View allAll Photos Tagged Brain_Imaging
Portrait of Luxiao Yu the Laboratory of Integrated Brain Imaging at the University of Michigan.
The Laboratory of Integrated Brain Imaging directed by Zhongming Liu, aims to accelerate neuroscience and artificial intelligence by developing novel tools to integrate neural imaging, recording, stimulation, and modeling. The lab is part of the Department of Biomedical Engineering and the Electrical and Computer Engineering Division in the Department of Electrical Engineering and Computer Science at the University of Michigan.
November 04, 2025
Photo by Marcin Szczepanski/Lead Multimedia Storyteller, University of Michigan College of Engineering
Diagnostic Imaging Services now offers NeuroQuant®, for fast, accurate and proven automated brain image analysis as part of a routine brain MRI.
NeuroQuant segments and measures volumes of the hippocampus, ventricles and other brain structures and compares the volumes to norms, based on the patient’s age, gender and intracranial volume. This information helps providers assess neurological conditions and neurodegenerative diseases such as:
• Alzheimer’s disease
• Epilepsy
• Traumatic brain injury
• Multiple Sclerosis
NeuroQuant volumetric MRI is performed at six Diagnostic Imaging Services locations. This specialty MRI study is completed via our 1.2T and 1.5T high field MRI systems and our 3T ultra-high field MRI.
Portrait of Fatimah Alkaabi from the Laboratory of Integrated Brain Imaging at the University of Michigan.
The Laboratory of Integrated Brain Imaging directed by Zhongming Liu, aims to accelerate neuroscience and artificial intelligence by developing novel tools to integrate neural imaging, recording, stimulation, and modeling. The lab is part of the Department of Biomedical Engineering and the Electrical and Computer Engineering Division in the Department of Electrical Engineering and Computer Science at the University of Michigan.
November 04, 2025
Photo by Marcin Szczepanski/Lead Multimedia Storyteller, University of Michigan College of Engineering
Group portrait of the members of the Laboratory of Integrated Brain Imaging at the University of Michigan.
The Laboratory of Integrated Brain Imaging directed by Zhongming Liu, aims to accelerate neuroscience and artificial intelligence by developing novel tools to integrate neural imaging, recording, stimulation, and modeling. The lab is part of the Department of Biomedical Engineering and the Electrical and Computer Engineering Division in the Department of Electrical Engineering and Computer Science at the University of Michigan.
From left to right: Zhongming Liu, Luxiao Yu, Xiaokai Wang, Ulrich Scheven, Wenxin Hu, Maddison Cayer, Owen MacKenzie, Zan Huang, Ashley Cornett, Jiyang Liu, Rodrigo Lobos, Fatimah Alkaabi, and Xiaoyin Wu.
November 04, 2025
Photo by Marcin Szczepanski/Lead Multimedia Storyteller, University of Michigan College of Engineering
Portrait of Jiyang Liu of the Laboratory of Integrated Brain Imaging at the University of Michigan.
The Laboratory of Integrated Brain Imaging directed by Zhongming Liu, aims to accelerate neuroscience and artificial intelligence by developing novel tools to integrate neural imaging, recording, stimulation, and modeling. The lab is part of the Department of Biomedical Engineering and the Electrical and Computer Engineering Division in the Department of Electrical Engineering and Computer Science at the University of Michigan.
November 04, 2025
Photo by Marcin Szczepanski/Lead Multimedia Storyteller, University of Michigan College of Engineering
Abstract
Brain imaging methods have revolutionized brain science by making it possible to observe neural activity in humans and animals at a level of detail never before possible. But understanding the brain requires that we understand more than what neural activity exists — we must understand the information it encodes, and how it operates on this information. We will describe a family of machine learning methods for auto-matically decoding the information encoded by neural activity, and their use to uncover new knowledge of how the brain represents meaning during reading.
Live Broadcast: coe.miami.edu/speaker/mitchell
Dr. Tom M. Mitchell is the E. Fredkin University Professor at Carnegie Mellon University, where he founded the world’s first Machine Learning Department. His research uses machine learning to develop computers that are learning to read the web, and uses brain imaging to study how the human brain understands what it reads. Mitchell is a member of the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI). In 2015 he received an honorary Doctor of Laws degree from Dalhousie University for his contributions to machine learning and cognitive neuroscience.
Portrait of Luxiao Yu from the Laboratory of Integrated Brain Imaging at the University of Michigan.
The Laboratory of Integrated Brain Imaging directed by Zhongming Liu, aims to accelerate neuroscience and artificial intelligence by developing novel tools to integrate neural imaging, recording, stimulation, and modeling. The lab is part of the Department of Biomedical Engineering and the Electrical and Computer Engineering Division in the Department of Electrical Engineering and Computer Science at the University of Michigan.
November 04, 2025
Photo by Marcin Szczepanski/Lead Multimedia Storyteller, University of Michigan College of Engineering
Abstract
Brain imaging methods have revolutionized brain science by making it possible to observe neural activity in humans and animals at a level of detail never before possible. But understanding the brain requires that we understand more than what neural activity exists — we must understand the information it encodes, and how it operates on this information. We will describe a family of machine learning methods for auto-matically decoding the information encoded by neural activity, and their use to uncover new knowledge of how the brain represents meaning during reading.
Live Broadcast: coe.miami.edu/speaker/mitchell
Dr. Tom M. Mitchell is the E. Fredkin University Professor at Carnegie Mellon University, where he founded the world’s first Machine Learning Department. His research uses machine learning to develop computers that are learning to read the web, and uses brain imaging to study how the human brain understands what it reads. Mitchell is a member of the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI). In 2015 he received an honorary Doctor of Laws degree from Dalhousie University for his contributions to machine learning and cognitive neuroscience.
Psychology professor Richard Davidson (R) shares a laugh with the 14th Dalai Lama of Tibet Tenzin Gyatso (center) and Buddhist monk Geshe Sopa during a tour of the Keck Laboratory for Functional Brain Imaging and Behavior. rain imaging experience at the facility..© UW-Madison University Communications 608/262-0067.Photo by: Jeff Miller.Date: 05/01 File#: 0105-105c-26a
"The title of the work means to manage and care for the future. 'Net curtains remind me of the lapses that occur in the brain. Images projected onto one curtain inevitably fall apart on the ones behind"
Pipilotti Rist at the MCA in Sydney
Abstract
Brain imaging methods have revolutionized brain science by making it possible to observe neural activity in humans and animals at a level of detail never before possible. But understanding the brain requires that we understand more than what neural activity exists — we must understand the information it encodes, and how it operates on this information. We will describe a family of machine learning methods for auto-matically decoding the information encoded by neural activity, and their use to uncover new knowledge of how the brain represents meaning during reading.
Live Broadcast: coe.miami.edu/speaker/mitchell
Dr. Tom M. Mitchell is the E. Fredkin University Professor at Carnegie Mellon University, where he founded the world’s first Machine Learning Department. His research uses machine learning to develop computers that are learning to read the web, and uses brain imaging to study how the human brain understands what it reads. Mitchell is a member of the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI). In 2015 he received an honorary Doctor of Laws degree from Dalhousie University for his contributions to machine learning and cognitive neuroscience.
Abstract
Brain imaging methods have revolutionized brain science by making it possible to observe neural activity in humans and animals at a level of detail never before possible. But understanding the brain requires that we understand more than what neural activity exists — we must understand the information it encodes, and how it operates on this information. We will describe a family of machine learning methods for auto-matically decoding the information encoded by neural activity, and their use to uncover new knowledge of how the brain represents meaning during reading.
Live Broadcast: coe.miami.edu/speaker/mitchell
Dr. Tom M. Mitchell is the E. Fredkin University Professor at Carnegie Mellon University, where he founded the world’s first Machine Learning Department. His research uses machine learning to develop computers that are learning to read the web, and uses brain imaging to study how the human brain understands what it reads. Mitchell is a member of the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI). In 2015 he received an honorary Doctor of Laws degree from Dalhousie University for his contributions to machine learning and cognitive neuroscience.
Portrait of Ashley Cornett from the Laboratory of Integrated Brain Imaging at the University of Michigan.
The Laboratory of Integrated Brain Imaging directed by Zhongming Liu, aims to accelerate neuroscience and artificial intelligence by developing novel tools to integrate neural imaging, recording, stimulation, and modeling. The lab is part of the Department of Biomedical Engineering and the Electrical and Computer Engineering Division in the Department of Electrical Engineering and Computer Science at the University of Michigan.
November 04, 2025
Photo by Marcin Szczepanski/Lead Multimedia Storyteller, University of Michigan College of Engineering
Portrait of Zhongming Liu, the director of the Laboratory of Integrated Brain Imaging at the University of Michigan.
The Laboratory of Integrated Brain Imaging aims to accelerate neuroscience and artificial intelligence by developing novel tools to integrate neural imaging, recording, stimulation, and modeling. The lab is part of the Department of Biomedical Engineering and the Electrical and Computer Engineering Division in the Department of Electrical Engineering and Computer Science at the University of Michigan.
November 04, 2025
Photo by Marcin Szczepanski/Lead Multimedia Storyteller, University of Michigan College of Engineering
Abstract
Brain imaging methods have revolutionized brain science by making it possible to observe neural activity in humans and animals at a level of detail never before possible. But understanding the brain requires that we understand more than what neural activity exists — we must understand the information it encodes, and how it operates on this information. We will describe a family of machine learning methods for auto-matically decoding the information encoded by neural activity, and their use to uncover new knowledge of how the brain represents meaning during reading.
Live Broadcast: coe.miami.edu/speaker/mitchell
Dr. Tom M. Mitchell is the E. Fredkin University Professor at Carnegie Mellon University, where he founded the world’s first Machine Learning Department. His research uses machine learning to develop computers that are learning to read the web, and uses brain imaging to study how the human brain understands what it reads. Mitchell is a member of the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI). In 2015 he received an honorary Doctor of Laws degree from Dalhousie University for his contributions to machine learning and cognitive neuroscience.
Abstract
Brain imaging methods have revolutionized brain science by making it possible to observe neural activity in humans and animals at a level of detail never before possible. But understanding the brain requires that we understand more than what neural activity exists — we must understand the information it encodes, and how it operates on this information. We will describe a family of machine learning methods for auto-matically decoding the information encoded by neural activity, and their use to uncover new knowledge of how the brain represents meaning during reading.
Live Broadcast: coe.miami.edu/speaker/mitchell
Dr. Tom M. Mitchell is the E. Fredkin University Professor at Carnegie Mellon University, where he founded the world’s first Machine Learning Department. His research uses machine learning to develop computers that are learning to read the web, and uses brain imaging to study how the human brain understands what it reads. Mitchell is a member of the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI). In 2015 he received an honorary Doctor of Laws degree from Dalhousie University for his contributions to machine learning and cognitive neuroscience.
Abstract
Brain imaging methods have revolutionized brain science by making it possible to observe neural activity in humans and animals at a level of detail never before possible. But understanding the brain requires that we understand more than what neural activity exists — we must understand the information it encodes, and how it operates on this information. We will describe a family of machine learning methods for auto-matically decoding the information encoded by neural activity, and their use to uncover new knowledge of how the brain represents meaning during reading.
Live Broadcast: coe.miami.edu/speaker/mitchell
Dr. Tom M. Mitchell is the E. Fredkin University Professor at Carnegie Mellon University, where he founded the world’s first Machine Learning Department. His research uses machine learning to develop computers that are learning to read the web, and uses brain imaging to study how the human brain understands what it reads. Mitchell is a member of the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI). In 2015 he received an honorary Doctor of Laws degree from Dalhousie University for his contributions to machine learning and cognitive neuroscience.
VISE ON THE ROAD:
VISE affiliate and MASI Lab member Allison Hainline, presented her conference poster, "Evidence-based inference on resting state functional connectivity" not once, but TWICE(!) at two different meetings (in less than a week!)
First up, (June 17-21) Hainline presented at the Organization for Human Brain Mapping annual meeting and then went on to the OHBM Satellite Meeting, Nonstandard Brain Image Analysis from June 22-23 (both in Singapore)
...isn't it? Anyway, second piece of proof that Laure did some hard work. No fake! Notice the brain image on the back of the paper! The sheer concentration!
We welcome Dr. Mohamed Hadi Eltonsi as a #Featured #Speaker at 2019 World Neuroscience and Psychiatry Conferencein Singapore – neuroscience.episirus.org/
#brain #neurology #conference #neuroscience #neurosurgery #neurologicaldisorders #Dementia #Headache #Brain_Imaging #Neuroimaging #Neuropsychology #MentalHealth #ScienceOutreach #Blog #Health #BiomedicalResearch #Autism #ASD #Neurofibramatosis #Depression #Sexual_Dysfunction #neurologicaldisorders #neurosurgery
Abstract
Brain imaging methods have revolutionized brain science by making it possible to observe neural activity in humans and animals at a level of detail never before possible. But understanding the brain requires that we understand more than what neural activity exists — we must understand the information it encodes, and how it operates on this information. We will describe a family of machine learning methods for auto-matically decoding the information encoded by neural activity, and their use to uncover new knowledge of how the brain represents meaning during reading.
Live Broadcast: coe.miami.edu/speaker/mitchell
Dr. Tom M. Mitchell is the E. Fredkin University Professor at Carnegie Mellon University, where he founded the world’s first Machine Learning Department. His research uses machine learning to develop computers that are learning to read the web, and uses brain imaging to study how the human brain understands what it reads. Mitchell is a member of the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI). In 2015 he received an honorary Doctor of Laws degree from Dalhousie University for his contributions to machine learning and cognitive neuroscience.
"The title of the work means to manage and care for the future. 'Net curtains remind me of the lapses that occur in the brain. Images projected onto one curtain inevitably fall apart on the ones behind"
Pipilotti Rist at the MCA in Sydney
Abstract
Brain imaging methods have revolutionized brain science by making it possible to observe neural activity in humans and animals at a level of detail never before possible. But understanding the brain requires that we understand more than what neural activity exists — we must understand the information it encodes, and how it operates on this information. We will describe a family of machine learning methods for auto-matically decoding the information encoded by neural activity, and their use to uncover new knowledge of how the brain represents meaning during reading.
Live Broadcast: coe.miami.edu/speaker/mitchell
Dr. Tom M. Mitchell is the E. Fredkin University Professor at Carnegie Mellon University, where he founded the world’s first Machine Learning Department. His research uses machine learning to develop computers that are learning to read the web, and uses brain imaging to study how the human brain understands what it reads. Mitchell is a member of the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI). In 2015 he received an honorary Doctor of Laws degree from Dalhousie University for his contributions to machine learning and cognitive neuroscience.
Two fundamental questions that clinical scientists and practitioners alike must address include when are emotions functional and when are they dysfunctional? Recent advances in affective science have provided new tools with which to address these age-old questions. The past few decades have witnessed an explosion of research on emotion, accompanied by new theories, such as evolutionary-based analyses of emotion, as well as methods, such as anatomically based systems for coding facial expressive behavior and brain imaging strategies. These developments have provided new tools with which to explore the role that emotions play in mental health and illness. (Product Description)
RC455.4.E46 E38 2007
Yes, a brain mapping test can be beneficial in several ways. Brain mapping, also known as functional brain imaging or neuroimaging, is a technique used to visualize and analyze the structure and function of the brain. It involves the use of various imaging technologies, such as magnetic resonance imaging (MRI), positron emission tomography (PET), or electroencephalography (EEG), to create detailed maps of the brain. visit - www.serenityclinic.care/service/brain-mapping-test/