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Changing the Paradigm from Neurochemical to Neuroelectrical Models
Published in Hanno W. Kirk, Restoring the Brain, 2020
The mathematical formalism for the understanding of networks did not become available until the 1990s. The brain is perhaps the most elaborate exemplar in the known universe of what is known as the “small-world” model of networks. This is a combination of high local connectivity – composed of the dendritic tree on the input site and the axonal branching network on the output side – and of high distal connectivity. The latter follows from the fact that every cortical pyramidal cell participates in the communication with distal networks by means of axons that jointly constitute the cortical white matter. By virtue of the globally connected network of pyramidal cells, the brain is drawn into a unitary functional entity, with every part communicating with every other part more or less directly. As the National Institute of Health (NIH)-sponsored Human Connectome Project has shown, our brain is so interconnected that any synapse in the cortex is no more than three synapses away from any other synapse in the cortex.28
Korbinian Brodmann (1868–1918)
Published in Andrew P. Wickens, Key Thinkers in Neuroscience, 2018
The fascination with mapping the cerebral cortex has never died. Indeed, one of the most remarkable attempts to extend Brodmann’s topography was made by Constantin von Economo and Georg Koskinas at the University of Vienna in 1925. They produced a book and atlas of over 800 pages, containing 112 photographs and 162 figures, which identified 107 different cortical regions. Although this work has been described as a milestone in the history of science, it surprisingly never gained widespread acceptance. More recently, the Human Connectome Project, launched in 2009 by the National Institute of Health (NIH), whose goal is to build a complete “network map” or connectome of the human brain, fibre by fibre, has published results from highly detailed magnetic resonance imaging (MRI) scans of 201 healthy young adults. This has revealed a map showing 180 different regions – including ninety-seven “new” brain areas that have never been described before (Glasser et al. 2016). This map, it is argued, not only should help scientists working in the field of neuroimaging to be more certain about the areas of the brain they are observing, but should also assist many other types of brain scientist interested in structural-functional relationships. It remains to be seen whether this will replace Brodmann’s parcellation of brain areas in the future.
Attention and Executive Function Disorders
Published in Christopher J. Nicholls, Neurodevelopmental Disorders in Children and Adolescents, 2018
The Human Connectome Project (n.d.) represents one example of the new approaches to understanding the brain. This collaboration between the University of Southern California’s Laboratory of Neuro Imaging and the Martinos Center for Biomedical Imaging at Massachusetts General Hospital has as its goal the construction of a map of the complete structural and functional neural connections of the brain. While producing spectacular images of the major brain pathways, this project hopes to map the essential circuits of the brain, to allow us to explore the cells of various areas of brain and the functions that depend upon those cells. What becomes abundantly clear is that a problem in one section of a circuit has far-reaching consequences and implications for other sections of that circuit. We are therefore turning our attention away from specific locations or areas of the brain, to the study of behaviors and skills that are reflective of complicated circuits and networks of brain “wiring.”
Dimension-wise sparse low-rank approximation of a matrix with application to variable selection in high-dimensional integrative analyzes of association
Published in Journal of Applied Statistics, 2022
J. C. Poythress, Cheolwoo Park, Jeongyoun Ahn
Much of modern scientific research aims to characterize the associations among high-dimensional, multimodal data. That is, researchers measure many variables representing different, often complementary, sources of information with the goal of performing an integrative analysis that combines information across all of the sources. For example, in epigenetics, one might want to understand how methylation of CpG sites is associated with the expression of genes related to breast cancer [13]. In a model of ischemic stroke, one might want to know how magnetic resonance imaging (MRI) measurements obtained shortly after stroke are related to short- and long-term recovery patterns in behavior, cognition, and mobility [7,22]. In the Human Connectome Project (humanconnectomeproject.org), researchers hope to map the anatomical and functional connectivity of the brain by integrating information obtained by structural MRI, functional MRI, diffusion MRI, and other imaging modalities. In each example, not only are data obtained from different modalities, but also within a modality, the number of variables measured often exceeds the number of subjects. In the breast cancer study by [13], the methylation levels at 1452 CpG sites and gene expressions for 511 genes were obtained from 179 samples. Thus, the total number of variables measured exceeded the sample size by an order of magnitude.
Alterations in ventral attention network connectivity in individuals with prediabetes
Published in Nutritional Neuroscience, 2021
Jennifer R. Sadler, Grace E. Shearrer, Kyle S. Burger
Data collection, preprocessing, and analysis for the PTN release in the Human Connectome Project has been detailed extensively elsewhere [17], therefore it will only be summarized here. Participants completed 4 resting state functional MRI (rfMRI) runs, totaling 58 min and 12 s of rfMRI data per participant [16]. Each rfMRI scan was performed on a 3 Tesla Siemen’s Skyra magnet and used an eight-factor multiband, gradient echo EPI sequence with the following parameters: TR: 720 ms, TE: 33.1 ms, flip angle: 52 degrees, slice thickness: 2.0 mm [16]. During the rsfMRI scan, participants were instructed to look at a light crosshair on a dark background projected into their field of view. The Human Connectome Project preprocessed all downloaded data in the HCP1200-PTN release using the recommended minimal preprocessing pipeline [18], and no additional preprocessing was performed locally.
Predictive analytics and machine learning in stroke and neurovascular medicine
Published in Neurological Research, 2019
Hamidreza Saber, Melek Somai, Gary B. Rajah, Fabien Scalzo, David S. Liebeskind
There has been an increasing trend in the adoption of predictive analytics in medicine (Figure 3(a)). It is widely used in biomedical research, including the ‘-omics’ fields (e.g. genomics, epigenomics, proteomics, lipidomics, and metabolomics) [10,11]. Other examples include ICU real-time risk prediction [12,13], accurate prediction of sepsis or septic shock [14–16], pathological labeling and pattern recognition for various types of pathologies including diagnosis and prognosis of tumors [17–21], and efficient processing of imaging biomarkers required for early detection and progression of various disorders [22]. A study using machine learning algorithm used positron emission tomography (PET) scan imaging data to train a model to look for differences between elderly patients with expected, age-related stable MCI and those with progressive MCI based on the regional information from amyloid [22–25]. Other growing applications are in the field of population health for management interventions and for tailoring cost-effective health-care interventions [26,27]. The role of artificial intelligence has been continuously growing in all aspects of neuroscience, with the development of international big data neuroscience initiatives, e.g. the Human Brain Project, the BRAIN initiative, the Human Connectome Project, and the National Institute of Mental Health’s Research Domain Criteria initiative. Overall, these techniques facilitate predicting clinical events and promote the identification, stratification, and management of patients who are at highest risk of poor health outcomes or who will benefit most from specific therapeutics or interventions.