke with ineffective cerebral blood flow. Ineffective cerebral blood circulation was recorded on the basis of transcranial Doppler ultrasound data; 87% of patients had clinical signs of brain death. All patients in the groups with haemorrhagic stroke had signs of multiple organ dysfunction according to the Sepsis-related Organ Failure scale, all of them received intensive care. An integrated scale based on the determination of plasma concentrations of cytokines (IL-6, IL-8, IL-10, TNF-α), procalcitonin, cortisol, D-dimers, myoglobin, troponin I was used to verify systemic inflammation. RESULTS AND CONCLUSION: Systemic inflammation or borderline state (pre-SI) was identified in all patients of the second group both on 1-3 days from the onset of haemorrhagic stroke, and on 5-8 days. On the contrary, in the third group, there were no signs of SI on 1-3 days. On 5-8 days, signs of SI and pre-SI were recorded only in 18.2% of patients. Apparently, the reason for these differences is the blockade of the passage of tissue decay products and other pro-inflammatory factors into the bloodstream from the damaged brain in the third group.


Assuntos
Circulação Cerebrovascular , Hemorragias Intracranianas/complicações , Hemorragias Intracranianas/diagnóstico por imagem , Acidente Vascular Cerebral/complicações , Encéfalo/diagnóstico por imagem , Humanos , Inflamação
4.
Nat Commun ; 11(1): 5004, 2020 10 05.
Artigo em Inglês | MEDLINE | ID: mdl-33020473

RESUMO

Adaptive brain function requires that sensory impressions of the social and natural milieu are dynamically incorporated into intrinsic brain activity. While dynamic switches between brain states have been well characterised in resting state acquisitions, the remodelling of these state transitions by engagement in naturalistic stimuli remains poorly understood. Here, we show that the temporal dynamics of brain states, as measured in fMRI, are reshaped from predominantly bistable transitions between two relatively indistinct states at rest, toward a sequence of well-defined functional states during movie viewing whose transitions are temporally aligned to specific features of the movie. The expression of these brain states covaries with different physiological states and reflects subjectively rated engagement in the movie. In sum, a data-driven decoding of brain states reveals the distinct reshaping of functional network expression and reliable state transitions that accompany the switch from resting state to perceptual immersion in an ecologically valid sensory experience.


Assuntos
Encéfalo/fisiologia , Filmes Cinematográficos , Adulto , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Feminino , Frequência Cardíaca/fisiologia , Humanos , Imagem por Ressonância Magnética , Masculino , Filmes Cinematográficos/classificação , Filmes Cinematográficos/estatística & dados numéricos , Percepção/fisiologia , Pupila/fisiologia , Descanso/fisiologia , Inquéritos e Questionários , Adulto Jovem
7.
Artigo em Inglês | MEDLINE | ID: mdl-33017931

RESUMO

Affective personality traits have been associated with a risk of developing mental and cognitive disorders and can be informative for early detection and management of such disorders. However, conventional personality trait detection is often biased and unreliable, as it depends on the honesty of the subjects when filling out the lengthy questionnaires. In this paper, we propose a method for objective detection of personality traits using physiological signals. Subjects are shown affective images and videos to evoke a range of emotions. The electrical activity of the brain is captured using EEG during this process and the multi-channel EEG data is processed to compute the inter-hemispheric asynchrony of the brainwaves. The most discriminative features are selected and then used to build a machine learning classifier, which is trained to predict 16 personality traits. Our results show high predictive accuracy for both image and video stimuli individually, and an improvement when the two stimuli are combined, achieving a 95.49% accuracy. Most of the selected discriminative features were found to be extracted from the alpha frequency band. Our work shows that personality traits can be accurately detected with EEG data, suggesting possible use in practical applications for early detection of mental and cognitive disorders.


Assuntos
Ondas Encefálicas , Eletroencefalografia , Encéfalo/diagnóstico por imagem , Aprendizado de Máquina , Personalidade
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 506-509, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018038

RESUMO

We use random matrix theory (RMT) to investigate the statistical properties of brain functional networks in lower limb motor imagery. Functional connectivity was calculated by Pearson correlation coefficient (PCC), mutual information (MTI) and phase locking value (PLV) extracted from EEG signals. We found that when the measured subjects imagined the movements of their lower limbs the spectral density as well as the level spacings displayed deviations from the random matrix prediction. In particular, a significant difference between the left and right foot imaginary movements was observed in the maximum eigenvalue from the PCC, which can provide a theoretical basis for further study on the classification of unilateral movement of lower limbs.


Assuntos
Eletroencefalografia , Imaginação , Encéfalo/diagnóstico por imagem , Imagens, Psicoterapia , Movimento
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1023-1026, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018159

RESUMO

Short-duration bursts of spontaneous activity are important markers of maturation in the electroencephalogram (EEG) of premature infants. This paper examines the application of a feature-less machine learning approach for detecting these bursts. EEGs were recorded over the first 3 days of life for infants with a gestational age below 30 weeks. Bursts were annotated on the EEG from 36 infants. In place of feature extraction, the time-series EEG is transformed into a time-frequency distribution (TFD). A gradient boosting machine is then trained directly on the whole TFD using a leave-one-out procedure. TFD kernel parameters, length of the Doppler and lag windows, are selected within a nested cross-validation procedure during training. Results indicate that detection performance is sensitive to Doppler-window length but not lag-window length. Median area under the receiver operator characteristic for detection is 0.881 (inter-quartile range 0.850 to 0.913). Examination of feature importance highlights a critical wideband region <15 Hz in the TFD. Burst detection methods form an important component in any fully-automated brain-health index for the vulnerable preterm infant.


Assuntos
Doenças do Recém-Nascido , Recém-Nascido Prematuro , Encéfalo/diagnóstico por imagem , Eletroencefalografia , Humanos , Lactente , Recém-Nascido , Aprendizado de Máquina
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1051-1054, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018166

RESUMO

Oxygen deprivation (hypoxia) and reduced blood supply (ischemia) can occur before, during or shortly after birth and can result in death, brain damage and long-term disability. Assessing neuronal survival after hypoxia-ischemia in the near-term fetal sheep brain model is essential for the development of novel treatment strategies. As manual quantification of neurons in histological images varies between different assessors and is extremely time-consuming, automation of the process is needed and has not been currently achieved. To achieve automation, successfully segmenting the neurons from the background is very important. Due to presence of densely populated overlapping cells and with no prior information of shapes and sizes, the segmentation of neurons from the image is complex. Initially, we segmented the RGB images by using K-means clustering to primarily segment the neurons from the background based on their colour value, a distance transform for seed detection and watershed method for separating overlapping objects. However, this resulted in unsatisfactory sensitivity and performance due to over-segmentation if we use the RGB image directly. In this paper, we propose a semi-automated modified approach to segment neurons that tackles the over-segmentation issue that we encountered. Initially, we separated the red, green and blue colour channel information from the RGB image. We determined that by applying the same segmentation method first to the blue channel image, then by performing segmentation on the green channel for the neurons that remain unsegmented from the blue channel segmentation and finally by performing segmentation on red channel for neurons that were still unsegmented from the green channel segmentation, improved performance results could be achieved. The modified approach increased performance for the healthy and ischemic animal images from 89.7% to 98.08% and from 94.36% to 98.06% respectively as compared to using RGB image directly.


Assuntos
Feto , Fenômenos Fisiológicos do Sistema Nervoso , Animais , Encéfalo/diagnóstico por imagem , Feminino , Humanos , Neurônios , Gravidez , Cuidado Pré-Natal , Ovinos
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1071-1074, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018171

RESUMO

While Deep Learning methods have been successfully applied to tackle a wide variety of prediction problems, their application has been mostly limited to data structured in a grid-like fashion. However, the study of the human brain "connectome" involves the representation of the brain as a graph with interacting nodes. In this paper, we extend the Graph Attention Network (GAT), a novel neural network (NN) architecture acting on the features of the nodes of a binary graph, to handle a set of graphs provided with node features and non-binary edge weights. We demonstrate the effectiveness of our architecture by training it multimodal data collected from a large homogeneous fMRI dataset (n=1003 individuals with multiple fMRI sessions per subject) made publicly available by the Human Connectome Project (HCP), demonstrating good performance and seamless integration of multimodal neuroimaging data. Our adaptation provides a powerful and flexible deep learning tool to integrate multimodal neuroimaging connectomics data in a predictive context.


Assuntos
Encéfalo , Conectoma , Atenção , Encéfalo/diagnóstico por imagem , Humanos , Imagem por Ressonância Magnética , Neuroimagem
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1080-1083, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018173

RESUMO

Cerebral vascular territories are related to the clinical progression and outcome of ischemic stroke. The vascular territory map (VTM) helps to understand stroke pathophysiology and potentially the clinical prognosis. A VTM can be generated from the bolus arrival time map. However, previous methods require initial seed points to be chosen manually, and the region inferior to the circle of Willis is not included. In this paper, we propose a method to automatically generate a map of the whole cerebral vascular territory from CT perfusion imaging. We applied the proposed method to 19 cases of ischemic stroke to generate VTM for each case.Clinical Relevance- The proposed map may improve the interpretation of the physiological status of collateral flow for ischemic stroke, and aid in treatment decision making.


Assuntos
Isquemia Encefálica , Sistema Cardiovascular , Acidente Vascular Cerebral , Encéfalo/diagnóstico por imagem , Isquemia Encefálica/diagnóstico por imagem , Mapeamento Encefálico , Humanos , Acidente Vascular Cerebral/diagnóstico por imagem
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1084-1087, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018174

RESUMO

Recently, more evidences manifest that the subjective cognitive decline (SCD) of unimpaired individual may represent first symptom of Alzheimer's disease (AD). This study investigated the differences of intrinsic glucose metabolic functional connectivity between SCD and healthy subject (HC) groups from the perspective of brain network topology. In this study we attained 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) scans from Xuanwu Hospital, Beijing, China, including 85 SCD subjects (male = 16, mean age = 66, MMSE = 28.4) and 74 HC subjects (male = 37, mean age = 65,MMSE=29.0). Graph theory method has been used in this study. Network parameters, including global efficiency, local efficiency, characteristic path length, clustering coefficient, betweenness centrality, sigma and modularity were calculated and compared between two groups. As a result, both SCD and HC groups showed the small-world property. Meanwhile, SCD showed loss of small-world properties, for example, sigma in SCD was significantly lower than HC (p<0.05). In addition, the clustering coefficient and local efficiency of SCD were both higher than HC significantly (p<0.05). In contrast, the characteristic path length and global efficiency of SCD were lower than HC, which led to the regularization of brain network in SCD group. Furthermore, we found global modularity of SCD was lower than HC and the number of modules also decreased. Our findings suggested that there exist differences in glucose metabolic brain network between two groups, demonstrating that the graph theory analysis method could be useful and helpful to predict risks in the preclinical stage of AD.


Assuntos
Doença de Alzheimer , Disfunção Cognitiva , Encéfalo/diagnóstico por imagem , China , Glucose , Humanos
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1088-1091, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018175

RESUMO

A unified framework for the analysis of fluorescence data taken by a two-photon imaging system is presented. As in the processing of blood-oxygen-level-dependent signals of functional magnetic resonance imaging, the acquired functional images have to be co-registered with a structural brain atlas before delineating the regions activated by a given stimulus. The voxels whose calcium traces are highly correlated with the predicted responses are demarcated without the need for subjective reasoning. Experimental data acquired while presenting olfactory stimuli are used to demonstrate the efficacy of the proposed schemes. The results indicate that the functional images of a Drosophila individual can be normalized into a standard stereotactic space, and the expected brain regions can be delineated adequately. This framework provides an opportunity to enable the development of a Drosophila functional connectome database.


Assuntos
Conectoma , Drosophila , Animais , Encéfalo/diagnóstico por imagem , Imageamento Tridimensional , Imagem por Ressonância Magnética
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1092-1095, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018176

RESUMO

Neuronal-related activity can be estimated from functional magnetic resonance imaging (fMRI) data with no knowledge of the timings of blood oxygenation level-dependent (BOLD) events by means of deconvolution with regularized least-squares. This work proposes two improvements on the deconvolution algorithm of sparse paradigm free mapping (SPFM): a new formulation that enables the estimation of neuronal events with long, sustained activity; and the implementation of a subsampling approach based on stability selection that avoids the choice of any regularization parameter. The proposed method is evaluated on real fMRI data and compared with both the original SPFM algorithm and conventional analysis with a general linear model (GLM) that is aware of the temporal model of the neuronal-related activity. We demonstrate that the novel stability-based SPFM algorithm yields activation maps with higher resemblance to the maps obtained with GLM analyses and offers improved detection of neuronal-related events over SPFM, particularly in scenarios with low contrast-to-noise ratio.


Assuntos
Mapeamento Encefálico , Encéfalo , Algoritmos , Encéfalo/diagnóstico por imagem , Modelos Lineares , Imagem por Ressonância Magnética
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1096-1099, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018177

RESUMO

Simultaneously resting brain glucose metabolism and intrinsic functional activity, by integrated PET/MRI scans, both reflect nerve actions. Studies showed that there existed relevance between two phenotypes of neuros in normal human brains. However, whether the relevance will change in cognitive dysfunction (CD) brains is still unknown. The aim of this study therefore is to explore the relevance between voxel-wise glucose metabolism and functional connectivity in Chinese CD people. The dataset in this study included two imaging modalities and clinical information of 21 healthy control (HC) individuals and 15 CD patients, from Xuanwu hospital, Beijing, China. Firstly, we calculated the standardized uptake value rate (SUVR) from positron emission tomography (PET), and three parameters for intrinsic functional activity from functional magnetic resonance imaging (fMRI), including amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Second, the two sample t-test was used to compare each parameter between HC and CD groups respectively. Third, the relevance between SUVR and the three fMRI parameters were measured by Spearman's rank correlation. The results of t-test showed that glucose metabolism consumption decreased in Default Mode Network (DMN) (p < 0.01), and the damage of functional connection also happened DMN area in CD group. The correlation between glucose metabolism and functional activity in CD group was lower than that in HC group in DMN. Especially, the correlation between SUVR and ReHo was significantly reduced (p < 0.05). Above results promoted a deeper understanding on the pathogenesis of cognitive impairment, and providing new biomarkers to discriminate CD and HC subjects.


Assuntos
Disfunção Cognitiva , Tomografia por Emissão de Pósitrons , Encéfalo/diagnóstico por imagem , China , Disfunção Cognitiva/diagnóstico por imagem , Glucose , Humanos , Imagem por Ressonância Magnética
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1104-1107, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018179

RESUMO

Alzheimer's disease (AD) is progressive neurodegenerative disease. It is important to identify effective biomarkers to explore changes of complex functional brain networks in AD patients based on functional magnetic resonance imaging (fMRI). Recently, four fMRI brain network parameters were frequently used, including regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (f/ALFF) and degree centrality (DC). However, these parameters only present the changes of brain networks in a full time quantum, but ignore changes over a short period of time and lack space information. In this study we propose a new brain network parameter for fMRI, called multilayer network modularity and spatiotemporal network switching rate (stNSR). This parameter is calculated combing Pearson correlation sliding Hamming window and the Louvain algorithm. To verify the efficiency of stNSR, we selected 61 AD patients and 110 healthy controls (HC) from Xuanwu Hospital, Beijing, China. First, we used two-sample t test to identify regions of interest (ROI) between AD patients and HCs. Second, we calculated the stNSR values in these ROIs, and compared them with ReHo, ALFF, f/ALFF, and DC values between AD and HC groups. The results showed that, stNSR values in left calcimine fissure and surrounding cortex, left Lingual gyrus and left cerebellum inferior significantly increased, while stNSR values significantly decreased in left Para hippocampal gyrus, left temporal and superior temporal gyrus. As a comparison, changes in these ROIs could not be observed using ReHo, ALFF, f/ALFF, and DC. The results indicated that stNSR may reflect differences of brain networks between AD patients and HCs.


Assuntos
Doença de Alzheimer , Doenças Neurodegenerativas , Doença de Alzheimer/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , China , Humanos
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1108-1111, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018180

RESUMO

Reconstructing the perceived faces from brain signals has become a promising work recently. However, the reconstruction accuracies rely on a large number of brain signals collected for training a stable reconstruction model, which is really time consuming, and greatly limits its application. In our current study, we develop a new framework that can efficiently perform high-quality face reconstruction with only a small number of brain signals as training samples. The framework consists of three mathematical models: principle component analysis (PCA), linear regression (LR) and conditional generative adversarial network (cGAN). We conducted a functional Magnetic Resonance Imaging (fMRI) experiment in which two subjects' brain signals were collected to test the efficiency of our proposed method. Results show that we can achieve state-of-the-art reconstruction performance from brain signals with a very limited number of fMRI training samples.


Assuntos
Encéfalo , Imagem por Ressonância Magnética , Encéfalo/diagnóstico por imagem , Modelos Lineares , Análise de Componente Principal
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1116-1119, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018182

RESUMO

Recent neuroimaging studies have employed graph theory as a data-driven approach to describe topological organization of the brain under different neurological disorders or task conditions and across life span. In this exploratory study, we tested whether subtle differences in interoception related to intravesical fullness can alter brain topological architecture in healthy participants. 17 right-handed women underwent a series of resting state fMRI scans that included catheterization and partial bladder filling. Using a whole brain regions of interest (ROIs), we computed several graph theory metrics to assess the efficiency of brain-wide information exchange. Results showed that brain network's topological properties significantly changed in many brain regions when we binary compared different interoceptive resting state conditions. Notably, we observed changes in global efficiency in the salience network, the central executive network, anterior dorsal attention network and the posterior default-mode network (DMN) as bladder became full and interoceptive signals intensified. Moreover, degree (the number of connections for each node), and betweenness centrality (how connected a particular region is to other regions) differed between the empty bladder, the catheterized empty bladder, and the catheterized and partially filled bladder. Comparing resting state data before and after an interoceptive task (repeated intravesical infusion and drainage) further showed increased average path length for the salience networks and decreased clustering coefficient of the DMN. These results suggest visceral interoception influences brain topological properties of resting state networks.


Assuntos
Interocepção , Imagem por Ressonância Magnética , Anatomia Regional , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Feminino , Humanos
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1120-1123, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018183

RESUMO

In recent years, the conceptualisation of the brain as a "connectome" as summary measures derived from graph theory analyses, has become increasingly popular. Still, such approaches are inherently limited by the need to condense and simplify temporal fMRI dynamics and architecture into a purely spatial representation. We formulate a novel architecture based on Geometric Deep Learning which is specifically tailored to the one-step integration of spatial relationship between nodes and single-node temporal dynamics. We compare different spatiotemporal modelling mechanisms and demonstrate the effectiveness of our architecture in a binary prediction task based on a large homogeneous fMRI dataset made publicly available by the Human Connectome Project (HCP). As the idea of e.g. a dynamical network connectivity is beginning to make its way into the more mainstream toolset which neuroscientists commonly employ with neuroimaging data, our model can contribute to laying the groundwork for explicitly incorporating spatiotemporal information into every association and prediction problem in neuroscience.


Assuntos
Conectoma , Neurociências , Encéfalo/diagnóstico por imagem , Humanos , Imagem por Ressonância Magnética
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