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1.
Transl Psychiatry ; 11(1): 102, 2021 Feb 04.
Artigo em Inglês | MEDLINE | ID: mdl-33542206

RESUMO

Major depressive disorder (MDD) is a prevailing chronic mental disorder with lifetime recurring episodes. Recurrent depression (RD) has been reported to be associated with greater severity of depression, higher relapse rate and prominent functioning impairments than first-episode depression (FED), suggesting the progressive nature of depression. However, there is still little evidence regarding brain functional connectome. In this study, 95 medication-free MDD patients (35 with FED and 60 with RD) and 111 matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (fMRI) scanning. After six months of treatment with paroxetine, 56 patients achieved clinical remission and finished their second scan. Network-based statistics analysis was used to explore the changes in functional connectivity. The results revealed that, compared with HCs, patients with FED exhibited hypoconnectivity in the somatomotor, default mode and dorsal attention networks, and RD exhibited hyperconnectivity in the somatomotor, salience, executive control, default mode and dorsal attention networks, as well as within and between salience and executive control networks. Moreover, the disrupted components in patients with current MDD did not change significantly when the patients achieved remission after treatment, and sub-hyperconnectivity and sub-hypoconnectivity were still found in those with remitted RD. Additionally, the hypoconnectivity in FED and hyperconnectivity in RD were associated with the number of episodes and total illness duration. This study provides initial evidence supporting that impairment of intrinsic functional connectivity across the course of depression is a progressive process.

2.
Med Image Anal ; 67: 101836, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33129141

RESUMO

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.


Assuntos
/diagnóstico por imagem , Redes Neurais de Computação , Pneumonia Viral/diagnóstico por imagem , Tomografia Computadorizada por Raios X , /classificação , Humanos , Pneumonia Viral/classificação , Radiografia Torácica , Sensibilidade e Especificidade
3.
IEEE Trans Cybern ; PP2020 Dec 07.
Artigo em Inglês | MEDLINE | ID: mdl-33284758

RESUMO

Current brain cognitive models are insufficient in handling outliers and dynamics of electroencephalogram (EEG) signals. This article presents a novel self-paced dynamic infinite mixture model to infer the dynamics of EEG fatigue signals. The instantaneous spectrum features provided by ensemble wavelet transform and Hilbert transform are extracted to form four fatigue indicators. The covariance of log likelihood of the complete data is proposed to accurately identify similar components and dynamics of the developed mixture model. Compared with its seven peers, the proposed model shows better performance in automatically identifying a pilot's brain workload.

4.
Hum Brain Mapp ; 2020 Dec 07.
Artigo em Inglês | MEDLINE | ID: mdl-33283954

RESUMO

Until now, dynamic functional connectivity (dFC) based on functional magnetic resonance imaging is typically estimated on a set of predefined regions of interest (ROIs) derived from an anatomical or static functional atlas which follows an implicit assumption of functional homogeneity within ROIs underlying temporal fluctuation of functional coupling, potentially leading to biases or underestimation of brain network dynamics. Here, we presented a novel computational method based on dynamic functional connectivity degree (dFCD) to derive meaningful brain parcellations that can capture functional homogeneous regions in temporal variance of functional connectivity. Several spatially distributed but functionally meaningful areas that are well consistent with known intrinsic connectivity networks were identified through independent component analysis (ICA) of time-varying dFCD maps. Furthermore, a systematical comparison with commonly used brain atlases, including the Anatomical Automatic Labeling template, static ICA-driven parcellation and random parcellation, demonstrated that the ROI-definition strategy based on the proposed dFC-driven parcellation could better capture the interindividual variability in dFC and predict observed individual cognitive performance (e.g., fluid intelligence, cognitive flexibility, and sustained attention) based on chronnectome. Together, our findings shed new light on the functional organization of resting brains at the timescale of seconds and emphasized the significance of a dFC-driven and voxel-wise functional homogeneous parcellation for network dynamics analyses in neuroscience.

5.
Hum Brain Mapp ; 2020 Oct 16.
Artigo em Inglês | MEDLINE | ID: mdl-33064332

RESUMO

Antisocial behavior (ASB) is believed to have neural substrates; however, the association between ASB and functional brain networks remains unclear. The temporal variability of the functional connectivity (or dynamic FC) derived from resting-state functional MRI has been suggested as a useful metric for studying abnormal behaviors including ASB. This is the first study using low-frequency fluctuations of the dynamic FC to unravel potential system-level neural correlates with ASB. Specifically, we individually associated the dynamic FC patterns with the ASB scores (measured by Antisocial Process Screening Device) of the male offenders (age: 23.29 ± 3.36 years) based on machine learning. Results showed that the dynamic FCs were associated with individual ASB scores. Moreover, we found that it was mainly the inter-network dynamic FCs that were negatively associated with the ASB severity. Three major high-order cognitive functional networks and the sensorimotor network were found to be more associated with ASB. We further found that impaired behavior in the ASB subjects was mainly associated with decreased FC dynamics in these networks, which may explain why ASB subjects usually have impaired executive control and emotional processing functions. Our study shows that temporal variation of the FC could be a promising tool for ASB assessment, treatment, and prevention.

6.
Front Neurosci ; 14: 881, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33013292

RESUMO

Increasing evidence has suggested that the dynamic properties of functional brain networks are related to individual behaviors and cognition traits. However, current fMRI-based approaches mostly focus on statistical characteristics of the windowed correlation time course, potentially overlooking subtle time-varying patterns in dynamic functional connectivity (dFC). Here, we proposed the use of an end-to-end deep learning model that combines the convolutional neural network (CNN) and long short-term memory (LSTM) network to capture temporal and spatial features of functional connectivity sequences simultaneously. The results on a large cohort (Human Connectome Project, n = 1,050) demonstrated that our model could achieve a high classification accuracy of about 93% in a gender classification task and prediction accuracies of 0.31 and 0.49 (Pearson's correlation coefficient) in fluid and crystallized intelligence prediction tasks, significantly outperforming previously reported models. Furthermore, we demonstrated that our model could effectively learn spatiotemporal dynamics underlying dFC with high statistical significance based on the null hypothesis estimated using surrogate data. Overall, this study suggests the advantages of a deep learning model in making full use of dynamic information in resting-state functional connectivity, and highlights the potential of time-varying connectivity patterns in improving the prediction of individualized characterization of demographics and cognition traits.

7.
Front Psychiatry ; 11: 431, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32477196

RESUMO

Background: Even with continuous antidepressant treatment, residual symptoms and the risk of relapse can persist in remitted major depressive disorder (MDD) patients. Hence, having a clear recognition of the persistent abnormalities of the underlying neural substrate in MDD through a longitudinal investigation is of great importance. Methods: A total of 127 adult medication-free MDD patients with an acute depressive episode and 118 matched healthy controls (HCs) underwent diffusion tensor imaging. Over a 6-month treatment course, 62 remitted patients underwent a second scan. Remission was defined as a 24-item Hamilton Depression Rating Scale (HAMD24) score ≤7 for at least two weeks. Diffusion tensor imaging was performed with a 3.0 T scanner. Differences in whole-brain fractional anisotropy (FA) between MDD patients and HCs were assessed by an independent t-test using gender, age, and education as covariates. Results: Significant FA reductions in the left insula, left middle occipital gyrus, right thalamus, left pallidum and left precuneus were observed in current MDD (cMDD) patients compared with HCs. Moreover, significant FA reductions in the left insula were observed in remitted (rMDD) patients compared to HCs. However, no significant differences in FA values were found when comparing cMDD and rMDD patients. Conclusions: The abnormalities in the insula showed state-independent characteristics, while the abnormalities in the middle occipital gyrus, thalamus, pallidum and precuneus seemed to be state-dependent impairments in MDD patients.

8.
Brain Topogr ; 33(4): 545-557, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32419099

RESUMO

This project aims to explore if stronger functional connectivity (FC) exists in the maximal BOLD response of EEG/fMRI analysis when it is concordant with seizure-onset-zone (SOZ). Twenty-six patients with drug-resistant focal epilepsy who had an EEG/fMRI and later underwent stereo-EEG implantation were included. Different types of IEDs were labeled in scalp EEG and IED-related maximal BOLD responses were evaluated separately, each constituting one study. After evaluating concordance between maximal BOLD and SOZ, twenty-seven studies were placed in the concordant group and eight in the discordant group. We evaluated the local connectivity and ipsilaterally distant connectivity difference between the maximal BOLD and the contralateral homotopic region. Significantly stronger local FC was found for the maximal BOLD in the concordant group (p < 0.05, Bonferroni corrected). 52% of the studies in the concordant group and 13% in the discordant group had a significant difference compared to healthy subjects (p < 0.05, uncorrected). The finding suggests that, when concordant with the SOZ, the maximal BOLD is more likely to have stronger local FC compared to its contralateral counterpart. This asymmetry in functional connectivity may help to noninvasively improve the specificity of EEG/fMRI analysis.

9.
Cogn Neurodyn ; 14(2): 253-265, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32226566

RESUMO

Motor imagery (MI) is a mental representation of motor behavior and has been widely used in electroencephalogram based brain-computer interfaces (BCIs). Several studies have demonstrated the efficacy of MI-based BCI-feedback training in post-stroke rehabilitation. However, in the earliest stage of the training, calibration data typically contain insufficient discriminability, resulting in unreliable feedback, which may decrease subjects' motivation and even hinder their training. To improve the performance in the early stages of MI training, a novel hybrid BCI paradigm based on MI and P300 is proposed in this study. In this paradigm, subjects are instructed to imagine writing the Chinese character following the flash order of the desired Chinese character displayed on the screen. The event-related desynchronization/synchronization (ERD/ERS) phenomenon is produced with writing based on one's imagination. Simultaneously, the P300 potential is evoked by the flash of each stroke. Moreover, a fusion method of P300 and MI classification is proposed, in which unreliable P300 classifications are corrected by reliable MI classifications. Twelve healthy naïve MI subjects participated in this study. Results demonstrated that the proposed hybrid BCI paradigm yielded significantly better performance than the single-modality BCI paradigm. The recognition accuracy of the fusion method is significantly higher than that of P300 (p < 0.05) and MI (p < 0.01). Moreover, the training data size can be reduced through fusion of these two modalities.

10.
Comput Biol Med ; 118: 103618, 2020 03.
Artigo em Inglês | MEDLINE | ID: mdl-32174331

RESUMO

This paper presents a self-paced brain-computer interface (BCI) based on the incorporation of an intelligent environment-understanding approach into a motor imagery (MI) BCI system for rehabilitation hospital environmental control. The interface integrates four types of daily assistance tasks: medical calls, service calls, appliance control and catering services. The system introduces intelligent environment understanding technology to establish preliminary predictions concerning a user's control intention by extracting potential operational objects in the current environment through an object detection neural network. According to the characteristics of the four types of control and services, we establish different response mechanisms and use an intelligent decision-making method to design and dynamically optimize the relevant control instruction set. The control feedback is communicated to the user via voice prompts; it avoids the use of visual channels throughout the interaction. The asynchronous and synchronous modes of the MI-BCI are designed to launch the control process and to select specific operations, respectively. In particular, the reliability of the MI-BCI is enhanced by the optimized identification algorithm. An online experiment demonstrated that the system can respond quickly and it generates an activation command in an average of 3.38s while effectively preventing false activations; the average accuracy of the BCI synchronization commands was 89.2%, which represents sufficiently effective control. The proposed system is efficient, applicable and can be used to both improve system information throughput and to reduce mental loads. The proposed system can be used to assist with the daily lives of patients with severe motor impairments.

11.
IEEE Trans Cybern ; 2020 Mar 20.
Artigo em Inglês | MEDLINE | ID: mdl-32203044

RESUMO

Pilots' brain fatigue status recognition faces two important issues. They are how to extract brain cognitive features and how to identify these fatigue characteristics. In this article, a gamma deep belief network is proposed to extract multilayer deep representations of high-dimensional cognitive data. The Dirichlet distributed connection weight vector is upsampled layer by layer in each iteration, and then the hidden units of the gamma distribution are downsampled. An effective upper and lower Gibbs sampler is formed to realize the automatic reasoning of the network structure. In order to extract the 3-D instantaneous time-frequency distribution spectrum of electroencephalogram (EEG) signals and avoid signal modal aliasing, this article also proposes a smoothed pseudo affine Wigner-Ville distribution method. Finally, experimental results show that our model achieves satisfactory results in terms of both recognition accuracy and stability.

12.
Neuroimage ; 211: 116595, 2020 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-32027965

RESUMO

This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors - derived from dynamic causal modelling (DCM) of EEG data - for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters - as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation.

13.
Cogn Neurodyn ; 14(1): 21-33, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-32015765

RESUMO

Many studies reported that ERP-based BCIs can provide communication for some people with amyotrophic lateral sclerosis (ALS). ERP-based BCIs often present characters within a matrix that occupies the center of the visual field. However, several studies have identified some concerns with the matrix-based approach. This approach may lead to fatigue and errors resulting from flashing adjacent stimuli, and is impractical for users who might want to use the BCI in tandem with other software or feedback in the center of the monitor. In this paper, we introduce and validate an alternate ERP-based BCI display approach. By presenting stimuli near the periphery of the display, we reduce the adjacency problem and leave the center of the display available for feedback or other applications. Two ERP-based display approaches were tested on 18 ALS patients to: (1) compare performance between a conventional matrix speller paradigm (Matrix-P, mean visual angle 6°) and a new speller paradigm with peripherally distributed stimuli (Peripheral-P, mean visual angle 8.8°); and (2) assess performance while spelling 42 characters online continuously, without a break. In the Peripheral-P condition, 12 subjects attained higher than 80% feedback accuracy during online performance, and 7 of these subjects obtained higher than 90% accuracy. The experimental results showed that the Peripheral-P condition yielded performance comparable to the conventional Matrix-P condition (p > 0.05) in accuracy and information transfer rate. This paper introduces a new display approach that leaves the center of the monitor open for feedback and/or other display elements, such as movies, games, art, or displays from other AAC software or conventional software tools.

14.
Neuroreport ; 31(5): 394-398, 2020 03 25.
Artigo em Inglês | MEDLINE | ID: mdl-32101953

RESUMO

The neurophysiological basis of spontaneous low-frequency brain activity has become a major theme in the study of neural function in both humans and animal models. In such studies, the anesthesia model was generally adopted. However, the effects of anesthesia on spontaneous activity remain unclear. In this work, we explored the characteristics of cerebral spontaneous low-frequency activities at different depths of anesthesia in mice. Using Fourier transformation and the multitaper analysis methods, spontaneous low-frequency oscillations (LFOs) in the intrinsic signals of different cerebral regions (artery, vein, and cortex) were extracted and analyzed. Under different concentrations of anesthetic, the frequency of spontaneous LFO signals remained stable, while LFO amplitudes increased with the depth of anesthesia. The results imply that the anesthetic impacts the amplitude of spontaneous LFOs but does not alter the oscillation frequency.

15.
Cereb Cortex ; 30(1): 269-282, 2020 01 10.
Artigo em Inglês | MEDLINE | ID: mdl-31044223

RESUMO

The human precuneus is involved in many high-level cognitive functions, which strongly suggests the existence of biologically meaningful subdivisions. However, the functional parcellation of the precuneus needs much to be investigated. In this study, we developed an eigen clustering (EIC) approach for the parcellation using precuneus-cortical functional connectivity from fMRI data of the Human Connectome Project. The EIC approach is robust to noise and can automatically determine the cluster number. It is consistently demonstrated that the human precuneus can be subdivided into six symmetrical and connected parcels. The anterior and posterior precuneus participate in sensorimotor and visual functions, respectively. The central precuneus with four subregions indicates a media role in the interaction of the default mode, dorsal attention, and frontoparietal control networks. The EIC-based functional parcellation is free of the spatial distance constraint and is more functionally coherent than parcellation using typical clustering algorithms. The precuneus subregions had high accordance with cortical morphology and revealed good functional segregation and integration characteristics in functional task-evoked activations. This study may shed new light on the human precuneus function at a delicate level and offer an alternative scheme for human brain parcellation.

16.
IEEE Trans Cybern ; 2019 Dec 03.
Artigo em Inglês | MEDLINE | ID: mdl-31804950

RESUMO

In real-world applications, not all instances in the multiview data are fully represented. To deal with incomplete data, incomplete multiview learning (IML) rises. In this article, we propose the joint embedding learning and low-rank approximation (JELLA) framework for IML. The JELLA framework approximates the incomplete data by a set of low-rank matrices and learns a full and common embedding by linear transformation. Several existing IML methods can be unified as special cases of the framework. More interestingly, some linear transformation-based complete multiview methods can be adapted to IML directly with the guidance of the framework. Thus, the JELLA framework improves the efficiency of processing incomplete multiview data, and bridges the gap between complete multiview learning and IML. Moreover, the JELLA framework can provide guidance for developing new algorithms. For illustration, within the framework, we propose the IML with the block-diagonal representation (IML-BDR) method. Assuming that the sampled examples have an approximate linear subspace structure, IML-BDR uses the block-diagonal structure prior to learning the full embedding, which would lead to more correct clustering. A convergent alternating iterative algorithm with the successive over-relaxation optimization technique is devised for optimization. The experimental results on various datasets demonstrate the effectiveness of IML-BDR.

17.
Neuroreport ; 30(18): 1294-1298, 2019 12 18.
Artigo em Inglês | MEDLINE | ID: mdl-31688422

RESUMO

As a basic organizing principle of the human brain, hemispheric specialization is an important perspective to explore the pathology of schizophrenia. However, it remains unclearly whether the hemispheric specialization of functional connectivity plays a role in mediating auditory verbal hallucinations in schizophrenia. In this study, 18 schizophrenic patients with auditory verbal hallucinations, 18 patients without auditory verbal hallucinations, and 18 matched healthy controls underwent resting-state functional MRI scans, and seed-based voxel-wise functional connectivity was calculated to quantify the degree of hemispheric specialization. The results revealed that both the auditory verbal hallucinations and non-auditory verbal hallucinations groups exhibited significantly increased specialization in the left middle temporal gyrus and left precuneus, and significantly reduced specialization in the right precuneus relative to healthy controls, and that the auditory verbal hallucinations severity was significantly correlated with the hemispheric specialization of the right precuneus in the auditory verbal hallucinations group. Moreover, the left frontal lobe exhibited reduced hemispheric specialization in the auditory verbal hallucinations group compared with non-auditory verbal hallucinations group, and the patients with and without auditory verbal hallucinations could be clustered into two groups with an accuracy of 80.6% based on the brain regions exhibiting significant specialization changes. The findings indicate that the hemispheric specialization of the aforementioned regions may play a role in mediating auditory verbal hallucinations in schizophrenia, and the distinct hemispheric specialization patterns of functional connectivity may provide a potential biomarker to differentiate schizophrenic patients with and without auditory verbal hallucinations.


Assuntos
Dominância Cerebral/fisiologia , Alucinações/fisiopatologia , Rede Nervosa/fisiopatologia , Esquizofrenia/fisiopatologia , Adolescente , Adulto , Feminino , Neuroimagem Funcional , Alucinações/diagnóstico por imagem , Humanos , Imagem por Ressonância Magnética , Masculino , Rede Nervosa/diagnóstico por imagem , Esquizofrenia/diagnóstico por imagem , Adulto Jovem
18.
Neuroimage Clin ; 24: 102038, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31734531

RESUMO

OBJECTIVE: To explore the relationship between functional connectivity and presence of interictal epileptic discharges (IEDs) in different brain regions in intracranial EEG (iEEG). METHODS: We studied 38 focal epilepsy patients who underwent simultaneous EEG/fMRI scanning and subsequent intracerebral stereo-EEG investigation. In EEG/fMRI analysis, IEDs with different spatial distributions were considered independent studies and IED-related maximal BOLD responses were evaluated. Studies with iEEG electrodes inside the maximal responses were selected and divided into three groups: Studies with 1. distinct maximal BOLD highly concordant with seizure-onset-zone (SOZ); 2. Moderate maximal BOLD concordant with SOZ; 3. maximal BOLD discordant with SOZ. Using maximal BOLD as seed, its functionally connected zone (FCZ) was determined. IED rates in iEEG channels inside and outside the FCZ were compared in the three groups. The effect of laterality and distance between channels and maximal BOLD, and correlation between functional connectivity values and IED rates were analyzed. RESULTS: Thirty-six studies in 25 patients were included. IED rates of intracranial EEG channels inside the FCZ were significantly higher than outside in Group 1 (p = 2.6×10-6) and Group 2 (p = 1.2×10-3) and the inside-outside difference remained after regressing distance and laterality factors. In Group 1, connectivity values were significantly correlated with IED rates in channels inside the FCZ (p < 0.05). SIGNIFICANCE: Our results indicate a higher probability of finding intracranial IEDs in the FCZ of SOZ-concordant maximal BOLD responses than in other regions, regardless of distance and laterality. In studies with distinct maximal BOLD, connectivity values can partially predict IED rates in intracranial EEG. It is thus feasible to non-invasively delineate brain regions that are likely to have high IED rates.


Assuntos
Epilepsia/diagnóstico por imagem , Vias Neurais/diagnóstico por imagem , Adulto , Mapeamento Encefálico , Eletrocorticografia , Epilepsia/fisiopatologia , Feminino , Lateralidade Funcional , Humanos , Imagem por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Vias Neurais/fisiopatologia , Convulsões/diagnóstico por imagem , Convulsões/fisiopatologia
19.
Front Neurosci ; 13: 731, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31379485

RESUMO

Background and Purpose: Previous neuroimaging studies have demonstrated type 2 diabetes (T2D)-related brain structural and functional changes are partly associated with cognitive decline. However, less is known about the underlying mechanisms. Chronic hyperglycemia and microvascular complications are the two of most important risk factors related to cognitive decline in diabetes. Cerebral small vessel diseases (CSVDs), such as those defined by lacunar infarcts, white matter hyperintensities (WMHs) and microhemorrhages, are also associated with an increased risk of cognitive decline and dementia. In this study, we examined brain magnetic resonance imaging (MRI) changes in patients in the early stages of T2D without CSVDs to focus on glucose metabolism factors and to avoid the interference of vascular risk factors on T2D-related brain damage. Methods: T2D patients with disease durations of less than 5 years and without any signs of CSVDs (n = 34) were compared with healthy control subjects (n = 24). Whole-brain region-based functional connectivity was analyzed with network-based statistics (NBS), and brain surface morphology was examined. In addition, the Montreal Cognitive Assessment (MoCA) was conducted for all subjects. Results: At the whole-brain region-based functional connectivity level, thirty-three functional connectivities were changed in T2D patients relative to those in controls, mostly manifested as pathological between-network positive connectivity and located mainly between the sensory-motor network and auditory network. Some of the connectivities were positively correlated with blood glucose level, insulin resistance, and MoCA scores in the T2D group. The network-level analysis showed between-network hyperconnectivity in T2D patients, but no significant changes in within-network connectivity. In addition, there were no significant differences in MoCA scores or brain morphology measures, including cortical thickness, surface area, mean curvature, and gray/white matter volume, between the two groups. Conclusion: The findings indicate that pathological between-network positive connectivity occurs in the early stages of T2D without CSVDs. The abnormal connectivity may indicate that the original balance of mutual antagonistic/cooperative relationships between the networks is broken, which may be a neuroimaging basis for predicting cognitive decline in early T2D patients.

20.
Brain Res ; 1722: 146348, 2019 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-31348912

RESUMO

Intrinsic functional connectivity (FC) exhibits high variability across individuals, which may account for the diversity of cognitive and behavioural ability. This variability in connectivity could be attributed to individual-specific trait and inter-session state differences (intra-subject differences), as well as a small amount of noise. However, it is still a challenge to perform accurate identification of connectivity traits from FC. Here, we introduced a novel low-rank learning model to solve this problem with a new constraint item that could reduce intra-subject differences. The model could dissociate FC into a substrate (substrate) that delineates functional characteristics common across the population and connectivity traits that are expected to account for individual behavioural differences. Subsequently, we performed a sparse dictionary learning algorithm on the extracted connectivity traits and obtained a dictionary matrix, named connectivity dictionary. We could then predict cognitive behaviours, including fluid intelligence, oral reading recognition, grip strength and anger-aggression, more accurately using the connectivity dictionary than the original FC. The results reflect that we captured individual connectivity traits that more effectively represent cognitive behaviour. Moreover, we found that the functional substrate is significantly correlated with large-scale anatomical brain architecture, and individual differences in connectivity traits are constrained by the connectivity substrate. Our findings may advance our understanding of the relationships among anatomy, function, and behaviour.


Assuntos
Encéfalo/fisiologia , Cognição/fisiologia , Conectoma/métodos , Processamento de Imagem Assistida por Computador/métodos , Imagem por Ressonância Magnética/métodos , Adulto , Algoritmos , Feminino , Humanos , Individualidade , Aprendizado de Máquina , Masculino , Vias Neurais/fisiologia , Adulto Jovem
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