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Machine Learning Approaches: From Theory to Application in Schizophrenia

Elisa Veronese, Umberto Castellani, Denis Peruzzo, Marcella Bellani, Paolo Brambilla
2013 Computational and Mathematical Methods in Medicine  
be considered a useful tool in understanding the biological underpinnings of schizophrenia.  ...  We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia.  ...  Methods such as boot-strapping [44] and cross-validation are commonly used for kernel selection.  ... 
doi:10.1155/2013/867924 pmid:24489603 pmcid:PMC3893837 fatcat:mhjhzq6ly5c2poqh6thznr7dyi

Discriminative MR Image Feature Analysis for Automatic Schizophrenia and Alzheimer's Disease Classification [chapter]

Yanxi Liu, Leonid Teverovskiy, Owen Carmichael, Ron Kikinis, Martha Shenton, Cameron S. Carter, V. Andrew Stenger, Simon Davis, Howard Aizenstein, James T. Becker, Oscar L. Lopez, Carolyn C. Meltzer
2004 Lecture Notes in Computer Science  
Discriminative image feature subspaces are computed, evaluated and selected automatically.  ...  We construct a computational framework for automatic central nervous system (CNS) disease discrimination using high resolution Magnetic Resonance Images (MRI) of human brains.  ...  The basic components in our computational framework include: 3D Image Alignment: All MR images for each classification problem in our feasibility study are taken using the same scanner and protocols.  ... 
doi:10.1007/978-3-540-30135-6_48 fatcat:b4qq4lwmencyldgdbuhbv4owuu

Wavelet Analysis of Electroencephalography Signals of Visual Emotion Induction in Schizophrenia Patients

Wen-Lin Chu, Min-Wei Huang, Te-Nan Tsai, Qun-Wei Chang
2020 Sensors and materials  
In this study, we used different visual stimuli to induce emotions on subjects, and electroencephalography (EEG) signals were simultaneously collected for analysis.  ...  The collected EEG signals were subjected to Daubechies wavelet transformation, and the extracted features were input to a support vector machine (SVM) for analysis and identification.  ...  Acknowledgments This work was supported in part by the Ministry of Science and Technology of the Republic of China, Taiwan, under Contract no. MOST 109-2634-F-367-001.  ... 
doi:10.18494/sam.2020.3106 fatcat:ysjgdsfmuvehxm2hhlb5jfxona


Gloria Diaz, Eduardo Romero, Juan Antonio Hernández-Tamames, Vicente Molina, Norberto Malpica
2010 Acta Biológica Colombiana  
The method uses the deformation values from a set of regions, automatically identified as relevant, in a process that selects the statistically significant regions of a t-test under the restriction that  ...  The proposed method was assessed to distinguish healthy controls from schizophrenia patients.  ...  In this work, the Gaussian radial basis function kernel, defined as Equation (2) was used (Platt,1999) . where x and y are two feature vectors, and γ controls the size of the Gaussian kernel.  ... 
doaj:5b986420968741458b82032e1ca95fea fatcat:u7vs7emqarfi3cw67pq5xjtbde

The facial expression of schizophrenic patients applied with infrared thermal facial image sequence

Bo-Lin Jian, Chieh-Li Chen, Wen-Lin Chu, Min-Wei Huang
2017 BMC Psychiatry  
We then chose the features that most effectively distinguish between moderately and markedly ill schizophrenia patients using the SVM.  ...  ITFIs were aligned using affine registration, and the changes induced by small irregular head movements were corrected.  ...  The highest identification rates for 9 features were obtained using quadratic and medium Gaussian kernels (up to 94.3%), followed by linear and cubic kernels (91.4%).  ... 
doi:10.1186/s12888-017-1387-y pmid:28646852 pmcid:PMC5483292 fatcat:cwzf6h2puvailgypcgzclk5n7m

Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

Muhammad Naveed Iqbal Qureshi, Jooyoung Oh, Dongrae Cho, Hang Joon Jo, Boreom Lee
2017 Frontiers in Neuroinformatics  
ACKNOWLEDGMENTS Authors thankfully acknowledge The Center for Biomedical Research Excellence (COBRE) for publicly releasing the dataset.  ...  It maps features in higher dimensional space using linear and nonlinear functions known as kernels. In this study, we used both linear and non-linear (RBF) SVM.  ...  The same number of slices was excluded for all subjects. Slice alignment was applied using the local Pearson's correlation (LPC) cost function.  ... 
doi:10.3389/fninf.2017.00059 pmid:28943848 pmcid:PMC5596100 fatcat:u6f4sgn4p5gfjjyyhzycjsz6ku

Neuroanatomical pattern classification in a population-based sample of first-episode schizophrenia

Marcus V. Zanetti, Maristela S. Schaufelberger, Jimit Doshi, Yangming Ou, Luiz K. Ferreira, Paulo R. Menezes, Marcia Scazufca, Christos Davatzikos, Geraldo F. Busatto
2013 Progress in Neuro-psychopharmacology and Biological Psychiatry  
We aimed to evaluate the diagnostic accuracy (DA) of the above technique to discriminate between incident cases of first-episode schizophrenia identified in a circumscribed geographical region over a limited  ...  In conclusion, using a "real world" sample recruited with epidemiological methods, the application of a neuroanatomical pattern classifier afforded only modest DA to classify first-episode schizophrenia  ...  McGuire in the design of the original study that generated MRI data for the present investigation.  ... 
doi:10.1016/j.pnpbp.2012.12.005 pmid:23261522 fatcat:ydjbxlxuhbdzvowcces6vfyda4

Predicting Individual Psychotic Experiences on a Continuum Using Machine Learning [article]

Jeremy A Taylor, Kit Melissa Larsen, Ilvana Dzafic, Marta I Garrido
2018 bioRxiv   pre-print
and the dysconnection hypothesis in schizophrenia, as well as the notion that psychosis may exist on a continuum.  ...  The aim of this study was to use electroencephalographic (EEG) data and pattern recognition to predict individual psychotic experiences on a continuum between these two extremes in otherwise healthy people  ...  Feature selection rates are indicated using warm colormap and feature weightings are shown in greyscale.  ... 
doi:10.1101/380162 fatcat:hhjqdpdmr5cohbijhiygdhxsym

A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methods

Gemma C. Monté-Rubio, Carles Falcón, Edith Pomarol-Clotet, John Ashburner
2018 NeuroImage  
In this work, a Gaussian Process machine learning approach was used for predicting age, gender and body mass index (BMI) of subjects in the IXI dataset, as well as age, gender and diagnostic status using  ...  MRI data were segmented and aligned using SPM12, and a variety of feature representations were derived from this preprocessing.  ...  J.A. is based at the Wellcome Center for Human Neuroimaging, which is supported by core funding from the Wellcome Trust (091593/Z/10/Z). We are indebted to lab colleagues for insightful discussions.  ... 
doi:10.1016/j.neuroimage.2018.05.065 pmid:29864520 pmcid:PMC6202442 fatcat:lijv54cnkzesvh5odhcpjhckwm

Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis

Raymond Salvador, Joaquim Radua, Erick J. Canales-Rodríguez, Aleix Solanes, Salvador Sarró, José M. Goikolea, Alicia Valiente, Gemma C. Monté, María del Carmen Natividad, Amalia Guerrero-Pedraza, Noemí Moro, Paloma Fernández-Corcuera (+6 others)
2017 PLoS ONE  
To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso,  ...  between those observed in healthy controls PLOS ONE | https://doi.Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis.  ...  In summary, from our exhaustive analysis of algorithms and data features we conclude that while grey matter VBM is the feature of choice for sMRI based classification in psychosis, the selection of classifier  ... 
doi:10.1371/journal.pone.0175683 pmid:28426817 pmcid:PMC5398548 fatcat:kdvh7zcj3bguzjqydwu7ivz2cu

Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia

Uicheul Yoon, Jong-Min Lee, Kiho Im, Yong-Wook Shin, Baek Hwan Cho, In Young Kim, Jun Soo Kwon, Sun I. Kim
2007 NeuroImage  
In particular, 40-70 principal components rearranged by a simple two-sample t-test which ranked the effectiveness of features were used for the best mean accuracy of simulated classification (frontal:  ...  And, discriminative patterns derived at every vertex in the original feature space with respect to support vector machine were analyzed with definitive findings of brain abnormalities in schizophrenia  ...  Table 2 shows the best accuracy of DAV feature selection with the number of features used for each lobe and total variance of those features.  ... 
doi:10.1016/j.neuroimage.2006.11.021 pmid:17188902 fatcat:cljcx2uufbc3hp7wkztvofarya

Classification of schizophrenia patients based on resting-state functional network connectivity

Mohammad R. Arbabshirani, Kent A. Kiehl, Godfrey D. Pearlson, Vince D. Calhoun
2013 Frontiers in Neuroscience  
However, resting-state data has been rarely used in discrimination of schizophrenia patients from healthy controls.  ...  To our knowledge our effects represent the first use of resting-state functional network connectivity (FNC) features to classify schizophrenia.  ...  Note that these results are presented for descriptive purposes but were not used for feature selection or at all in the classification process.  ... 
doi:10.3389/fnins.2013.00133 pmid:23966903 pmcid:PMC3744823 fatcat:4jclfi6vcrbcjk7vojunkupdru

Identify schizophrenia using resting-state functional connectivity: an exploratory research and analysis

Yan Tang, Lifeng Wang, Fang Cao, Liwen Tan
2012 BioMedical Engineering OnLine  
Thus, our result may provide insights into the identification of potentially effective biomarkers for the clinical diagnosis of schizophrenia.  ...  The brain region of great weight may be the problematic region of information exchange in schizophrenia.  ...  The similar methods of the selection for optimal feature number of the final feature space for classification also were used to choose the dimension for PCA and C for SVM.  ... 
doi:10.1186/1475-925x-11-50 pmid:22898249 pmcid:PMC3462724 fatcat:lktf4fct4vgrdp2j5zgytsi6fy

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging

Yuhui Du, Zening Fu, Vince D. Calhoun
2018 Frontiers in Neuroscience  
Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods.  ...  Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation.  ...  used RFE algorithm for the feature selection.  ... 
doi:10.3389/fnins.2018.00525 pmid:30127711 pmcid:PMC6088208 fatcat:b7hdtmrz4vehlhjw4fo2bewhwq

Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review

Renato de Filippis, Elvira Anna Carbone, Raffaele Gaetano, Antonella Bruni, Valentina Pugliese, Cristina Segura-Garcia, Pasquale De Fazio
2019 Neuropsychiatric Disease and Treatment  
The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ.  ...  Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75-90%).  ...  Disclosure The authors report no conflicts of interest in this work. References  ... 
doi:10.2147/ndt.s202418 pmid:31354276 pmcid:PMC6590624 fatcat:wubpr7zpubhp3nf7wy2yqhgsg4
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