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Functional MR imaging detects liars and cheats
TV cops are unlikely to visit their local MR center when extracting confessions from suspected criminals. But the appearance of 3T scanners on programs such as "CSI" could simply be a matter of time, following research demonstrating the accuracy of fMRI-based lie detection.
TV cops are unlikely to visit their local MR center when extracting confessions from suspected criminals. But the appearance of 3T scanners on programs such as "CSI" could simply be a matter of time, following research demonstrating the accuracy of fMRI-based lie detection.
Group-based studies have already shown that telling the truth and lying cause different areas of the brain to light up on fMRI. Now a team from the University of Pennsylvania has shown that this neuropsychological phenomenon can be used to reveal individual liars. Their data analysis has revealed the power of fMRI to identify truthful or false statements correctly almost 90% of the time.
"For fMRI to be used in practice for lie detection, you need to be able to put a subject in a scanner, ask 20 questions, and distinguish between truth and lies without knowing which is which in advance," said Dr. Daniel Langleben, an assistant professor of psychiatry at Penn. "That is what we have done."
Full details of the investigation will be published in Human Brain Mapping and NeuroImage.
FDA Approves Fluorescence Imaging System for Detecting Residual Breast Cancer
April 18th 2024The combination of the optical imaging agent Lumisight and the fluorescence imaging device Lumicell Direct Visualization System, collectively known as LumiSystem, reportedly offers 84 percent accuracy with real-time detection of residual breast cancer after lumpectomy procedures.
Study of Ofatumumab for Multiple Sclerosis Shows 'Profoundly Suppressed MRI Lesion Activity'
April 17th 2024The use of continuous ofatumumab in patients within three years of a relapsing multiple sclerosis diagnosis led to substantial reductions in associated lesions on brain MRI scans, according to research recently presented at the American Academy of Neurology (AAN) conference.
Could a Deep Learning Model for Mammography Improve Prediction of DCIS and Invasive Breast Cancer?
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