scholarly journals Psychiatric Neural Networks and Precision Therapeutics by Machine Learning

Biomedicines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 403
Author(s):  
Hidetoshi Komatsu ◽  
Emi Watanabe ◽  
Mamoru Fukuchi
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Psychiatric Disorders ◽  
Social Dynamics ◽  
Real Life ◽  
Brain Regions ◽  
Mental Illnesses ◽  
Multidimensional Data ◽  
Learning Approaches ◽  
Network Systems

Learning and environmental adaptation increase the likelihood of survival and improve the quality of life. However, it is often difficult to judge optimal behaviors in real life due to highly complex social dynamics and environment. Consequentially, many different brain regions and neuronal circuits are involved in decision-making. Many neurobiological studies on decision-making show that behaviors are chosen through coordination among multiple neural network systems, each implementing a distinct set of computational algorithms. Although these processes are commonly abnormal in neurological and psychiatric disorders, the underlying causes remain incompletely elucidated. Machine learning approaches with multidimensional data sets have the potential to not only pathologically redefine mental illnesses but also better improve therapeutic outcomes than DSM/ICD diagnoses. Furthermore, measurable endophenotypes could allow for early disease detection, prognosis, and optimal treatment regime for individuals. In this review, decision-making in real life and psychiatric disorders and the applications of machine learning in brain imaging studies on psychiatric disorders are summarized, and considerations for the future clinical translation are outlined. This review also aims to introduce clinicians, scientists, and engineers to the opportunities and challenges in bringing artificial intelligence into psychiatric practice.

Computerised Decision Support for Women’s Health Informatics

2012 ◽  
pp. 1404-1416 ◽  
Author(s):  
David Parry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Analysis ◽  
Health Informatics ◽  
Learning Approaches ◽  
Learning From Data ◽  
Computerized Records

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

Computerised Decision Support for Women's Health Informatics

2009 ◽  
pp. 302-314
Author(s):  
David Parry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Analysis ◽  
Health Informatics ◽  
Learning Approaches ◽  
Learning From Data ◽  
Computerized Records

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

scholarly journals Profiling immune cells in the kidney using tissue cytometry and machine learning

Kidney360 ◽  
2021 ◽  
pp. 10.34067/KID.0006802020
Author(s):  
Seth Winfree ◽  
Mohamad Al Hasan ◽  
Tarek M. El-Achkar
Keyword(s):  
Machine Learning ◽  
Immune System ◽  
Immune Cells ◽  
Quantitative Imaging ◽  
Kidney Tissue ◽  
Confocal Imaging ◽  
Qualitative Assessment ◽  
Multidimensional Data ◽  
Learning Approaches ◽  
Imaging Data

The immune system governs key functions that maintain renal homeostasis through various effector cells that reside in or infiltrate the kidney. These immune cells play an important role in shaping adaptive or maladaptive responses to local or systemic stress and injury. We increasingly recognize that microenvironments within the kidney are characterized by unique distribution of immune cells, the function of which depends on this unique spatial localization. Therefore, quantitative profiling of immune cells in intact kidney tissue becomes essential, particularly at a scale and resolution that allow the detection of differences between the various "nephro-ecosystems" in health and disease. In this review, we discuss advancements in tissue cytometry of the kidney, performed through multiplexed confocal imaging and analysis using the Volumetric tissue exploration and analysis (VTEA) software. We highlight how this tool has improved our understanding of the role of the immune system in the kidney and its relevance in pathobiology of renal disease. We also discuss how the field is increasingly incorporating machine learning to enhance the analytical potential of the imaging data and provide unbiased methods to explore and visualize multidimensional data. Such novel analytical methods could be particularly relevant when applied to profiling immune cells. Furthermore, machine learning approaches applied to cytometry could present venues for non-exhaustive exploration and classifications of cells from existing data and improving tissue economy. Therefore, tissue cytometry is transforming what used to be a qualitative assessment of the kidney into a highly quantitative imaging-based "omics" assessment that compliments other advanced molecular interrogation technologies.

scholarly journals Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2081
Author(s):  
Urko Aguirre-Larracoechea ◽  
Cruz E. Borges
Keyword(s):  
Machine Learning ◽  
Real Life ◽  
Absolute Error ◽  
Outcome Data ◽  
Imputation Method ◽  
Learning Approaches ◽  
Life Data ◽  
Machine Learning Methods ◽  
Real Life Data ◽  
Heavy Tailed

Real-life data are bounded and heavy-tailed variables. Zero-one-inflated beta (ZOIB) regression is used for modelling them. There are no appropriate methods to address the problem of missing data in repeated bounded outcomes. We developed an imputation method using ZOIB (i-ZOIB) and compared its performance with those of the naïve and machine-learning methods, using different distribution shapes and settings designed in the simulation study. The performance was measured employing the absolute error (MAE), root-mean-square-error (RMSE) and the unscaled mean bounded relative absolute error (UMBRAE) methods. The results varied depending on the missingness rate and mechanism. The i-ZOIB and the machine-learning ANN, SVR and RF methods showed the best performance.

scholarly journals Distinctive Oculomotor Behaviors in Alzheimer's Disease and Frontotemporal Dementia

2021 ◽  
Vol 12 ◽  
Author(s):  
Carmen Lage ◽  
Sara López-García ◽  
Alexandre Bejanin ◽  
Martha Kazimierczak ◽  
Ignacio Aracil-Bolaños ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eye Tracking ◽  
Frontotemporal Dementia ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Machine Learning Classification

Oculomotor behavior can provide insight into the integrity of widespread cortical networks, which may contribute to the differential diagnosis between Alzheimer's disease and frontotemporal dementia. Three groups of patients with Alzheimer's disease, behavioral variant of frontotemporal dementia (bvFTD) and semantic variant of primary progressive aphasia (svPPA) and a sample of cognitively unimpaired elders underwent an eye-tracking evaluation. All participants in the discovery sample, including controls, had a biomarker-supported diagnosis. Oculomotor correlates of neuropsychology and brain metabolism evaluated with 18F-FDG PET were explored. Machine-learning classification algorithms were trained for the differentiation between Alzheimer's disease, bvFTD and controls. A total of 93 subjects (33 Alzheimer's disease, 24 bvFTD, seven svPPA, and 29 controls) were included in the study. Alzheimer's disease was the most impaired group in all tests and displayed specific abnormalities in some visually-guided saccade parameters, as pursuit error and horizontal prosaccade latency, which are theoretically closely linked to posterior brain regions. BvFTD patients showed deficits especially in the most cognitively demanding tasks, the antisaccade and memory saccade tests, which require a fine control from frontal lobe regions. SvPPA patients performed similarly to controls in most parameters except for a lower number of correct memory saccades. Pursuit error was significantly correlated with cognitive measures of constructional praxis and executive function and metabolism in right posterior middle temporal gyrus. The classification algorithms yielded an area under the curve of 97.5% for the differentiation of Alzheimer's disease vs. controls, 96.7% for bvFTD vs. controls, and 92.5% for Alzheimer's disease vs. bvFTD. In conclusion, patients with Alzheimer's disease, bvFTD and svPPA exhibit differentiating oculomotor patterns which reflect the characteristic neuroanatomical distribution of pathology of each disease, and therefore its assessment can be useful in their diagnostic work-up. Machine learning approaches can facilitate the applicability of eye-tracking in clinical practice.

scholarly journals Grey matter biomarker identification in Schizophrenia: detecting regional alterations and their underlying substrates

2018 ◽  
Author(s):  
V. Chatzi ◽  
R.P. Teixeira ◽  
J. Shawe-Taylor ◽  
A. Altmann ◽  
O. O’Daly ◽  
...  
Keyword(s):  
Machine Learning ◽  
Grey Matter ◽  
Structural Mri ◽  
Brain Regions ◽  
List Type ◽  
Healthy Controls ◽  
Learning Approaches ◽  
Learning Framework ◽  
Software Packages ◽  
Biological Substrates

AbstractState-of-the-art approaches in Schizophrenia research investigate neuroanatomical biomarkers using structural Magnetic Resonance Imaging. However, current models are 1) voxel-wise, 2) difficult to interpret in biologically meaningful ways, and 3) difficult to replicate across studies. Here, we propose a machine learning framework that enables the identification of sparse, region-wise grey matter neuroanatomical biomarkers and their underlying biological substrates by integrating well-established statistical and machine learning approaches. We address the computational issues associated with application of machine learning on structural MRI data in Schizophrenia, as discussed in recent reviews, while promoting transparent science using widely available data and software. In this work, a cohort of patients with Schizophrenia and healthy controls was used. It was found that the cortical thickness in left pars orbitalis seems to be the most reliable measure for distinguishing patients with Schizophrenia from healthy controls.HighlightsWe present a sparse machine learning framework to identify biologically meaningful neuroanatomical biomarkers for SchizophreniaOur framework addresses methodological pitfalls associated with application of machine learning on structural MRI data in Schizophrenia raised by several recent reviewsOur pipeline is easy to replicate using widely available software packagesThe presented framework is geared towards identification of specific changes in brain regions that relate directly to the pathology rather than classification per se

scholarly journals Behavioural analysis of single-cell aneural ciliate, Stentor roeseli, using machine learning approaches

2019 ◽  
Vol 16 (161) ◽  
pp. 20190410
Author(s):  
Mi Kieu Trinh ◽  
Matthew T. Wayland ◽  
Sudhakaran Prabakaran
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Neural Circuits ◽  
Learning Approaches ◽  
Learning Behaviour ◽  
Link Type ◽  
Decision Making Processes ◽  
Artificial Neural ◽  
Avoidance Reactions

There is still a significant gap between our understanding of neural circuits and the behaviours they compute—i.e. the computations performed by these neural networks (Carandini 2012 Nat. Neurosci. 15 , 507–509. ( doi:10.1038/nn.3043 )). Cellular decision-making processes, learning, behaviour and memory formation—all that have been only associated with animals with neural systems—have also been observed in many unicellular aneural organisms, namely Physarum , Paramecium and Stentor (Tang & Marshall2018 Curr. Biol. 28 , R1180–R1184. ( doi:10.1016/j.cub.2018.09.015 )). As these are fully functioning organisms, yet being unicellular, there is a much better chance to elucidate the detailed mechanisms underlying these learning processes in these organisms without the complications of highly interconnected neural circuits. An intriguing learning behaviour observed in Stentor roeseli (Jennings 1902 Am. J. Physiol. Legacy Content 8 , 23–60. ( doi:10.1152/ajplegacy.1902.8.1.23 )) when stimulated with carmine has left scientists puzzled for more than a century. So far, none of the existing learning paradigm can fully encapsulate this particular series of five characteristic avoidance reactions. Although we were able to observe all responses described in the literature and in a previous study (Dexter et al . 2019), they do not conform to any particular learning model. We then investigated whether models inferred from machine learning approaches, including decision tree, random forest and feed-forward artificial neural networks could infer and predict the behaviour of S. roeseli . Our results showed that an artificial neural network with multiple ‘computational’ neurons is inefficient at modelling the single-celled ciliate's avoidance reactions. This has highlighted the complexity of behaviours in aneural organisms. Additionally, this report will also discuss the significance of elucidating molecular details underlying learning and decision-making processes in these unicellular organisms, which could offer valuable insights that are applicable to higher animals.

scholarly journals Real-Life Self-Control is Predicted by Parietal Activity During Preference Decision Making: A Brain Decoding Analysis

2021 ◽  
Author(s):  
Klaus-Martin Krönke ◽  
Holger Mohr ◽  
Max Wolff ◽  
Anja Kräplin ◽  
Michael N. Smolka ◽  
...  
Keyword(s):  
Decision Making ◽  
Community Sample ◽  
Real Life ◽  
Brain Regions ◽  
Self Control ◽  
Angular Gyrus ◽  
Brain Decoding ◽  
Long Term Consequences ◽  
The Individual

AbstractDespite its relevance for health and education, the neurocognitive mechanism of real-life self-control is largely unknown. While recent research revealed a prominent role of the ventromedial prefrontal cortex in the computation of an integrative value signal, the contribution and relevance of other brain regions for real-life self-control remains unclear. To investigate neural correlates of decisions in line with long-term consequences and to assess the potential of brain decoding methods for the individual prediction of real-life self-control, we combined functional magnetic resonance imaging during preference decision making with ecological momentary assessment of daily self-control in a large community sample (N = 266). Decisions in line with long-term consequences were associated with increased activity in bilateral angular gyrus and precuneus, regions involved in different forms of perspective taking, such as imagining one’s own future and the perspective of others. Applying multivariate pattern analysis to the same clusters revealed that individual patterns of activity predicted the probability of real-life self-control. Brain activations are discussed in relation to episodic future thinking and mentalizing as potential mechanisms mediating real-life self-control.

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