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1.
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
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 204-207, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017965

RESUMO

For a correct assessment of stereo-electroencephalographic (SEEG) recordings, a proper signal electrical reference is necessary. Such a reference might be physical or virtual. Physical reference can be noisy and a proper virtual reference calculation is often time-consuming. This paper uses the variance of the SEEG signals to calculate the reference from relatively low noise signals to reduce the contamination by distant sources, while maintaining negligible computing time.Ten patients with SEEG recordings were used in this study. 20-second long recordings from each patient, sampled at 5000 Hz, were used to calculate variances of SEEG signals and a low-variance (LV) subset of signals was selected for each patient. Consequently, 4 different reference signals were calculated using: 1) an average signal from WM contacts only (AVG_WM); 2) an average signal from LV contacts only (AVG_LV); 3) independent component analysis (ICA) method from WM contacts only (ICA_WM); and 4) ICA method from LV signals only (ICA_LV). Also, the original testing reference, an average signal from all SEEG contacts (AVG) was utilized. Finally, bipolar signals and average signals from anatomical structures were calculated and used to evaluate reference signals.91.7% of the WM SEEG contacts were found below the average variance. ICA_LV showed the best and AVG_WM the worst overall results. AVG_LV had the most positive impact on minimizing the mutual correlations between separate brain structures and correcting the outliers. The average processing time for ICA methods was 66.72 seconds and 0.7870 seconds for AVG methods (100 000 samples, 125.7±20.4 SEEG signals).Utilizing the LV data subset improves the reference signal. WM references are difficult to obtain and seem to be more susceptible to errors caused by low number of WM contacts in the dataset. ICA_LV can be considered as one of the best reference estimations, however the calculation is very demanding and time consuming. AVG_LV shows good and stable results, while it is based on a straightforward methodology and outstandingly fast calculation.


Assuntos
Encéfalo , Eletroencefalografia , Algoritmos , Mapeamento Encefálico , Humanos
3.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 398-401, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018012

RESUMO

We propose a framework for studying the electrophysiological correlates of BOLD-fMRI. This framework relies on structured coupled matrix-tensor factorization (sCMTF), a joint multidimensional decomposition which reveals dynamical interactions between LFP/EEG oscillatory features and BOLD-fMRI data. We test whether LFP/EEG-BOLD co-fluctuations and regional hemodynamic response functions can be estimated by sCMTF using whole-brain modelling of restingstate activity. We produce permuted datasets to show that our framework extracts EEG/LFP temporal patterns that correlate significantly with BOLD signal fluctuations. Our framework is also capable of estimating HRFs that accurately embodies simulated hemodynamics, with a word of caution regarding initialization of the sCMTF algorithm.


Assuntos
Mapeamento Encefálico , Imagem por Ressonância Magnética , Eletroencefalografia , Fenômenos Eletrofisiológicos , Hemodinâmica
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 841-846, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018116

RESUMO

Investigating the electroencephalography (EEG) correlates of human emotional experiences has attracted increasing interest in the field of affective computing. Substantial progress has been made during the past decades, mainly by using EEG features extracted from localized brain activities. The present study explored a brain network-based feature defined by EEG microstates for a possible representation of emotional experiences. A publicly available and widely used benchmarking EEG dataset called DEAP was used, in which 32 participants watched 40 one-minute music videos with their 32channel EEG recorded. Four quasi-stable prototypical microstates were obtained, and their temporal parameters were extracted as features. In random forest regression, the microstate features showed better performances for decoding valence (model fitting mean squared error (MSE) = 3.85±0.28 and 4.07 ± 0.30, respectively, p = 0.022) and comparable performances for decoding arousal (MSE = 3.30±0.30 and 3.41 ±0.31, respectively, p = 0.169), as compared to conventional spectral power features. As microstate features describe neural activities from a global spatiotemporal dynamical perspective, our findings demonstrate a possible new mechanism for understanding human emotion and provide a promising type of EEG feature for affective computing.


Assuntos
Nível de Alerta , Eletroencefalografia , Encéfalo , Mapeamento Encefálico , Emoções , Humanos
5.
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
6.
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
7.
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
8.
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
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1424-1427, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018257

RESUMO

Major depressive disorder (MDD) is a complex mental disorder characterized by a persistent sad feeling and depressed mood. Recent studies reported differences between healthy control (HC) and MDD by looking to brain networks including default mode and cognitive control networks. More recently there has been interest in studying the brain using advanced machine learning-based classification approaches. However, interpreting the model used in the classification between MDD and HC has not been explored yet. In the current study, we classified MDD from HC by estimating whole-brain connectivity using several classification methods including support vector machine, random forest, XGBoost, and convolutional neural network. In addition, we leveraged the SHapley Additive exPlanations (SHAP) approach as a feature learning method to model the difference between these two groups. We found a consistent result among all classification method in regard of the classification accuracy and feature learning. Also, we highlighted the role of other brain networks particularly visual and sensory motor network in the classification between MDD and HC subjects.


Assuntos
Transtorno Depressivo Maior , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Humanos , Imagem por Ressonância Magnética , Vias Neurais
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1493-1496, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018274

RESUMO

Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional network connectivity (FNC), estimated from resting state functional magnetic resonance imaging (fMRI), to investigate the biological signature of MDD. However, the majority of them have ignored the temporal change of brain interaction by focusing on static FNC (sFNC). Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In the current study, by applying k-means clustering on dFNC of MDD and healthy subjects (HCs), we estimated 5 different states. Next, we use the hidden Markov model as a potential biomarker to differentiate the dFNC pattern of MDD patients from HCs. Comparing MDD and HC subjects' hidden Markov model (HMM) features, we have highlighted the role of transition probabilities between states as potential biomarkers and identified that transition probability from a lightly- connected state to highly connected one reduces as symptom severity increases in MDD subjects.Index Terms- Major depressive disorder, Dynamic functional network connectivity, Machine learning, Resting- state functional magnetic resonance imaging, Hidden Markov model.


Assuntos
Transtorno Depressivo Maior , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Humanos , Imagem por Ressonância Magnética , Probabilidade
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1683-1686, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018320

RESUMO

In application to functional magnetic resonance imaging (fMRI) data analysis, a number of data fusion algorithms have shown success in extracting interpretable brain networks that can distinguish two groups such two populations-patients with mental disorder and the healthy controls. However, there are situations where more than two groups exist such as the fusion of multi-task fMRI data. Therefore, in this work we propose the use of IVA to effectively extract information that is able to distinguish across multiple groups when applied to data fusion. The performance of IVA is investigated using a simulated fMRI-like data. The simulation results illustrate that IVA with multivariate Laplacian distribution and second-order statistics (IVA-L-SOS) yields better performance compared to joint independent component analysis and IVA with multivariate Gaussian distribution in terms of both estimation accuracy and robustness. When applied to real multi-task fMRI data, IVA-L-SOS successfully extract task-related brain networks that are able to distinguish three tasks.


Assuntos
Encéfalo , Imagem por Ressonância Magnética , Algoritmos , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Humanos
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1701-1704, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018324

RESUMO

With an estimated five million new stroke survivors every year and a rapidly aging population suffering from hyperintensities and diseases of presumed vascular origin that affect white matter and contribute to cognitive decline, it is critical that we understand the impact of white matter damage on brain structure and behavior. Current techniques for assessing the impact of lesions consider only location, type, and extent, while ignoring how the affected region was connected to the rest of the brain. Regional brain function is a product of both local structure and its connectivity. Therefore, obtaining a map of white matter disconnection is a crucial step that could help us predict the behavioral deficits that patients exhibit. In the present work, we introduce a new practical method for computing lesion-based white matter disconnection maps that require only moderate computational resources. We achieve this by creating diffusion tractography models of the brains of healthy adults and assessing the connectivity between small regions. We then interrupt these connectivity models by projecting patients' lesions into them to compute predicted white matter disconnection. A quantified disconnection map can be computed for an individual patient in approximately 35 seconds using a single core CPU-based computation. In comparison, a similar quantification performed with other tools provided by MRtrix3 takes 5.47 minutes.


Assuntos
Substância Branca , Adulto , Idoso , Envelhecimento , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Imagem de Tensor de Difusão , Humanos , Substância Branca/diagnóstico por imagem
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1705-1708, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018325

RESUMO

Primary open angle glaucoma (POAG) is one of the most common causes of permanent blindness in the world. Recent studies have originated the hypothesis that POAG could be considered as a central nervous system pathology which results in secondary visual involvement. The aim of this study is to assess possible structural whole brain connectivity alterations in POAG by combining multi-shell diffusion weighted imaging, multi-shell multi-tissue probabilistic tractography, graph theoretical measures and a newly designed disruption index, which evaluates the global reorganization of brain networks in group-wise comparisons. We found global differences in structural connectivity between Glaucoma patients and controls, as well as in local graph theoretical measures. These changes extended well beyond the primary visual pathway. Furthermore, group-wise and subject-wise disruption indices were found to be statistically different between glaucoma patients and controls, with a positive slope. Overall, our results support the hypothesis of a whole-brain structural reorganization in glaucoma which is specific to structural connectivity, possibly placing this disease within the recently defined groups of brain disconnection syndrome.


Assuntos
Encéfalo , Glaucoma de Ângulo Aberto , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Imagem de Difusão por Ressonância Magnética , Substância Cinzenta , Humanos
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1722-1725, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018329

RESUMO

Afferent nerves that carry interoceptive signals from the viscera to the brain include Aδ and C-fibers. Previously, we examined the effects of detrusor distention (conveyed mainly by Aδ fibers) on the static functional network connectivity (FNC) of the brain using independent component analysis (ICA) of fMRI time series. In the present study, we investigate the impact of intravesical cold sensation (thought to be conveyed by C-fibers) on brain FNC using similar ICA approach. Thirteen healthy women were scanned on a 3.0T MRI scanner during a resting state scan and an intravesical cold sensation task fMRI. High dimensional ICA (n = 75) were used to decompose the fMRI data into several intrinsic connectivity networks (ICNs) including the default-mode (DMN), subcortical (SCN; amygdala, thalamus), salience (SN), central executive (CEN), sensorimotor (SMN), and cerebellar/brainstem (CBN) networks. Results demonstrate significant FNC differences in several ICN pairs primarily between the SCN and cognitive networks such as CEN, as well as between SN and CBN and DMN when intravesical cold water condition was compared to rest (FDR-corrected p-value of 0.05). Significant increases in FNC between CBN and between SMN were also observed during interoceptive condition. The results indicate significant impact of Aδ and C-fiber-originated interoceptive signals on the brain connectivity when compared to the baseline rest.


Assuntos
Mapeamento Encefálico , Rede Nervosa , Encéfalo/diagnóstico por imagem , Feminino , Humanos , Imagem por Ressonância Magnética , Sensação
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1730-1733, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018331

RESUMO

Recent reports suggested that even moderate sudden sensorineural hearing loss (SSNHL) can be partly responsible for a loss of gray matter volume in the primary auditory cortex, hence reducing the capacity of the auditory cortical areas to react to sound stimulation. There is also evidence for a plastic reorganization of brain functional networks visible as enhanced local functional connectivity. The aim of this study was to use rs-fMRI, in conjunction with graph- theoretical analysis and a newly developed functional "disruption index" to study whole-brain as well as local functional changes in patients with acute and unilateral sensorineural hearing loss. No statistically significant differences in global or local network measures we found between SSNHL patients and healthy controls. However, when analyzing local metrics through the disruption index k, we found negative values for k which were statistically different from zero both in single subject analysis. Additionally, we found several associations between graph-theoretical metrics and clinical parameters.


Assuntos
Perda Auditiva Neurossensorial , Perda Auditiva Súbita , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Humanos , Imagem por Ressonância Magnética
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1734-1737, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018332

RESUMO

Modular functional alterations were shown in obsessive-compulsive disorder (OCD) patients from previous functional magnetic resonance imaging (fMRI) studies. However, most studies considered each module as a specific node and ignore the intramodular connectivity information. In this paper, we investigated the intramodular functional connectivity (FC) alterations in drug naïve OCD patients using a whole brain graph theoretical approach for functional modular parcellation. Seventy-three drug-naïve OCD patients and seventy-eight matched healthy controls were included in this study. We utilized infomap algorithm for modules detection. The functional connectivity strength (FCS) was calculated within each module to obtain the FC between a given voxel and all other voxels in the module. We found increased FCS in precentral and postcentral gyrus within sensor-motor network (SMN) and decreased FCS in insula within salience network (SN). Moreover, FC within SMN was negatively correlated with YBOCS- compulsions scores, while FC within SN was negatively correlated with YBOCS-total, compulsions and obsessions scores. Our findings brought useful insights in understanding the pathophysiology of OCD.


Assuntos
Transtorno Obsessivo-Compulsivo , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Córtex Cerebral , Humanos , Imagem por Ressonância Magnética , Transtorno Obsessivo-Compulsivo/diagnóstico por imagem
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2392-2395, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018488

RESUMO

Timing prediction plays a key role in optimizing sensory perception and guiding adaptive behaviors. It is critical to study the neural signatures of timing prediction. Comparing to numerous studies focusing on the local brain area, less is known about how the timing prediction influences the functional and effective connectivity of the whole brain network. This study designed a double-tap task, in which the period before the first tap had no timing prediction (NTP), while that of the second tap was influenced by timing prediction (TP). Twelve subjects participated in this study. The functional connectivity was measured by an undirected network constructed by phase-lag index (PLI), while the effective connectivity was measured by a directed network constructed by partial directed coherence (PDC). By comparing the connection strength and modes between NTP and TP, it's found that in alpha-band, timing prediction could improve the global efficiency and transitivity of PLI networks, and shift the in-degree center of PDC networks from frontal area to parieto-occipital area. These results could provide neural evidence for the modeling of timing prediction.


Assuntos
Mapeamento Encefálico , Encéfalo , Lobo Parietal
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2548-2551, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018526

RESUMO

People make decisions multiple times on a daily basis. However, some decisions are easier to make than others and perhaps require more attention to ensure a positive outcome. During gambling, one should attempt to compute the expected rewards and risks associated with decisions. Failing to allocate attention and neural resources to estimate these values can be costly, and in some cases can lead to bankruptcy. Alpha-band (8-12 Hz) oscillatory power in the brain is thought to reflect attention, but how this influences financial decision making is not well understood. Using local field potential recordings in nine human subjects performing a gambling task, we compared alpha-band power from the cingulate cortex (CC) during trials of low and high attention. We found that alpha-band power tended to be higher during a 2 second window after a fixation cue was shown in low attention trials.


Assuntos
Jogo de Azar , Giro do Cíngulo , Encéfalo , Mapeamento Encefálico , Humanos , Recompensa
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2829-2832, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018595

RESUMO

Accurate detection of neuro-psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD) using resting state functional Magnetic Resonance Imaging (rs-fMRI) is challenging due to high dimensionality of input features, low inter-class separability, small sample size and high intra-class variability. For automatic diagnosis of ADHD and autism, spatial transformation methods have gained significance and have achieved improved classification performance. However, they are not reliable due to lack of generalization in dataset like ADHD with high variance and small sample size. Therefore, in this paper, we present a Metaheuristic Spatial Transformation (MST) approach to convert the spatial filter design problem into a constraint optimization problem, and obtain the solution using a hybrid genetic algorithm. Highly separable features obtained from the MST along with meta-cognitive radial basis function based classifier are utilized to accurately classify ADHD. The performance was evaluated using the ADHD200 consortium dataset using a ten fold cross validation. The results indicate that the MST based classifier produces state of the art classification accuracy of 72.10% (1.71% improvement over previous transformation based methods). Moreover, using MST based classifier the training and testing specificity increased significantly over previous methods in literature. These results clearly indicate that MST enables the determination of the highly discriminant transformation in dataset with high variability, small sample size and large number of features. Further, the performance on the ADHD200 dataset shows that MST based classifier can be reliably used for the accurate diagnosis of ADHD using rs-fMRI.Clinical relevance- Metaheuristic Spatial Transformation (MST) enables reliable and accurate detection of neuropsychological disorders like ADHD from rs-fMRI data characterized by high variability, small sample size and large number of features.


Assuntos
Transtorno do Deficit de Atenção com Hiperatividade , Transtorno Autístico , Transtorno do Deficit de Atenção com Hiperatividade/diagnóstico , Encéfalo/diagnóstico por imagem , Mapeamento Encefálico , Humanos , Imagem por Ressonância Magnética
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2901-2904, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018613

RESUMO

This paper reported data-driven functional connectivity (FC) analytical method to investigate functional near infrared spectroscopy (fNIRS)-based connectivity. We evaluated the synchronization of oxygenated hemoglobin using Pearson's correlation and employed orthogonal minimal spanning trees (OMSTs) in characterizing brain connectivity. Then we compared the resultant global cost efficiency and robustness with those generated by non-human i.e. lattice and random networks. We also further benchmarked our method using proportional threshold. Results from 59 healthy subjects demonstrated global cost efficiency and assortativity varied in lattice and random network significantly (p < 0.05), highlighting the potential of OMSTs in extracting true neuronal network. Moreover, the inadequate of proportional threshold in extracting small world network from the same dataset supported that the OMSTs might be the better alternative in FC analysis especially in evaluating cost-efficiency and robustness of network.


Assuntos
Mapeamento Encefálico , Espectroscopia de Luz Próxima ao Infravermelho , Encéfalo , Análise Custo-Benefício
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