Alfonso Nieto-Castanon | |
---|---|
Born | September 1972 |
Alma mater | Universidad de Valladolid, Boston University |
Known for | functional neuroimaging, subject-specific ROIs, connectome, CONN |
Scientific career | |
Fields | Computational neuroscience, Neuroimaging |
Institutions | Boston University, Massachusetts Institute of Technology |
Doctoral advisor | Frank H. Guenther |
Alfonso Nieto-Castanon (born September 1972) is a Spanish computational neuroscientist and developer of computational neuroimaging analysis methods and tools. He is a visiting researcher at the Boston University College of Health and Rehabilitation Sciences, [1] and research affiliate at MIT McGovern Institute for Brain Research. [2] His research focuses on the understanding and characterization of human brain dynamics underlying mental function.
Nieto-Castanon was born in Spain in 1972. [3] He was part of the first Spanish team to participate in the International Physics Olympiad in 1990[ citation needed ]. He went to college at the Universidad de Valladolid from 1991 to 1995 and earned a B.S./M.S. in Telecommunications Engineering. In 1998 he pursued graduate studies in Boston University Cognitive and Neural Systems Department and was awarded a research training fellowship from Fundación Séneca/Cedetel, and a graduate research fellowship from Boston University. He received a Ph.D. in Computational Neuroscience in 2004. [4]
In some of his early work Nieto-Castanon helped develop novel methods for region of interest (ROI) analyses of fMRI data, [5] with a focus on multivariate techniques and the use of subject-specific ROIs, where regions of interest are defined differently for each person based on common anatomical or functional landmarks. [6] [7] Subject-specific ROIs allowed researchers to probe the limits of the functional localization hypotheses common in neuroimaging, and better understand the spatial and functional specificity of different brain areas. [8]
In collaboration with Boston University's Neural Prosthesis Laboratory, Nieto-Castanon helped build a Neuroprosthetic device for real-time speech synthesis. [9] This system was designed to allow patients with locked-in syndrome to produce speech by decoding signals from a neurotrophic electrode implanted in the brain. [10] [11]
Nieto-Castanon also developed multiple influential mathematical and computational techniques for functional connectivity analyses, [12] with a special emphasis on the robust estimation of functional connectivity measures in the presence of subject-motion and physiological noise sources. [13] In 2011 he developed CONN to integrate and facilitate best practices in functional connectivity studies. [14] CONN included a combination of novel methods such as multivariate connectivity analyses and dynamic connectivity estimation, together with multiple well known techniques such as psycho-physiological interactions, graph analyses, or independent component analyses. His software has been since widely adopted in the field [15] [16] [17] [18] [19] and it is now regularly used in functional connectivity studies, with over 900 citations during 2021 alone [20]
Nieto-Castanon has given numerous courses and lectures worldwide [21] [22] [23] [24] [25] and his work has been cited in over 8000 refereed journal articles to date. [26]
Beyond his research, Nieto-Castanon is also recognized for his participation in international programming and data-analysis competitions. Programming in Matlab, Nieto-Castanon won in 2009 and in 2011 the Color Bridge and Vines MathWorks collaborative-programming competitions. [27] [28] He was also the winner in 2011 of the Microsoft Kinect video gesture identification competition, [29] [30] obtained second place at the Marinexplore and Cornell University Whale Detection audio classification challenge, [31] took first prize in 2013 Genentech's Flu Forecasting predictive model competition, [32] and placed second in MathWorks 2014 bin packing optimization competition. [33] In 2013 Nieto-Castanon was ranked as the third best data-scientist in Kaggle, [34] [35] and he has been ranked as the best Matlab programmer in MathWorks Cody games for seven consecutive years between 2013 and 2019. [36]
Functional neuroimaging is the use of neuroimaging technology to measure an aspect of brain function, often with a view to understanding the relationship between activity in certain brain areas and specific mental functions. It is primarily used as a research tool in cognitive neuroscience, cognitive psychology, neuropsychology, and social neuroscience.
Imaging genetics refers to the use of anatomical or physiological imaging technologies as phenotypic assays to evaluate genetic variation. Scientists that first used the term imaging genetics were interested in how genes influence psychopathology and used functional neuroimaging to investigate genes that are expressed in the brain.
Functional near-infrared spectroscopy (fNIRS) is an optical brain monitoring technique which uses near-infrared spectroscopy for the purpose of functional neuroimaging. Using fNIRS, brain activity is measured by using near-infrared light to estimate cortical hemodynamic activity which occur in response to neural activity. Alongside EEG, fNIRS is one of the most common non-invasive neuroimaging techniques which can be used in portable contexts. The signal is often compared with the BOLD signal measured by fMRI and is capable of measuring changes both in oxy- and deoxyhemoglobin concentration, but can only measure from regions near the cortical surface. fNIRS may also be referred to as Optical Topography (OT) and is sometimes referred to simply as NIRS.
The posterior cingulate cortex (PCC) is the caudal part of the cingulate cortex, located posterior to the anterior cingulate cortex. This is the upper part of the "limbic lobe". The cingulate cortex is made up of an area around the midline of the brain. Surrounding areas include the retrosplenial cortex and the precuneus.
FreeSurfer is a brain imaging software package originally developed by Bruce Fischl, Anders Dale, Martin Sereno, and Doug Greve. Development and maintenance of FreeSurfer is now the primary responsibility of the Laboratory for Computational Neuroimaging at the Athinoula A. Martinos Center for Biomedical Imaging. FreeSurfer contains a set of programs with a common focus of analyzing magnetic resonance imaging (MRI) scans of brain tissue. It is an important tool in functional brain mapping and contains tools to conduct both volume based and surface based analysis. FreeSurfer includes tools for the reconstruction of topologically correct and geometrically accurate models of both the gray/white and pial surfaces, for measuring cortical thickness, surface area and folding, and for computing inter-subject registration based on the pattern of cortical folds.
John Gabrieli is a neuroscientist at MIT, and an Investigator at the McGovern Institute for Brain Research. He is the Grover Hermann Professor of Health Sciences and Technology, a faculty member in the department of Brain and Cognitive Sciences and director of the Athinoula A. Martinos Imaging Center, part of the McGovern Institute. Gabrieli is an expert on the brain mechanisms of human cognition, including memory, thought and emotion. His work includes neuroimaging studies on healthy adults and children as well as clinical patients with many different brain disorders, including schizophrenia, depression, Alzheimer's disease, autism and dyslexia.
Dr. Christopher deCharms is a neuroscientist, author, and inventor. Currently, Dr. deCharms is the founder and CEO of Brainful, a life-sciences companies focused on neurotechnology, including technology based on imaging methods that allow people to watch the activation of their own brains 'live' using functional magnetic resonance imaging (fMRI).
A connectome is a comprehensive map of neural connections in the brain, and may be thought of as its "wiring diagram". An organism's nervous system is made up of neurons which communicate through synapses. A connectome is constructed by tracing the neuron in a nervous system and mapping where neurons are connected through synapses.
Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The nervous system is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a high-throughput application of functional and structural neural imaging, most commonly magnetic resonance imaging (MRI), electron microscopy, and histological techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected spanning the nervous system including the various areas of cortex, cerebellum, the retina, the peripheral nervous system and neuromuscular junctions.
In neuroscience, the default mode network (DMN), also known as the default network, default state network, or anatomically the medial frontoparietal network (M-FPN), is a large-scale brain network primarily composed of the dorsal medial prefrontal cortex, posterior cingulate cortex, precuneus and angular gyrus. It is best known for being active when a person is not focused on the outside world and the brain is at wakeful rest, such as during daydreaming and mind-wandering. It can also be active during detailed thoughts related to external task performance. Other times that the DMN is active include when the individual is thinking about others, thinking about themselves, remembering the past, and planning for the future.
Psychophysiological interaction (PPI) is a brain connectivity analysis method for functional brain imaging data, mainly functional magnetic resonance imaging (fMRI). It estimates context-dependent changes in effective connectivity (coupling) between brain regions. Thus, PPI analysis identifies brain regions whose activity depends on an interaction between psychological context and physiological state of the seed region.
The Human Connectome Project (HCP) is a five-year project sponsored by sixteen components of the National Institutes of Health, split between two consortia of research institutions. The project was launched in July 2009 as the first of three Grand Challenges of the NIH's Blueprint for Neuroscience Research. On September 15, 2010, the NIH announced that it would award two grants: $30 million over five years to a consortium led by Washington University in St. Louis and the University of Minnesota, with strong contributions from University of Oxford (FMRIB) and $8.5 million over three years to a consortium led by Harvard University, Massachusetts General Hospital and the University of California Los Angeles.
Resting state fMRI is a method of functional magnetic resonance imaging (fMRI) that is used in brain mapping to evaluate regional interactions that occur in a resting or task-negative state, when an explicit task is not being performed. A number of resting-state brain networks have been identified, one of which is the default mode network. These brain networks are observed through changes in blood flow in the brain which creates what is referred to as a blood-oxygen-level dependent (BOLD) signal that can be measured using fMRI.
Dynamic functional connectivity (DFC) refers to the observed phenomenon that functional connectivity changes over a short time. Dynamic functional connectivity is a recent expansion on traditional functional connectivity analysis which typically assumes that functional networks are static in time. DFC is related to a variety of different neurological disorders, and has been suggested to be a more accurate representation of functional brain networks. The primary tool for analyzing DFC is fMRI, but DFC has also been observed with several other mediums. DFC is a recent development within the field of functional neuroimaging whose discovery was motivated by the observation of temporal variability in the rising field of steady state connectivity research.
CONN is a Matlab-based cross-platform imaging software for the computation, display, and analysis of functional connectivity in fMRI in the resting state and during task.
Russell "Russ" Alan Poldrack is an American psychologist and neuroscientist. He is a professor of psychology at Stanford University, associate director of Stanford Data Science, member of the Stanford Neuroscience Institute and director of the Stanford Center for Reproducible Neuroscience and the SDS Center for Open and Reproducible Science.
Stefan G. Hofmann is a German-born clinical psychologist. He is the Alexander von Humboldt Professor and recipient of the LOEWE Spitzenprofessur for Translational Clinical Psychology at the Philipps University of Marburg in Germany, examining Cognitive Behavioral Therapy, especially for anxiety disorders.
Susan Whitfield-Gabrieli is an American scientist, psychologist/neuroscientist, academic and researcher. She is a professor of psychology, the Founding Director of the Biomedical Imaging Center at Northeastern University, Researcher in the Department of Psychiatry at Massachusetts General Hospital, Harvard Medical School and a Research Affiliate of McGovern Institute for Brain Research at Massachusetts Institute of Technology.