scholarly journals Neglect and the Kaleidoscopic Mind: Psychology and Mental Health in Contemporary Art

Arts ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 47
Author(s):  
Marcos Lutyens ◽  
Leonardo Christov-Moore
Keyword(s):  
Machine Learning ◽  
Contemporary Art ◽  
Brain Function ◽  
Medical Community ◽  
Haptic Interfaces ◽  
Combined Approach ◽  
Stroke Patients ◽  
The 1960S ◽  
Simple Analog ◽  
Ongoing Project

This paper seeks to explore the broad question of whether and how art can be applied to medical therapeutic practices. As part of this research, the paper outlines an ongoing project, exemplifying this combined approach, which seeks to improve function in stroke patients. We reviewed previous collaborations between art and psychology dating back to the 1960s, employing methods ranging from simple, analog, haptic interfaces to the contemporary potential of machine learning to improve brain function. We then outline an ongoing project employing machine learning and multisensory stimulation to improve function in stroke patients, which are being run in collaboration with Klinik Lippoldsberg, Germany. We discuss the possibility that these same approaches may also be applied to healthy people as an open-ended inquiry into consciousness and mental optimization. It is hoped that these approaches will be beneficial to the medical community, but also equally broaden the reach and context of contemporary art, which is so often marginalized within institutions that are not readily accessible to or in communication with other disciplines.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

From governmentality to solidarity: George Drivas’ Laboratory of Dilemmas

2021 ◽  
Vol 7 (1) ◽  
pp. 49-68 ◽  
Author(s):  
Anthi Argyriou
Keyword(s):  
Contemporary Art ◽  
Literary Text ◽  
Visual Narrative ◽  
Refugee Crisis ◽  
Local Populations ◽  
Current Trends ◽  
Relational Perspective ◽  
Close Analysis ◽  
The 1960S ◽  
Alternative Reading

In the context of current trends in contemporary art on migration, this article undertakes a close analysis of George Drivas’ installation Laboratory of Dilemmas (2017). It delineates the response this work offers to dominant discourses of the so-called ‘refugee crisis’ and explores how contemporary art can intervene critically in these discourses. In Laboratory of Dilemmas, Drivas articulates an audio-visual narrative in which the dilemma of accepting or rejecting the ‘foreign(er)’ is played out in two distinct registers: that of a biology experiment allegedly conducted in the 1960s and that of a millennia-old literary text, Aeschylus’ Suppliant Women. The analysis of the installation will answer the following questions: How do the themes of governmentality, biopolitics and hospitality come into play? In what ways does the artwork undermine the established ‘foreign’/‘native’ dichotomy and how does it foster a space of potentiality between incoming and local populations? Looking at the exemplary biopolitical setting of the artwork and taking stock of existing interpretations, I propose an alternative reading that sees the work overturning the governmentality paradigm in favour of a profoundly inclusive and relational perspective.

Abstract P81: SARS-CoV-2 and Stroke Characteristics a Report From the Multinational COVID-19 Stroke Study Group

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Shima Shahjouei ◽  
Georgios K Tsivgoulis ◽  
Ghasem Farahmand ◽  
Eric Koza ◽  
Ashkan Mowla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Health Expenditure ◽  
Mechanical Thrombectomy ◽  
Vascular Risk Factors ◽  
Stroke Severity ◽  
Vascular Risk ◽  
Stroke Patients ◽  
Unsupervised Machine Learning ◽  
Toast Criteria

Objective and Design: We conducted a multinational observational study on features of consecutive acute ischemic stroke (AIS), intracranial hemorrhage (ICH), and cerebral venous or sinus thrombosis (CVST) among SARS-CoV-2 infected patients. Main Outcome Measures: We investigated the association of demographics, clinical data, geographical regions, and countries’ health expenditure among AIS patients with the risk of large vessel occlusion (LVO), stroke severity as measured by National Institute of Health stroke scale (NIHSS), and stroke subtype as measured by the TOAST criteria. Additionally, we applied unsupervised machine learning algorithms to uncover possible similarities among stroke patients. Results: Among the 136 tertiary centers of 32 countries who participated in this study, 71 centers from 17 countries had at least one eligible stroke patient. Out of 432 patients included, 323(74.8%) had AIS, 91(21.1%) ICH, and 18(4.2%) CVST. Among 23 patients with subarachnoid hemorrhage, 16(69.5%) had no evidence of aneurysm. A total of 183(42.4%) patients were women, 104(24.1%) patients were younger than 55 years, and 105(24.4%) patients had no identifiable vascular risk factors. Among 380 patients who had known interval onset of the SARS-CoV-2 and stroke, 144(37.8%) presented to the hospital with chief complaints of stroke-related symptoms, with asymptomatic or undiagnosed SARS-CoV-2 infection. Among AIS patients 44.5% had LVO; 10% had small artery occlusion according to the TOAST criteria. We observed a lower median NIHSS (8[3-17], versus 11[5-17]; p=0.02) and higher rate of mechanical thrombectomy (12.4% versus 2%; p<0.001) in countries with middle to high-health expenditure when compared to countries with lower health expenditure. The unsupervised machine learning identified 4 subgroups, with a relatively large group with no or limited comorbidities. Conclusions and Relevance: We observed a relatively high number of young, and asymptomatic SARS-CoV-2 infections among stroke patients. Traditional vascular risk factors were absent among a relatively large cohort of patients. The stroke severity was lower and rate of mechanical thrombectomy was higher among countries with middle to high-health expenditure.

scholarly journals Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study (Preprint)

2020 ◽  
Author(s):  
Eunjeong Park ◽  
Kijeong Lee ◽  
Taehwa Han ◽  
Hyo Suk Nam
Keyword(s):  
Machine Learning ◽  
Medical Staff ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Grading System ◽  
Operating Characteristics ◽  
Stroke Patients ◽  
Motor Weakness ◽  
Automatic Grading

BACKGROUND Subtle abnormal motor signs are indications of serious neurological diseases. Although neurological deficits require fast initiation of treatment in a restricted time, it is difficult for nonspecialists to detect and objectively assess the symptoms. In the clinical environment, diagnoses and decisions are based on clinical grading methods, including the National Institutes of Health Stroke Scale (NIHSS) score or the Medical Research Council (MRC) score, which have been used to measure motor weakness. Objective grading in various environments is necessitated for consistent agreement among patients, caregivers, paramedics, and medical staff to facilitate rapid diagnoses and dispatches to appropriate medical centers. OBJECTIVE In this study, we aimed to develop an autonomous grading system for stroke patients. We investigated the feasibility of our new system to assess motor weakness and grade NIHSS and MRC scores of 4 limbs, similar to the clinical examinations performed by medical staff. METHODS We implemented an automatic grading system composed of a measuring unit with wearable sensors and a grading unit with optimized machine learning. Inertial sensors were attached to measure subtle weaknesses caused by paralysis of upper and lower limbs. We collected 60 instances of data with kinematic features of motor disorders from neurological examination and demographic information of stroke patients with NIHSS 0 or 1 and MRC 7, 8, or 9 grades in a stroke unit. Training data with 240 instances were generated using a synthetic minority oversampling technique to complement the imbalanced number of data between classes and low number of training data. We trained 2 representative machine learning algorithms, an ensemble and a support vector machine (SVM), to implement auto-NIHSS and auto-MRC grading. The optimized algorithms performed a 5-fold cross-validation and were searched by Bayes optimization in 30 trials. The trained model was tested with the 60 original hold-out instances for performance evaluation in accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve (AUC). RESULTS The proposed system can grade NIHSS scores with an accuracy of 83.3% and an AUC of 0.912 using an optimized ensemble algorithm, and it can grade with an accuracy of 80.0% and an AUC of 0.860 using an optimized SVM algorithm. The auto-MRC grading achieved an accuracy of 76.7% and a mean AUC of 0.870 in SVM classification and an accuracy of 78.3% and a mean AUC of 0.877 in ensemble classification. CONCLUSIONS The automatic grading system quantifies proximal weakness in real time and assesses symptoms through automatic grading. The pilot outcomes demonstrated the feasibility of remote monitoring of motor weakness caused by stroke. The system can facilitate consistent grading with instant assessment and expedite dispatches to appropriate hospitals and treatment initiation by sharing auto-MRC and auto-NIHSS scores between prehospital and hospital responses as an objective observation.

Advancements of MRI-Based Brain Tumor Segmentation from Traditional to Recent Trends- A Review

2021 ◽  
Vol 17 ◽  
Author(s):  
Padmapriya Thiyagarajan ◽  
Sriramakrishnan Padmanaban ◽  
Kalaiselvi Thiruvenkadam ◽  
Somasundaram Karuppanagounder
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Human Head ◽  
Medical Community ◽  
Tumor Segmentation ◽  
Normal Brain ◽  
Learning Methods ◽  
Brain Tumor Segmentation ◽  
Mri Scans

Background: Among the brain-related diseases, brain tumor segmentation on magnetic resonance imaging (MRI) scans is one of the highly focused research domains in the medical community. Brain tumor segmentation is a very challenging task due to its asymmetric form and uncertain boundaries. This process segregates the tumor region into the active tumor, necrosis and edema from normal brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF). Introduction: The proposed paper analyzed the advancement of brain tumor segmentation from conventional image processing techniques, to deep learning through machine learning on MRI of human head scans. Method: State-of-the-art methods of these three techniques are investigated, and the merits and demerits are discussed. Results: The prime motivation of the paper is to instigate the young researchers towards the development of efficient brain tumor segmentation techniques using conventional and recent technologies. Conclusion: The proposed analysis concluded that the conventional and machine learning methods were mostly applied for brain tumor detection, whereas deep learning methods were good at tumor substructures segmentation.

Abstract TP215: Machine Learning Analysis of Gene Expression Data and Predictive Stroke Outcomes in Acute Ischemic Stroke

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Sarah R Martha ◽  
Qiang Cheng ◽  
Liyu Gong ◽  
Lisa Collier ◽  
Stephanie Davis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Mechanical Thrombectomy ◽  
Stroke Patients ◽  
Stroke Outcomes ◽  
Inflammatory Genes ◽  
Patient Demographics ◽  
Edema Volume

Background and Purpose: The ability to predict ischemic stroke outcomes in the first day of admission could be vital for patient counseling, rehabilitation, and care planning. The Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC; clinicaltrials.gov NCT03153683) collects blood samples distal and proximal to the intracranial thrombus during mechanical thrombectomy. These samples are a novel resource in evaluating acute gene expression changes at the time of ischemic stroke. The purpose of this study was to identify inflammatory genes and patient demographics that are predictive of stroke outcomes (infarct and/or edema volume) in acute ischemic stroke patients. Methods: The BACTRAC study is a non-probability, convenience sampling of subjects (≥ 18 year olds) treated with mechanical thrombectomy for emergent large vessel occlusion. We evaluated relative concentrations of mRNA for gene expression in 84 inflammatory molecules in static blood distal and proximal to the intracranial thrombus from adults who underwent thrombectomy. We employed a machine learning method, Random Forest, utilizing the first set of enrolled subjects, to predict which inflammatory genes and patient demographics were important features for infarct and edema volumes. Results: We analyzed the first 28 subjects (age = 66 ± 15.48, 11 males) in the BACTRAC registry. Results from machine learning analyses demonstrate that the genes CCR4, IFNA2, IL9, CXCL3, Age, DM, IL7, CCL4, BMI, IL5, CCR3, TNF, and IL27 predict infarct volume. The genes IFNA2, IL5, CCL11, IL17C, CCR4, IL9, IL7, CCR3, IL27, DM, and CSF2 predict edema volume. There is an intersection of genes CCR4, IFNA2, IL9, IL7, IL5, CCR3 to both infarct and edema volumes. Overall, these genes depicts a microenvironment for chemoattraction and proliferation of autoimmune cells, particularly Th2 cells and neutrophils. Conclusions: Machine learning algorithms can be employed to develop predictive biomarker signatures for stroke outcomes in ischemic stroke patients, particularly in regard to identifying acute gene expression changes that occur during stroke.

Response/Abilities of Seeing

2019 ◽  
pp. 1-16
Keyword(s):  
Contemporary Art ◽  
Political Engagement ◽  
The Political ◽  
Theoretical Underpinning ◽  
The 1960S

By considering John Cage's re-phrasing of 'responsibility' as 'response-ability', and thus foregrounding political engagement as requiring aesthetic capabilities and sensibilities, alongside Victor Burgin's extended explorations of the specificities of both making and viewing an artwork, the introduction undertakes two key functions. Firstly, it sets out the context for the book, which concerns Burgin's sustained reading of Barthes, begun in the 1960s, and how we understand images/imaging/Image. Secondly, in dialogue with this context, the chapter establishes the key operative themes and terms operative in the book as a whole. In attempting to set out its theoretical underpinning, the chapter ranges over issues of the political aesthetic, spatial/temporal ethics, the generative capacities of visualisation technologies, and contemporary spectatorship, particularly in relation to contemporary art.

Abstract WMP23: Automated ASPECTS Scoring of CT Scans for Acute Ischemic Stroke Patients Using Machine Learning

Stroke ◽  
2018 ◽  
Vol 49 (Suppl_1) ◽  
Author(s):  
Hulin Kuang ◽  
Ericka Teleg ◽  
Mohamed Najm ◽  
Alexis T Wilson ◽  
Sung I Sohn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Ct Scans ◽  
Stroke Patients
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