scholarly journals Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Dafa Shi ◽  
Yanfei Li ◽  
Haoran Zhang ◽  
Xiang Yao ◽  
Siyuan Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multimodal Imaging ◽  
Functional Neuroimaging ◽  
Neurological Diseases ◽  
Structural Mri ◽  
Neural Mechanisms ◽  
Global Signal ◽  
Structural Abnormalities ◽  
Brain Data

Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.

scholarly journals Human Empathy Through the Lens of Social Neuroscience

2006 ◽  
Vol 6 ◽  
pp. 1146-1163 ◽  
Author(s):  
Jean Decety ◽  
Claus Lamm
Keyword(s):  
Information Processing ◽  
Cognitive Neuroscience ◽  
Executive Control ◽  
Perspective Taking ◽  
Functional Neuroimaging ◽  
Social Neuroscience ◽  
Neural Mechanisms ◽  
The Self ◽  
Neural Systems

Empathy is the ability to experience and understand what others feel without confusion between oneself and others. Knowing what someone else is feeling plays a fundamental role in interpersonal interactions. In this paper, we articulate evidence from social psychology and cognitive neuroscience, and argue that empathy involves both emotion sharing (bottom-up information processing) and executive control to regulate and modulate this experience (top-down information processing), underpinned by specific and interacting neural systems. Furthermore, awareness of a distinction between the experiences of the self and others constitutes a crucial aspect of empathy. We discuss data from recent behavioral and functional neuroimaging studies with an emphasis on the perception of pain in others, and highlight the role of different neural mechanisms that underpin the experience of empathy, including emotion sharing, perspective taking, and emotion regulation.

scholarly journals The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning

Biomolecules ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1132
Author(s):  
Ángela García-Fonseca ◽  
Cynthia Martin-Jimenez ◽  
George E. Barreto ◽  
Andres Felipe Aristizábal Pachón ◽  
Janneth González
Keyword(s):  
Machine Learning ◽  
Neurodegenerative Diseases ◽  
Neurodegenerative Disorder ◽  
Neurological Diseases ◽  
Expression Profiles ◽  
Relevant Information ◽  
Vast Number ◽  
Non Coding Rnas ◽  
Significant Attention

Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction and death of brain cells population. As the early manifestations of NDs are similar, their symptoms are difficult to distinguish, making the timely detection and discrimination of each neurodegenerative disorder a priority. Several investigations have revealed the importance of microRNAs and long non-coding RNAs in neurodevelopment, brain function, maturation, and neuronal activity, as well as its dysregulation involved in many types of neurological diseases. Therefore, the expression pattern of these molecules in the different NDs have gained significant attention to improve the diagnostic and treatment at earlier stages. In this sense, we gather the different microRNAs and long non-coding RNAs that have been reported as dysregulated in each disorder. Since there are a vast number of non-coding RNAs altered in NDs, some sort of synthesis, filtering and organization method should be applied to extract the most relevant information. Hence, machine learning is considered as an important tool for this purpose since it can classify expression profiles of non-coding RNAs between healthy and sick people. Therefore, we deepen in this branch of computer science, its different methods, and its meaningful application in the diagnosis of NDs from the dysregulated non-coding RNAs. In addition, we demonstrate the relevance of machine learning in NDs from the description of different investigations that showed an accuracy between 85% to 95% in the detection of the disease with this tool. All of these denote that artificial intelligence could be an excellent alternative to help the clinical diagnosis and facilitate the identification diseases in early stages based on non-coding RNAs.

scholarly journals The Emerging Role of SPECT Functional Neuroimaging in Schizophrenia and Depression

2021 ◽  
Vol 12 ◽  
Author(s):  
Anil Kalyoncu ◽  
Ali Saffet Gonul
Keyword(s):  
Psychiatric Disorders ◽  
Functional Neuroimaging ◽  
Single Photon ◽  
Imaging Techniques ◽  
Photon Emission ◽  
Neural Mechanisms ◽  
Psychiatric Research ◽  
Emission Computed Tomography ◽  
Future Implication

Over the last three decades, the brain's functional and structural imaging has become more prevalent in psychiatric research and clinical application. A substantial amount of psychiatric research is based on neuroimaging studies that aim to illuminate neural mechanisms underlying psychiatric disorders. Single-photon emission computed tomography (SPECT) is one of those developing brain imaging techniques among various neuroimaging technologies. Compared to PET, SPECT imaging is easy, less expensive, and practical for radioligand use. Current technologies increased the spatial accuracy of SPECT findings by combining the functional SPECT images with CT images. The radioligands bind to receptors such as 5-hydroxytryptamine 2A, and dopamine transporters can help us comprehend neural mechanisms of psychiatric disorders based on neurochemicals. This mini-review focuses on the SPECT-based neuroimaging approach to psychiatric disorders such as schizophrenia and major depressive disorder (MDD). Research-based SPECT findings of psychiatric disorders indicate that there are notable changes in biochemical components in certain disorders. Even though many studies support that SPECT can be used in psychiatric clinical practice, we still only use subjective diagnostic criteria such as the Diagnostic Statistical Manual of Mental Disorders (DSM-5). Glimpsing into the brain's biochemical world via SPECT in psychiatric disorders provides more information about the pathophysiology and future implication of neuroimaging techniques.

Neurological consultations and diagnoses in a large, dedicated COVID-19 university hospital

2020 ◽  
Vol 78 (8) ◽  
pp. 494-500 ◽  
Author(s):  
Adalberto STUDART-NETO ◽  
Bruno Fukelmann GUEDES ◽  
Raphael de Luca e TUMA ◽  
Antonio Edvan CAMELO FILHO ◽  
Gabriel Taricani KUBOTA ◽  
...  
Keyword(s):  
Neurological Diseases ◽  
Neuromuscular Disorders ◽  
Neurological Symptoms ◽  
University Hospital ◽  
Special Treatment ◽  
Nasal Swabs ◽  
Neurological Exam ◽  
Neurological Conditions ◽  
De Medicina

ABSTRACT Background: More than one-third of COVID-19 patients present neurological symptoms ranging from anosmia to stroke and encephalopathy. Furthermore, pre-existing neurological conditions may require special treatment and may be associated with worse outcomes. Notwithstanding, the role of neurologists in COVID-19 is probably underrecognized. Objective: The aim of this study was to report the reasons for requesting neurological consultations by internists and intensivists in a COVID-19-dedicated hospital. Methods: This retrospective study was carried out at Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Brazil, a 900-bed COVID-19 dedicated center (including 300 intensive care unit beds). COVID-19 diagnosis was confirmed by SARS-CoV-2-RT-PCR in nasal swabs. All inpatient neurology consultations between March 23rd and May 23rd, 2020 were analyzed. Neurologists performed the neurological exam, assessed all available data to diagnose the neurological condition, and requested additional tests deemed necessary. Difficult diagnoses were established in consensus meetings. After diagnosis, neurologists were involved in the treatment. Results: Neurological consultations were requested for 89 out of 1,208 (7.4%) inpatient COVID admissions during that period. Main neurological diagnoses included: encephalopathy (44.4%), stroke (16.7%), previous neurological diseases (9.0%), seizures (9.0%), neuromuscular disorders (5.6%), other acute brain lesions (3.4%), and other mild nonspecific symptoms (11.2%). Conclusions: Most neurological consultations in a COVID-19-dedicated hospital were requested for severe conditions that could have an impact on the outcome. First-line doctors should be able to recognize neurological symptoms; neurologists are important members of the medical team in COVID-19 hospital care.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Role of Nicotinic and Muscarinic Receptors on Synaptic Plasticity and Neurological Diseases

2016 ◽  
Vol 22 (14) ◽  
pp. 2004-2014 ◽  
Author(s):  
Marco Fuenzalida ◽  
Miguel Ángel Pérez ◽  
Hugo R. Arias
Keyword(s):  
Synaptic Plasticity ◽  
Muscarinic Receptors ◽  
Neurological Diseases

Microbiota-Immune System Interactions in Human Neurological Disorders

2020 ◽  
Vol 19 (7) ◽  
pp. 509-526
Author(s):  
Qin Huang ◽  
Fang Yu ◽  
Di Liao ◽  
Jian Xia
Keyword(s):  
Immune System ◽  
Gut Microbiota ◽  
Neurological Disorders ◽  
Brain Function ◽  
Neurological Diseases ◽  
Host Immune System ◽  
Gut Dysbiosis ◽  
Characteristic Features ◽  
Novel Therapeutic Strategies

: Recent studies implicate microbiota-brain communication as an essential factor for physiology and pathophysiology in brain function and neurodevelopment. One of the pivotal mechanisms about gut to brain communication is through the regulation and interaction of gut microbiota on the host immune system. In this review, we will discuss the role of microbiota-immune systeminteractions in human neurological disorders. The characteristic features in the development of neurological diseases include gut dysbiosis, the disturbed intestinal/Blood-Brain Barrier (BBB) permeability, the activated inflammatory response, and the changed microbial metabolites. Neurological disorders contribute to gut dysbiosis and some relevant metabolites in a top-down way. In turn, the activated immune system induced by the change of gut microbiota may deteriorate the development of neurological diseases through the disturbed gut/BBB barrier in a down-top way. Understanding the characterization and identification of microbiome-immune- brain signaling pathways will help us to yield novel therapeutic strategies by targeting the gut microbiome in neurological disease.

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