scholarly journals Evidence of memory from brain data

2020 ◽  
Author(s):  
Emily R D Murphy ◽  
Jesse Rissman
Keyword(s):  
Human Memory ◽  
Machine Learning Algorithms ◽  
Imaging Analysis ◽  
Conceptual Foundation ◽  
Legal Proceedings ◽  
Psychological Understanding ◽  
New Information ◽  
Witness Memory ◽  
Brain Data ◽  
Technological Constraints

Abstract Much courtroom evidence relies on assessing witness memory. Recent advances in brain imaging analysis techniques offer new information about the nature of autobiographical memory and introduce the potential for brain-based memory detection. In particular, the use of powerful machine-learning algorithms reveals the limits of technological capacities to detect true memories and contributes to existing psychological understanding that all memory is potentially flawed. This article first provides the conceptual foundation for brain-based memory detection as evidence. It then comprehensively reviews the state of the art in brain-based memory detection research before establishing a framework for admissibility of brain-based memory detection evidence in the courtroom and considering whether and how such use would be consistent with notions of justice. The central question that this interdisciplinary analysis presents is: if the science is sophisticated enough to demonstrate that accurate, veridical memory detection is limited by biological, rather than technological, constraints, what should that understanding mean for broader legal conceptions of how memory is traditionally assessed and relied upon in legal proceedings? Ultimately, we argue that courtroom admissibility is presently a misdirected pursuit, though there is still much to be gained from advancing our understanding of the biology of human memory.

Eyewitness Testimony

2018 ◽  
pp. 31-43 ◽  
Author(s):  
Elizabeth F. Loftus
Keyword(s):  
Legal System ◽  
Expert Testimony ◽  
Human Memory ◽  
Eyewitness Testimony ◽  
Court Cases ◽  
Legal Cases ◽  
Repressed Memories ◽  
Witness Memory

This chapter describes the author’s studies of human memory and eyewitness testimony that drew her into a long involvement with the legal system. The history describes efforts on the part of lawyers and eyewitness scientists to introduce expert testimony about witness memory into legal cases. The author discusses the contamination of accurate memories due to misinformation after the fact, such as witnesses talking to one another or erroneous media, and the role of repressed memories in court cases. The chapter includes a brief description of the rocky path from early resistance to ultimate appreciation of the science and its usefulness in legal cases.

scholarly journals Metabolic Impairment in Human Amnesia: A PET Study of Memory Networks

1992 ◽  
Vol 12 (3) ◽  
pp. 353-358 ◽  
Author(s):  
Ferruccio Fazio ◽  
Daniela Perani ◽  
Maria Carla Gilardi ◽  
Fabio Colombo ◽  
Stefano F. Cappa ◽  
...  
Keyword(s):  
Hippocampal Formation ◽  
Cerebral Metabolism ◽  
Human Memory ◽  
Clinical Syndrome ◽  
Long Term Memory ◽  
Positron Emission ◽  
New Information ◽  
Magnetic Resonance Imaging Mri

Human amnesia is a clinical syndrome exhibiting the failure to recall past events and to learn new information. Its “pure” form, characterized by a selective impairment of long-term memory without any disorder of general intelligence or other cognitive functions, has been associated with lesions localized within Papez's circuit and some connected areas. Thus, amnesia could be due to a functional disconnection between components of this or other neural structures involved in long-term learning and retention. To test this hypothesis, we measured regional cerebral metabolism with 2-[18F]fluoro-2-deoxy-d-glucose ([18F]FDG) and positron emission tomography (PET) in 11 patients with “pure” amnesia. A significant bilateral reduction in metabolism in a number of interconnected cerebral regions (hippocampal formation, thalamus, cingulate gyrus, and frontal basal cortex) was found in the amnesic patients in comparison with normal controls. The metabolic impairment did not correspond to alterations in structural anatomy as assessed by magnetic resonance imaging (MRI). These results are the first in vivo evidence for the role of a functional network as a basis of human memory.

scholarly journals A Survey of Crowdsourcing in Medical Image Analysis

2020 ◽  
Vol 7 ◽  
pp. 1-26 ◽  
Author(s):  
Silas Nyboe Ørting ◽  
Andrew Doyle ◽  
Arno Van Hilten ◽  
Matthias Hirth ◽  
Oana Inel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Imaging Analysis ◽  
High Quality ◽  
Comprehensive Literature Review

Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

scholarly journals Analysis On Deep Learning Approaches For Timely Detection Of Osteoarthritis

2021 ◽  
Vol 12 (3) ◽  
pp. 4396-4405
Author(s):  
R Kanthavel Et.al
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Elastic Tissue ◽  
Learning Approaches ◽  
X Rays ◽  
Imaging Analysis ◽  
Bone Injury ◽  
Learning Techniques

Osteoarthritis is mainly a familiar kind of arthritis when an elastic tissue named Cartilage that softens the tops of the bones, cracks down. The Person with osteoarthritis can encompass joint pain, inflexibility, or inflammation and there is no particular examination for osteoarthritis and physicians take the amalgamation of both medical cum clinical record and X-rays imaging analysis to make a diagnosis of the state. Osteoarthritis is generally only detected following ache and bone scratch and in advance, analysis could permit for ultimate involvement to avoid cartilage worsening and bone injury. Through machine-learning algorithms, the system can be trained to automatically distinguish among people who would develop osteoarthritis and persons who would not with the detection of exact biochemical variances in the midpoint of the knee’s cartilage. The outcome of the Machine learning Techniques will give the persons who are pre-symptomatic by the occasion of the baseline imaging and also the reduction in liquid concentration. In this study, we present the analysis of various deep learning techniques for timely detection of osteoarthritis disease. Several subsets of machine learning called deep learning techniques have been in use for the timely detection of osteoarthritis disease; and therefore analysis is needed highly to choose the best as far as accuracy and reliability are concerned.

scholarly journals Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System

2021 ◽  
Vol 12 ◽  
Author(s):  
Rachel E. Stirling ◽  
Matias I. Maturana ◽  
Philippa J. Karoly ◽  
Ewan S. Nurse ◽  
Kate McCutcheon ◽  
...  
Keyword(s):  
Seizure Activity ◽  
Characteristic Curve ◽  
Epileptiform Activity ◽  
Machine Learning Algorithms ◽  
Eeg Monitoring ◽  
Accurate Identification ◽  
Patient Reported ◽  
Diagnostic Management ◽  
Eeg Recordings ◽  
Brain Data

Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.

scholarly journals Sport and exercise genomics: the FIMS 2019 consensus statement update

2020 ◽  
Vol 54 (16) ◽  
pp. 969-975 ◽  
Author(s):  
Kumpei Tanisawa ◽  
Guan Wang ◽  
Jane Seto ◽  
Ioanna Verdouka ◽  
Richard Twycross-Lewis ◽  
...  
Keyword(s):  
Data Protection ◽  
Swot Analysis ◽  
Machine Learning Algorithms ◽  
Genomic Information ◽  
Inappropriate Use ◽  
Exercise Medicine ◽  
New Information ◽  
High Throughput Dna Sequencing ◽  
Conducting Research ◽  
Sport And Exercise

Rapid advances in technologies in the field of genomics such as high throughput DNA sequencing, big data processing by machine learning algorithms and gene-editing techniques are expected to make precision medicine and gene-therapy a greater reality. However, this development will raise many important new issues, including ethical, moral, social and privacy issues. The field of exercise genomics has also advanced by incorporating these innovative technologies. There is therefore an urgent need for guiding references for sport and exercise genomics to allow the necessary advancements in this field of sport and exercise medicine, while protecting athletes from any invasion of privacy and misuse of their genomic information. Here, we update a previous consensus and develop a guiding reference for sport and exercise genomics based on a SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis. This SWOT analysis and the developed guiding reference highlight the need for scientists/clinicians to be well-versed in ethics and data protection policy to advance sport and exercise genomics without compromising the privacy of athletes and the efforts of international sports federations. Conducting research based on the present guiding reference will mitigate to a great extent the risks brought about by inappropriate use of genomic information and allow further development of sport and exercise genomics in accordance with best ethical standards and international data protection principles and policies. This guiding reference should regularly be updated on the basis of new information emerging from the area of sport and exercise medicine as well as from the developments and challenges in genomics of health and disease in general in order to best protect the athletes, patients and all other relevant stakeholders.

scholarly journals A Survey of Crowdsourcing in Medical Image Analysis

2020 ◽  
Vol 7 ◽  
pp. 1-26
Author(s):  
Silas Nyboe Ørting ◽  
Andrew Doyle ◽  
Arno Van Hilten ◽  
Matthias Hirth ◽  
Oana Inel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Imaging Analysis ◽  
High Quality ◽  
Comprehensive Literature Review

Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

scholarly journals Visual working memory buffers information retrieved from visual long-term memory

2017 ◽  
Vol 114 (20) ◽  
pp. 5306-5311 ◽  
Author(s):  
Keisuke Fukuda ◽  
Geoffrey F. Woodman
Keyword(s):  
Working Memory ◽  
Human Memory ◽  
Long Term Memory ◽  
High Temporal Resolution ◽  
Term Memory ◽  
Long Term Storage ◽  
New Information ◽  
Neurophysiological Activity ◽  
Term Storage

Human memory is thought to consist of long-term storage and short-term storage mechanisms, the latter known as working memory. Although it has long been assumed that information retrieved from long-term memory is represented in working memory, we lack neural evidence for this and need neural measures that allow us to watch this retrieval into working memory unfold with high temporal resolution. Here, we show that human electrophysiology can be used to track information as it is brought back into working memory during retrieval from long-term memory. Specifically, we found that the retrieval of information from long-term memory was limited to just a few simple objects’ worth of information at once, and elicited a pattern of neurophysiological activity similar to that observed when people encode new information into working memory. Our findings suggest that working memory is where information is buffered when being retrieved from long-term memory and reconcile current theories of memory retrieval with classic notions about the memory mechanisms involved.

scholarly journals Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System

2021 ◽  
Author(s):  
Rachel E Stirling ◽  
Philippa J Karoly ◽  
Matias Maturana ◽  
Ewan S Nurse ◽  
Kate McCutcheon ◽  
...  
Keyword(s):  
Seizure Activity ◽  
Characteristic Curve ◽  
Epileptiform Activity ◽  
Machine Learning Algorithms ◽  
Eeg Monitoring ◽  
Accurate Identification ◽  
Patient Reported ◽  
Diagnostic Management ◽  
Eeg Recordings ◽  
Brain Data

Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder TM), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilising cycles in EA and previous seizure times. The procedures and devices were well tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88) is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.

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