The quest for biomarkers in Schizophrenia: from neuroimaging to machine learning

Bajouco, M.; Mota, Dálete Delalibera Corrêa de Faria; Coroa, Manuel; Caldeira, Salomé; Santos, Vítor; Madeira, Nuno

doi:10.21035/ijcnmh.2017.4(suppl.3).s03

Cited by 3 publications

(2 citation statements)

References 96 publications

(137 reference statements)

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“…In line with the technological advances across recent decades, the knowledge on neuronal connectivity has been advanced with different neuroimaging methods including functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), arterial spin labeling (ASL), functional near infrared spectroscopy (fNIRS) and magnetoencephalography (MEG) [27–29]. The term ‘connectivity’ in neuroscience refers to patterns of links between distinct units within a nervous system.…”

Section: Significance Of Neuronal Connectivitymentioning

confidence: 99%

Stochastic and vibrational resonance in complex networks of neurons

Çalım

Palabaş

Uzuntarla

2021

Phil. Trans. R. Soc. A.

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The concept of resonance in nonlinear systems is crucial and traditionally refers to a specific realization of maximum response provoked by a particular external perturbation. Depending on the system and the nature of perturbation, many different resonance types have been identified in various fields of science. A prominent example is in neuroscience where it has been widely accepted that a neural system may exhibit resonances at microscopic, mesoscopic and macroscopic scales and benefit from such resonances in various tasks. In this context, the two well-known forms are stochastic and vibrational resonance phenomena which manifest that detection and propagation of a feeble information signal in neural structures can be enhanced by additional perturbations via these two resonance mechanisms. Given the importance of network architecture in proper functioning of the nervous system, we here present a review of recent studies on stochastic and vibrational resonance phenomena in neuronal media, focusing mainly on their emergence in complex networks of neurons as well as in simple network structures that represent local behaviours of neuron communities. From this perspective, we aim to provide a secure guide by including theoretical and experimental approaches that analyse in detail possible reasons and necessary conditions for the appearance of stochastic resonance and vibrational resonance in neural systems. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 2)’.

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Section: Significance Of Neuronal Connectivitymentioning

confidence: 99%

Stochastic and vibrational resonance in complex networks of neurons

Çalım

Palabaş

Uzuntarla

2021

Phil. Trans. R. Soc. A.

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“…There is also study that compared EEG data in BD during manic and depressive episodes. 16 Furthermore, several studies focused on neuroimaging biomarkers for major depressive disorder (MDD) 17 and schizophrenia (SZ), 18,19 and comparing patients diagnosed with SZ and MDD, 20 using sLORETA. An informative overview of neuroimaging techniques for all neuropsychiatric diseases can be found in Malhi and Lagopoulos 6 and Hughes and John 21 ; however, as mentioned earlier each neuroimaging modality has specific technological and physiological limitations that are leading to more widespread use of MN among neuroscientists.…”

Section: Application Of Neuroimaging: From Unimodal To Multimodalmentioning

confidence: 99%

Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases

et al. 2018

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Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers-especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification-especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.

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