Academic literature on the topic 'Whole brain model'
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Journal articles on the topic "Whole brain model"
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Delawati, Denna. "KETERAMPILAN BERPIKIR KRITIS: MODEL BRAIN-BASED LEARNING DAN DAN MODEL WHOLE BRAIN TEACHING." Jurnal Bidang Pendidikan Dasar 3, no. 2 (July 5, 2019): 9–14. http://dx.doi.org/10.21067/jbpd.v3i2.3356.
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Penelitian ini bertujuan untuk mengetahui perbedaan keterampilan berpikir krtitis siswa dengan menggunakan model pembelajaran Brain-Based Learning dan model Whole Brain Teaching pada muatan IPA kelas V SDN 3 Senggreng Kecamatan Sumberpucung. Jenis penelitian ini adalah Pra-Eksperimental Design dengan rancangan The Static Group Pretest-Posttest Design. Sampel yang digunakan adalah seluruh kelas 5A sebagai eksperimen 1 dan kelas 5B sebagai eksperimen 2. Instrumen penelitian yang digunakan berupa tes untuk menguji berpikir kritis siswa. Hasil analisis data menunjukkan bahwa keterampilan berpikir kritis dengan menggunakan model Whole Brain Teaching lebih tinggi dibandingkan menggunakan model Brain-Based Learning. Data yang diperoleh menggunakan analisis Uji-t. Dari hasil Uji-t diketahui bahwa sebesar 2,127 dan 2,122, lebih besar ttabel (> 2,020) dan nilai signifikasi 5% menunjukkan bahwa nilai 0,039 dan 0,040 (< 0,05), oleh karena itu hipotesis alternatif diterima. Dengan demikian terdapat perbedaan model Brain-Based Learning dan model Whole Brain Teaching pada keterampilan berpikir kritis IPA kelas V SDN 3 Senggreng Kecamatan Sumberpucung. Diharapan dengan menggunakan model berbasis otak ini, siswa akan lebih mudah memahami materi dan dapat menyelesaikan permasalahan dalam kehidupan sehari-hari.
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Handayani, Baiq Sri, and A. D. Corebima. "Model brain based learning (BBL) and whole brain teaching (WBT) in learning." International Journal of Science and Applied Science: Conference Series 1, no. 2 (August 14, 2017): 153. http://dx.doi.org/10.20961/ijsascs.v1i2.5142.
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<p class="Abstract">The learning process is a process of change in behavior as a form of the result of learning. The learning model is a crucial component of the success of the learning process. The learning model is growing fastly, and each model has different characteristics. Teachers are required to be able to understand each model to teach the students optimally by matching the materials and the learning model. The best of the learning model is the model that based on the brain system in learning that are the model of Brain Based Learning (BBL) and the model of Whole Brain Teaching (WBT). The purposes of this article are to obtain information related to (1) the brain’s natural learning system, (2) analyze the characteristics of the model BBL and WBT based on theory, brain sections that play a role associated with syntax, similarities, and differences, (3) explain the distinctive characteristics of both models in comparison to other models. The results of this study are: (1) the brain’s natural learning system are: (a) the nerves in each hemisphere do not work independently, (b) doing more activities can connect more brain nerves, (c) the right hemisphere controls the left side motoric sensor of the body, and vice versa; (2) the characteristics of BBL and WBT are: (a) BBL is based on the brain’s structure and function, while the model WBT is based on the instructional approach, neurolinguistic, and body language, (b) the parts of the brain that work in BBL are: cerebellum, cerebral cortex, frontal lobe, limbic system, and prefrontal cortex; whereas the parts that work WBT are: prefrontal cortex, visual cortex, motor cortex, limbic system, and amygdala, (c) the similarities between them are that they both rely on the brain’s system and they both promote gesture in learning, whereas the differences are on the view of the purposes of gestures and the learning theory that they rely on. BBL relies on cognitive theory while WBT relies on social theory; (3) the typical attribute of them compared to other models are that in BBL there are classical music and gestures in the form of easy exercises, while on the WBT model there are fast instructions and movements as instructions or code of every spoken word.</p>
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Frässle, Stefan, Ekaterina I. Lomakina, Lars Kasper, Zina M. Manjaly, Alex Leff, Klaas P. Pruessmann, Joachim M. Buhmann, and Klaas E. Stephan. "A generative model of whole-brain effective connectivity." NeuroImage 179 (October 2018): 505–29. http://dx.doi.org/10.1016/j.neuroimage.2018.05.058.
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Maniglia, Marcello, and Aaron R. Seitz. "Towards a whole brain model of Perceptual Learning." Current Opinion in Behavioral Sciences 20 (April 2018): 47–55. http://dx.doi.org/10.1016/j.cobeha.2017.10.004.
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Eagleton, Saramarie, and Anton Muller. "Development of a model for whole brain learning of physiology." Advances in Physiology Education 35, no. 4 (December 2011): 421–26. http://dx.doi.org/10.1152/advan.00007.2011.
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In this report, a model was developed for whole brain learning based on Curry's onion model. Curry described the effect of personality traits as the inner layer of learning, information-processing styles as the middle layer of learning, and environmental and instructional preferences as the outer layer of learning. The model that was developed elaborates on these layers by relating the personality traits central to learning to the different quadrants of brain preference, as described by Neethling's brain profile, as the inner layer of the onion. This layer is encircled by the learning styles that describe different information-processing preferences for each brain quadrant. For the middle layer, the different stages of Kolb's learning cycle are classified into the four brain quadrants associated with the different brain processing strategies within the information processing circle. Each of the stages of Kolb's learning cycle is also associated with a specific cognitive learning strategy. These two inner circles are enclosed by the circle representing the role of the environment and instruction on learning. It relates environmental factors that affect learning and distinguishes between face-to-face and technology-assisted learning. This model informs on the design of instructional interventions for physiology to encourage whole brain learning.
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Deshpande, Prasad, and Suhas Baxi. "ST‐IM model: a whole brain approach to implementation." Industrial and Commercial Training 43, no. 4 (June 14, 2011): 228–38. http://dx.doi.org/10.1108/00197851111137843.
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Røge, Rasmus E., Kristoffer H. Madsen, Mikkel N. Schmidt, and Morten Mørup. "Infinite von Mises–Fisher Mixture Modeling of Whole Brain fMRI Data." Neural Computation 29, no. 10 (October 2017): 2712–41. http://dx.doi.org/10.1162/neco_a_01000.
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Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises–Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling. Comparing the vMF and gaussian mixture models on synthetic data, we demonstrate that the vMF model has a slight advantage inferring the true underlying clustering when compared to gaussian-based models on data generated from both a mixture of vMFs and a mixture of gaussians subsequently normalized. Thus, when performing model selection, the two models are not in agreement. Analyzing multisubject whole brain resting-state fMRI data from healthy adult subjects, we find that the vMF mixture model is considerably more reliable than the gaussian mixture model when comparing solutions across models trained on different groups of subjects, and again we find that the two models disagree on the optimal number of components. The analysis indicates that the fMRI data support more than a thousand clusters, and we confirm this is not a result of overfitting by demonstrating better prediction on data from held-out subjects. Our results highlight the utility of using directional statistics to model standardized fMRI data and demonstrate that whole brain segmentation of fMRI data requires a very large number of functional units in order to adequately account for the discernible statistical patterns in the data.
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TEZCAN, GAMZE. "4MAT Öğretim Modeli, Bütünsel Beyin Modeli’nin Fen Dersi Öz Yeterlik Algısına Etkisi." KIRŞEHİR EĞİTİM FAKÜLTESİ DERGİSİ 18, no. 3 (December 30, 2017): 193–214. http://dx.doi.org/10.29299/kefad.2017.18.3.011.
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Coronel-Oliveros, Carlos, Rodrigo Cofré, and Patricio Orio. "Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models." PLOS Computational Biology 17, no. 2 (February 18, 2021): e1008737. http://dx.doi.org/10.1371/journal.pcbi.1008737.
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Segregation and integration are two fundamental principles of brain structural and functional organization. Neuroimaging studies have shown that the brain transits between different functionally segregated and integrated states, and neuromodulatory systems have been proposed as key to facilitate these transitions. Although whole-brain computational models have reproduced this neuromodulatory effect, the role of local inhibitory circuits and their cholinergic modulation has not been studied. In this article, we consider a Jansen & Rit whole-brain model in a network interconnected using a human connectome, and study the influence of the cholinergic and noradrenergic neuromodulatory systems on the segregation/integration balance. In our model, we introduce a local inhibitory feedback as a plausible biophysical mechanism that enables the integration of whole-brain activity, and that interacts with the other neuromodulatory influences to facilitate the transition between different functional segregation/integration regimes in the brain.
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Wood, Tobias C., Camilla Simmons, Samuel A. Hurley, Anthony C. Vernon, Joel Torres, Flavio Dell’Acqua, Steve C. R. Williams, and Diana Cash. "Whole-brain ex-vivo quantitative MRI of the cuprizone mouse model." PeerJ 4 (November 1, 2016): e2632. http://dx.doi.org/10.7717/peerj.2632.
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Myelin is a critical component of the nervous system and a major contributor to contrast in Magnetic Resonance (MR) images. However, the precise contribution of myelination to multiple MR modalities is still under debate. The cuprizone mouse is a well-established model of demyelination that has been used in several MR studies, but these have often imaged only a single slice and analysed a small region of interest in the corpus callosum. We imaged and analyzed the whole brain of the cuprizone mouse ex-vivo using high-resolution quantitative MR methods (multi-component relaxometry, Diffusion Tensor Imaging (DTI) and morphometry) and found changes in multiple regions, including the corpus callosum, cerebellum, thalamus and hippocampus. The presence of inflammation, confirmed with histology, presents difficulties in isolating the sensitivity and specificity of these MR methods to demyelination using this model.
Dissertations / Theses on the topic "Whole brain model"
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Clark, Heather Winona Schulte. "Effect of Whole Brain Teaching on Student Self-Concept." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2146.
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Sufficient research exists indicating that the brain mechanisms involved with use of whole brain teaching (WBT) techniques will likely lead to improved academic achievement and that academic self-concept (ASC) is both a cause and consequence of academic achievement. However, it is not known if there is a relationship between WBT and ASC. Given the benefits derived from positive ASC, it becomes important to assess WBT as a predictor variable of positive ASC. The purpose of this quantitative study was to examine the relationship between different levels of exposure to WBT techniques and the mean difference in ASC, as measured by the general-school, mathematics, and reading subscores on the Self Description Questionnaire I, between treatment conditions. Self-concept theory as posited by Shavelson et al. and the Marsh/Shavelson revision, the skill development approach to self-concept enhancement, and the reciprocal effect model provide the theoretical foundations of this dissertation. A one-way multivariate analysis of variance (MANOVA) was used to determine if the mean ASC scores differed among 191 second and third grade students exposed to three levels of the WBT factor. Results of the three-group MANOVA failed to support use of WBT techniques to improve ASC. Reconfiguration of the quasi-independent variable into two groups revealed that general-school ASC scores were significantly lower in the group exposed to limited to no WBT techniques. Assessing students at risk for educational problems may reveal more convincing evidence for WBT as an effective ASC intervention. The implications for social change include encouraging WBT practitioners to make more empirically sound claims and decisions regarding their practice, thereby allowing students an educational experience grounded in scientific findings, rather than subjective assumptions.
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Goode, Heather A. "Using the Herrmann whole brain® model for mentoring academic staff." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/45935.
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My research provides an account of evaluating my mentoring practice using an Action Research design complemented by a mixed methods approach and the Hermann Whole Brain® Model (Herrmann, 1995). I explored how I can transform my mentoring practice using the principles of Whole Brain® thinking and how I can contribute to enriching the professional development of academic staff. My research has proceeded from an innovative idea and existing practice as an asset-based approach (Du Toit, 2009).
By utilising an Action Research design my research articulates the construction of my understanding of mentoring of other academic staff in their professional practice. I followed a constructivist approach as used by Piaget (1952, 1970) that is considered an appropriate epistemological underpinning of Action Research. My research design shows thinking style flexibility as an action researcher in that I have drawn on each quadrant of the Whole Brain® Theory as developed by Herrmann (1995). This enabled me to construct meaning with my peer mentees through the assessing of practice-based evidence, engagement and reflection. As my goal in mentoring is to assist in developing independent reflexive practitioners, I have chosen to use the constructs contribute to and catalyse to express my awareness that responsibility for professional development remains with the individual and that a mentor is not the only source of professional development in the context of a Private Higher Education Institution.
I have found that my peer mentees have differing thinking style preferences and varying professional experiences that required of me to engage with each in distinct ways to support the development of their professional practices. I position Whole Brain® Mentoring as a practice of mentoring that utilises multiple strategies for professional learning, both formal and non-formal, to engage the thinking preferences and disinclinations of mentees to catalyse the professional development of both the mentor and mentees. Many of my peer mentees perceive themselves as mentors, both of students and, in some cases, of other academic staff (our peers) as well. There is evidence that I utilise multiple strategies to facilitate professional learning and contribute to the professional development of peer mentees and that they have contributed to mine. My research provides evidence that I have become a more reflective practitioner, able to transform my Whole Brain® Mentoring Practice.Dissertation (MEd)--University of Pretoria, 2014.
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Campbell, Velma-Jean. "The implications of Ned Herrmann’s whole-brain model for violin teaching : a case study." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/1889.
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Thesis (MMus (Music))--University of Stellenbosch, 2008.This study is concerned with determining whether the application of Ned Herrmann’s “whole-brain” model would impact violin teaching in any way. Our educational system places great importance on what has become known as the left-brain modes, that is, reading, writing and arithmetic, to the neglect of the socalled right brain’s cognitive abilities, such as, music, art, intuition and dance. Wellintentioned, yet ill-informed teachers teach learners in ways that make learning difficult or impossible, as they are unaware of how to determine and use the preferred learning style of each learner. When a learner’s learning style is not matched with the method of instruction, the learner’s discomfort level may be so great that it not only interferes with the learning process but it could also ultimately prevent learning from taking place. The researcher, therefore, set out to determine whether the use of whole-brain development would lead to any significant changes in the learning process. For a period of two school terms, case studies using action research were conducted on five of the learners that received instruction from the researcher. The research participants were so chosen as to make the experimental group as homogenous as possible. Data was collected qualitatively by means of diaries and was presented descriptively. Every week the learners received a printed copy of the homework exercises. They recorded their feedback weekly, in their diaries. The researcher, as the teacher of the learners, made weekly observations during lessons. During this research the process of triangulation was used. This process added validity to the study as information about specific aspects was gained from three different perspectives, namely, that of the learners, the teacher and the learners’ accompanists. The accompanists gave their feedback before the start of the research and again at the end. After applying Herrmann’s model for two terms, the following became apparent: • The learners practised more, were more motivated and there was a general improvement in their attitude. • The learners felt that having received a printed copy of the exercises, a whole brain exercise in itself, had helped them to know what and how to practise. • There was a significant change in the playing of the majority of learners (three of the five). • The learners, where significant changes were not apparent in their playing, indicated that their understanding of their practising methods and playing had increased. • The learners felt that they had benefited from the experiment as they all indicated that they would like future lessons to be conducted in the same manner. In view of the positive outcome of the research, and given that this was a pilot study, the researcher suggests that similar studies using larger numbers of learners and involving a longer period of time, be conducted. The inclusion of a control group would also render the findings more conclusive. The researcher also suggests that violin teachers become knowledgeable about learning styles and whole-brain learning if they wish to reach all learners and enable them to achieve their potential.
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Curtis, James. "Whole Brain Isotropic Arterial Spin Labeling Magnetic Resonance Imaging in a transgenic mouse model of Alzheimer's Disease." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32516.
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This thesis presents the design, implementation, and validation of a novel, arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) pulse sequence to generate three-dimensional quantitative maps of cerebral blood flow (CBF) in mice at 7 Tesla with an isotropic 281μm resolution. ASL and anatomical scans were registered to a common template using an automated non-linear registration pipeline to allow for voxel-wise inter-scan and inter-subject comparisons of CBF. The technique was applied to the study of a transgenic mouse model of Alzheimer's Disease (AD) which demonstrates many of the characteristic features of cerebrovascular dysfunction present in AD. The technique resolved regions of significant difference between transgenic and wild-type mouse populations using voxel-wise- and region-of-interest-based analyses. These findings are the first to demonstrate the utility of perfusion MRI for population-based analysis of cerebrovascular pathophysiology in transgenic AD mice.Cette thèse présente la conception et la validation d'un nouveau séquence d'acquisition d'imagerie par resonance magnetique (IRM) pour la marquage des spins des arteres (ASL) pour créer des cartes parametrique en trois-dimensions de debit de sanguin cérébral (CBF) dans les souris à 7 Tesla. avec un résolution isotrope de 281 μm. Les volumes d'IRM anatomique et ASL ont été enregistrées avec un procedure non linéaire pour effectuer des comparaisons de CBF par-voxel entre les scans seriale et entre les animaux. La technique a été appliquée à l'étude d'un modèle de souris transgénique de la maladie d'Alzheimer (MA), qui démontre beaucoup de traits caractéristiques de dysfonctionnement cérébral qui sont présents dans la maladie d'Alzheimer. La technique résolu régions de différence significative entre les populations transgéniques et de type sauvage par les methodes d'analyse par-voxel et par-regions-d'intérêt. Ces résultats sont les premiers à démontrer l'utilité de l'IRM de perfusion au niveau de la population sur l'analyse de physiopathologie vasculaire cérébral dans les souris transgéniques MA.
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Pallarés, Picazo Vicente. "Individual traits versus invariances of cognitive functions: a model-based study of brain connectivity." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666806.
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Es conocido en la literatura de neuroimagen que las redes cerebrales funcionales reflejan rasgos personales. Estas características individuales, podrían interferir al caracterizar la cognición entendida como la manera en que se coordinan las redes para realizar una tarea, como mantener la atención, recordar, o procesar información visual. Cómo estos aspectos individuales coexisten con mecanismos generales es, por tanto, una pregunta clave en investigación sobre conectividad cerebral. Este trabajo estudia la relación entre marcadores de conectividad específicos tanto de sujetos, como de tareas. Se centra en dos escalas temporales distintas: la variabilidad entre sesiones, y las fluctuaciones rápidas producidas durante una sesión de adquisición. Utilizamos técnicas de machine learning para separar cuantitativamente las contribuciones de información del sujeto y del estado cognitivo a la conectividad. La metodología presentada nos permite extraer aquellas redes representativas de ambas dimensiones, así como profundizar en su evolución, sugiriendo las escalas temporales relevantes en la cognición.És conegut en la literatura de neuroimatge que les xarxes cerebrals funcionals reflecteixen trets personals. Aquestes característiques individuals podrien interferir en caracteritzar la cognició entesa com la manera en què les xarxes es coordinen per realitzar una tasca, com mantenir l'atenció, recordar o processar informació visual. Cóm aquests aspectes individuals coexisteixen amb mecanismes generals, és, per tant, una pregunta clau en recerca sobre connectivitat cerebral. Aquest treball estudia la relació entre marcadors de connectivitat específics tant de subjectes, com de tasques. Se centra en dues escales temporals: la variabilitat entre sessions, i les fluctuacions ràpides produïdes durant una sessió d'adquisició. Utilitzem tècniques de machine learning per separar quantitativament les contribucions d'informació del subjecte i de l'estat cognitiu a la connectivitat. La metodologia presentada ens permet extreure aquelles xarxes representatives d'ambdues dimensions, així com aprofundir en la seva evolució, suggerint les escales temporals rellevants en la cognició.
There is consistent evidence in the neuroimaging literature that functional brain networks reflect personal traits. Individual specificity may interfere with the characterization of cognition, in terms of coordination of brain networks to perform a task, such as sustained attention, memory retrieval or visual information processing. How individual traits coexist with invariant mechanisms is, therefore, a key question in brain connectivity research. This work aims to examine the relationship between subject- and task-specific connectivity signatures. It focuses on two different timescales: day-to-day variability and faster fluctuations exhibited within a scanning session. We adopt a machine learning approach to quantitatively disentangle the contribution of subject information and cognitive state to the connectivity patterns. The proposed methodology allows us to extract the specific brain networks that support each of the two dimensions, as well as to delve into their changes over time, suggesting the relevant timescales for cognition.
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Santiago, Jessica de. "Extracting informative spatio-temporal features from fMRI dynamics : a model-based characterization of timescales." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/671346.
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In neuropsychiatry, the development of brain imaging and dedicated data analysis for personalized medicine promises to predict both the evolution of diseases and responses of treatments. The ability to estimate the time course of the disease is the first step to understand the response to potential treatments, which implies the development of methods able to capture subject-specific features in addition to the discrimination between pathological conditions. However, methods that effectively characterize the neuronal activity at the whole-brain level are still lacking, and many efforts are currently made in the fields of clinical research and neuroscience to fill this gap. The above is particularly problematic to interpret functional Magnetic Resonance Imaging (fMRI) data, which are indirectly coupled with neuronal activity because of hemodynamics, yielding much slower signals than neuronal activity. We propose a multiscale method that combines a computational whole-brain model with machine learning to solve this issue. In our approach, the model relates the neuronal activity and the fMRI signals in a mechanistic fashion, allowing for access to neuronal activity down to millisecond precision. Specifically, we use a novel methodology that allows the extraction of space-time motifs at different timescales through binned time windows. Then, we use machine learning to study which range of timescales in the modeled neuronal activity is most informative to separate the brain's dynamics during rest, distinguishing subjects, tasks, and neuropsychiatric conditions. Our multiscale computational approach is a further step to study the multiple timescales of brain dynamics and predict the dynamical interactions between brain regions. Overall, this method raises outlooks to detect biomarkers and predict responses of treatments.En neuropsiquiatría, el desarrollo de imágenes cerebrales y el análisis de datos dedicados a la medicina personalizada prometen predecir tanto la evolución de las enfermedades como las respuestas a los tratamientos. La capacidad de estimar el curso temporal de la enfermedad es el primer paso para comprender la respuesta a posibles tratamientos, lo que implica el desarrollo de métodos capaces de capturar características específicas del sujeto, además de la discriminación entre condiciones patológicas. Sin embargo, todavía faltan métodos que caractericen eficazmente la actividad neuronal a nivel de todo el cerebro, y actualmente se están haciendo muchos esfuerzos en los campos de la investigación clínica y la neurociencia. Lo anterior es particularmente problemático para interpretar los datos funcionales de las imágenes de resonancia magnética (fMRI por sus siglas en inglés), que están acoplados indirectamente con la actividad neuronal debido a la hemodinámica, lo que produce señales mucho más lentas que la actividad neuronal. En este trabajo, proponemos un método multiescala que combina un modelo computacional de cerebro completo con aprendizaje automático para resolver este problema. En nuestro enfoque, el modelo relaciona la actividad neuronal y las señales de resonancia magnética funcional de manera mecanicista, lo que permite el acceso a la actividad neuronal con una precisión de milisegundos. Específicamente, utilizamos una nueva metodología que permite la extracción de patrones espacio-temporales en diferentes escalas temporales a través de ventanas de tiempo. Después, usamos aprendizaje automático para estudiar qué rango de escalas de tiempo en la actividad neuronal modelada es más informativo, para separar la dinámica del cerebro durante el descanso, distinguiendo sujetos, tareas y condiciones neuropsiquiátricas. Nuestro enfoque computacional multiescala es un paso más para estudiar las múltiples escalas de tiempo de la dinámica del cerebro y predecir las interacciones dinámicas entre las regiones del cerebro. En general, este método aumenta las perspectivas para detectar biomarcadores y predecir la respuesta de tratamientos.
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Cloete, Dina J. "‘n Ondersoek na die fasilitering van verskillende leerstyle en meervoudige intelligensies tydens koöperatiewe leer en groepaktiwiteite in hoër onderwys (Afrikaans)." Diss., University of Pretoria, 2004. http://hdl.handle.net/2263/26729.
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Education programmes of the previous education departments in South Africa were based mainly on individual achievement and competition. In the new Outcomes Based programmes there is a shift to interaction, shared knowledge and the mastering of a variety of interaction skills. Ned Herrmann’s (1996) theory about the complexity of the human brain, the MI-theory of Gardner (2000b) and Sternberg’s (1997), Litzinger and Osif’s (1993) view of thinking and learning styles form the theoretical framework of this study. This research focuses in the first place on the way that students experience group work and co-operative learning, secondlyon what facilitators know or do not know about MI (multiple intelligences), co-operative learning and groupwork and how they implement the principles in their planning and during contact sessions. The research problem is: To what extend do facilitators in higher education make provision for the learning style preferences and other individual differences of learners during contact sessions facilitated by means of group work and/or co-operative learning? How do the learners respond to these approaches? This mixed methods research is done through observation, semi-structured interviews, a diagnostic questionnaire and a content analysis of study documentation. Study documentation was analysed to determine to what extend facilitators provided for learners’ learning style preferences and other individual differences. The behaviour of teacher training students was recorded over a period of three months and in different group settings. Participants completed a diagnostic questionnaire and the data obtained were compared with their behaviour to determine if there is any correlation between certain learning styles and behaviour patterns. I found that although the questions and assignments leave room for the learners’ differences, there is no reference to MI and learning styles in the study documentation. The theory of co-operative learning and group work is addressed in die study documentation. Learners with high interpersonal intelligence scores participated spontaneously in group activities and co-operative learning. Contrary to this the intrapersonal learners responded in a negative way. Personality clashes, conflict, prejudice, etc. were resolved to a great extend by changing group combinations. Nevertheless, the intrapersonal learner maintains a negative response towards group activities and co-operative learning. There was little or no significant negative behavior observed from learners with high scores in the other seven intelligences according to Gardner (2000b). The outcome of the first contact session led me to the conclusion that co-operative learning and group work are valuable facilitating strategies on the basis of shared sources, knowledge and progress in learning activities. Although the facilitators used these facilitating strategies, I could find no evidence that they took learners’ preferences and differences into account during contact sessions. However, in the interviews it became clear that they are enthusiastic to learn more about MI and learning styles and ways to plan and facilitate according to that knowledge. Facilitators need to know the learners in order to accommodate their diversities in group activities and co-operative learning. Learners, on the other hand, need to know their own abilities, intelligence and learning style preferences. The teaching profession is complex and continuous renewal and amelioration are essential to ensure dynamic and effective learning.Dissertation (MEd (Curriculum Studies))--University of Pretoria, 2006.
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Robinson, Emma Claire. "Characterising population variability in brain structure through models of whole-brain structural connectivity." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/5875.
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Models of whole-brain connectivity are valuable for understanding neurological function. This thesis seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically acquired diffusion data. We propose new approaches for studying these models. The aim is to develop techniques which can take models of brain connectivity and use them to identify biomarkers or phenotypes of disease. The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections are traced between 77 regions of interest, automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data. These are compared in subsequent studies. To date, most whole-brain connectivity studies have characterised population differences using graph theory techniques. However these can be limited in their ability to pinpoint the locations of differences in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include a spectral clustering approach for comparing population differences in the clustering properties of weighted brain networks. In addition, machine learning approaches are suggested for the first time. These are particularly advantageous as they allow classification of subjects and extraction of features which best represent the differences between groups. One limitation of the proposed approach is that errors propagate from segmentation and registration steps prior to tractography. This can cumulate in the assignment of false positive connections, where the contribution of these factors may vary across populations, causing the appearance of population differences where there are none. The final contribution of this thesis is therefore to develop a common co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject into a single probabilistic model of diffusion for the population. This allows tractography to be performed only once, ensuring that there is one model of connectivity. Cross-subject differences can then be identified by mapping individual subjects’ anisotropy data to this model. The approach is used to compare populations separated by age and gender.
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Vandenberghe, Michel. "3D whole-brain quantitative histopathology : methodology and applications in mouse models of Alzheimer's disease." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066411/document.
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L’histologie est la méthode de choix pour l’étude ex vivo de la distribution spatiale des molécules qui composent les organes. En particulier, l’histologie permet de mettre en évidence les marqueurs neuropathologiques de la maladie d’Alzheimer ce qui en fait un outil incontournable pour étudier la physiopathologie de la maladie et pour évaluer l’efficacité de candidats médicaments. Classiquement, l’analyse de données histologiques implique de lourdes interventions manuelles, et de ce fait, est souvent limitée à l’analyse d’un nombre restreint de coupe histologiques et à quelques régions d’intérêts. Dans ce travail de thèse, nous proposons une méthode automatique pour l’analyse quantitative de marqueurs histopathologiques en trois dimensions dans le cerveau entier de rongeurs. Les images histologiques deux-dimensionnelles sont d’abord reconstruites en trois dimensions en utilisant l’imagerie photographique de bloc comme référence géométrique et les marqueurs d’intérêts sont segmentés par apprentissage automatique. Deux approches sont proposées pour détecter des différences entre groupes d’animaux: la première est basée sur l’utilisation d’une ontologie anatomique de cerveau qui permet détecter des différences à l’échelle de structures entières et la deuxième approche est basée sur la comparaison voxel-à-voxel afin de détecter des différences locales sans a priori spatial. Cette méthode a été appliquée dans plusieurs études chez des souris modèles de déposition amyloïde afin d’en démontrer l’utilisabilitéHistology is the gold standard to study the spatial distribution of the molecular building blocks of organs. In humans and in animal models of disease, histology is widely used to highlight neuropathological markers on brain tissue sections. This makes it particularly useful to investigate the pathophysiology of neurodegenerative diseases such as Alzheimer’s disease and to evaluate drug candidates. However, due to tedious manual interventions, quantification of histopathological markers is classically performed on a few tissue sections, thus restricting measurements to limited portions of the brain. Quantitative methods are lacking for whole-brain analysis of cellular and pathological markers. In this work, we propose an automated and scalable method to thoroughly quantify and analyze histopathological markers in 3D in rodent whole brains. Histology images are reconstructed in 3D using block-face photography as a spatial reference and the markers of interest are segmented via supervised machine learning. Two complimentary approaches are proposed to detect differences in histopathological marker load between groups of animals: an ontology-based approach is used to infer difference at the level of brain regions and a voxel-wise approach is used to detect local differences without spatial a priori. Several applications in mouse models of A-beta deposition are described to illustrate 3D histopathology usability to characterize animal models of brain diseases, to evaluate the effect of experimental interventions, to anatomically correlate cellular and pathological markers throughout the entire brain and to validate in vivo imaging techniques
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Wang, Xue. "An Integrated Multi-modal Registration Technique for Medical Imaging." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3512.
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Registration of medical imaging is essential for aligning in time and space different modalities and hence consolidating their strengths for enhanced diagnosis and for the effective planning of treatment or therapeutic interventions. The primary objective of this study is to develop an integrated registration method that is effective for registering both brain and whole-body images. We seek in the proposed method to combine in one setting the excellent registration results that FMRIB Software Library (FSL) produces with brain images and the excellent results of Statistical Parametric Mapping (SPM) when registering whole-body images. To assess attainment of these objectives, the following registration tasks were performed: (1) FDG_CT with FLT_CT images, (2) pre-operation MRI with intra-operation CT images, (3) brain only MRI with corresponding PET images, and (4) MRI T1 with T2, T1 with FLAIR, and T1 with GE images. Then, the results of the proposed method will be compared to those obtained using existing state-of-the-art registration methods such as SPM and FSL.
Initially, three slices were chosen from the reference image, and the normalized mutual information (NMI) was calculated between each of them for every slice in the moving image. The three pairs with the highest NMI values were chosen. The wavelet decomposition method is applied to minimize the computational requirements. An initial search applying a genetic algorithm is conducted on the three pairs to obtain three sets of registration parameters. The Powell method is applied to reference and moving images to validate the three sets of registration parameters. A linear interpolation method is then used to obtain the registration parameters for all remaining slices. Finally, the aligned registered image with the reference image were displayed to show the different performances of the 3 methods, namely the proposed method, SPM and FSL by gauging the average NMI values obtained in the registration results. Visual observations are also provided in support of these NMI values. For comparative purposes, tests using different multi-modal imaging platforms are performed.
Books on the topic "Whole brain model"
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Mildred, Haipt, ed. Thinking with the whole brain: An integrative teaching/learning model (K-8). Washington, D.C: National Education Association, 1986.
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Guillery, Ray. Starting to study the brain. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198806738.003.0005.
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Chapter 5 takes a look at some of the basic assumptions, questions, rules, laws, and dogmas that we encounter, use, or ignore when we study the brain. As a student, I was taught to study the brain as a functional part of a whole animal, and the nervous system as a part of biology. I was introduced to the ‘neuron doctrine’, which was applied specifically to studies of the brain. I became aware of its strengths and gradually learnt its weaknesses. I learnt the difference between descriptive science and science as a hypothetico-deductive system, and began to see myself as a descriptive scientist looking for areas of the unknown still needing to be explored. I have used this chapter to illustrate, that for the most complex parts of the brain, the rules that are useful where we know and understand many details are often irrelevant or confused where, as for the thalamus and cortex, we know only a few of the relevant details. This book has been written about areas where we often lack such details, where crucial questions have not been asked because they did not arise under the standard perception-to-action model. New questions become relevant under the interactive view, providing opportunities for many new, needed descriptive studies.
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Verschure, Paul F. M. J. The architecture of mind and brain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0035.
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The components of a Living Machine must be integrated into a functioning whole, which requires a detailed understanding of the architecture of living machines. This chapter starts with a conceptual and historical analysis which from Plato brings us to nineteenth-century neuroscience and early concepts of the layered structure of nervous systems. These concepts were further captured in the cognitive behaviorism of Tolman and came to full fruition in the cognitive revolution of the second half of the twentieth century. Verschure subsequently describes the most relevant proposals of cognitive architectures followed by an overview of the few proposals stemming from modern neuroscience on the architecture of the brain. Subsequently, we will look at contemporary contenders that mediate between cognitive and brain architecture. An important challenge to any model of cognitive architectures is how to benchmark it. Verschure proposes the Unified Theories of Embodied Minds (UTEM) benchmark which advances from Newell’s classic Unified Theories of Cognition benchmark.
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Thompson, Evan. Looping Effects and the Cognitive Science of Mindfulness Meditation. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190495794.003.0003.
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Cognitive neuroscience tends to conceptualize mindfulness meditation as inner observation of a private mental realm of thoughts, feelings, and body sensations, and tries to model mindfulness as instantiated in neural networks visible through brain imaging tools such as EEG and fMRI. This approach confuses the biological conditions for mindfulness with mindfulness itself, which, as classically described, consists in the integrated exercise of a whole host of cognitive and bodily skills in situated and ethically directed action. From an enactive perspective, mindfulness depends on internalized social cognition and is a mode of skillful, embodied cognition that depends directly not only on the brain, but also on the rest of the body and the physical, social, and cultural environment.
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Brennan, Brian P., and Scott L. Rauch. Functional Neuroimaging Studies in Obsessive-Compulsive Disorder: Overview and Synthesis. Edited by Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0021.
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Studies using functional neuroimaging have played a critical role in the current understanding of the neurobiology of obsessive-compulsive disorder (OCD). Early studies using positron emission tomography (PET) identified a core cortico-striatal-thalamo-cortical circuit that is dysfunctional in OCD. Subsequent studies using behavioral paradigms in conjunction with functional magnetic resonance imaging (fMRI) have provided additional information about the neural substrates underlying specific psychological processes relevant to OCD. More recently, studies utilizing resting state fMRI have identified abnormal functional connectivity within intrinsic brain networks including the default mode and frontoparietal networks in OCD patients. Although these studies, as a whole, clearly substantiate the model of cortico-striatal-thalamo-cortical circuit dysfunction in OCD and support the continued investigation of neuromodulatory treatments targeting these brain regions, there is also growing evidence that brain regions outside this core circuit, particularly frontoparietal regions involved in cognitive control processes, may also play a significant role in the pathophysiology of OCD.
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Ayala, Francisco J., and Camilo J. Cela-Conde. Neanderthals and modern humans. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198739906.003.0011.
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This chapter deals with the similarities and differences between Homo neanderthalensis and Homo sapiens, by considering genetic, brain, and cognitive evidence. The genetic differentiation emerges from fossil genetic evidence obtained first from mtDNA and later from nuclear DNA. With high throughput whole genome sequencing, sequences have been obtained from the Denisova Cave (Siberia) fossils. Nuclear DNA of a third species (“Denisovans”) has been obtained from the same cave and used to define the phylogenetic relationships among the three species during the Upper Palaeolithic. Archaeological comparisons make it possible to advance a four-mode model of the evolution of symbolism. Neanderthals and modern humans would share a “modern mind” as defined up to Symbolic Mode 3. Whether the Neanderthals reached symbolic Mode 4 remains unsettled.
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Alder, Catherine A., Mary Guerriero Austrom, Michael A. LaMantia, and Malaz A. Boustani. Aging Brain Care. Edited by Robert E. Feinstein, Joseph V. Connelly, and Marilyn S. Feinstein. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190276201.003.0008.
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While fragmented care is a problem across the entire health care delivery system, it is especially problematic for vulnerable older adults with dementia and late-life depression. Most older adults have multiple chronic conditions. Cognitive impairment and mood disorders complicate the management of these comorbid conditions by interfering with the patient’s ability to monitor and report symptoms and comply with the care plan. To reduce fragmentation and promote integrated care, each medical provider must adopt a more holistic view of health care, recognizing the importance of communication and collaboration among all providers and the potential impact of any one action on the patient’s overall health. The Aging Brain Care (ABC) model provides a structure for integrating evidence-based interventions for dementia and depression into the primary care environment. By extending the delivery of care beyond the clinic, ABC offers patient-centered services aimed at coordinating care across multiple providers, settings, and community resources.
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Rubia, Katya. ADHD brain function. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198739258.003.0007.
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ADHD patients appear to have complex multisystem impairments in several cognitive-domain dissociated inferior, dorsolateral, and medial fronto-striato-parietal and frontocerebellar neural networks during inhibition, attention, working memory, and timing functions. There is emerging evidence for abnormalities in motivation and affect control regions, most prominently in ventral striatum, but also orbital/ventromedial frontolimbic areas. Furthermore, there is an immature interrelationship between hypoengaged task-positive cognitive control networks and a poorly ‘switched off’ default mode network, both of which impact performance. Stimulant medication enhances the activation of inferior frontostriatal systems, while atomoxetine appears to have more pronounced effects on the dorsal attention network. More studies are needed to understand the neurofunctional correlates of the effects of age, gender, ADHD subtypes, and comorbidities with other psychiatric conditions. The use of pattern recognition analyses applied to imaging to make individual diagnostic or prognostic predictions are promising and will be the challenge over the next decade.
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Hasker, William. Incarnation: The Avatar Model. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198806967.003.0006.
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This chapter presents a model of the Incarnation developed on the basis of the Na’vi avatars of the science fiction movie Avatar. The model does not address the metaphysics of the Incarnation; rather, its main concern is with the consciousness of Jesus Christ, the incarnate Son. “One-sphere models,” in which the Son while incarnate has a single sphere of consciousness, are examined and found to be unsatisfactory. The avatar model is a “two-sphere model,” in which there exist distinct spheres of consciousness for the divine nature and the human nature, similar to the “two minds” view proposed by Thomas Morris. It is argued that this does not amount to Nestorianism. The possibility of a single person with multiple spheres of consciousness is defended by comparison with the psychological “split-brain” and “multiple personality” phenomena; this way of understanding those phenomena is defended against a contrary view expounded by Tim Bayne.
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Carter, Adrian, and Wayne Hall. Looking to the future: Clinical and policy implications of the brain disease model of addiction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198786832.003.0025.
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The increasing recognition of addiction as a brain disease promises to significantly improve clinical treatment, reduce the stigma and discrimination aimed at people with an addiction, increase treatment funding and access, and discourage the use of punitive responses. This chapter argues that the brain disease model of addiction is not supported by the evidence sought in laboratories worldwide, and has failed to provide the clinical, social, and public policy benefits espoused by its proponents. Treating addiction solely as a brain disease may in fact reduce treatment-seeking, increase stigma, and focus attention on the medical treatment of addiction at the expense of more broadly effective public health policies that reduce the harms of drug use. The neuroscience research of addiction needs to be framed in a way that both accurately reflects the impact of drug use on the brain and communicated in a way that realizes the clinical and social benefits while eschewing avoidable harms.
Book chapters on the topic "Whole brain model"
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Kang, Hakmook, Xue Yang, Frederick W. Bryan, Christina M. Tripp, and Bennett A. Landman. "Whole Brain Functional Connectivity Using Multi-scale Spatio-Spectral Random Effects Model." In Multimodal Brain Image Analysis, 170–79. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02126-3_17.
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Kane, P. J., P. Modha, R. D. Strachan, A. D. Mendelow, S. Cook, and I. R. Chambers. "The Effect of Immunosuppression with Whole Body and Regional Irradiation on the Development of Cerebral Oedema in a Rat Model of Intracerebral Haemorrhage." In Brain Edema VIII, 52–54. Vienna: Springer Vienna, 1990. http://dx.doi.org/10.1007/978-3-7091-9115-6_18.
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Riel, Stefanie, Mohammad Bashiri, Werner Hemmert, and Siwei Bai. "Computational Models of Brain Stimulation with Tractography Analysis." In Brain and Human Body Modeling 2020, 101–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_6.
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AbstractComputational human head models have been used in studies of brain stimulation. These models have been able to provide useful information that can’t be acquired or difficult to acquire from experimental or imaging studies. However, most of these models are purely volume conductor models that overlooked the electric excitability of axons in the white matter of the brain. We hereby combined a finite element (FE) model of electroconvulsive therapy (ECT) with a whole-brain tractography analysis as well as the cable theory of neuronal excitation. We have reconstructed a whole-brain tractogram with 2000 neural fibres from diffusion-weighted magnetic resonance scans and extracted the information on electrical potential from the FE ECT model of the same head. Two different electrode placements and three different white matter conductivity settings were simulated and compared. We calculated the electric field and second spatial derivatives of the electrical potential along the fibre direction, which describes the activating function for homogenous axons, and investigated sensitive regions of white matter activation. Models with anisotropic white matter conductivity yielded the most distinctive electric field and activating function distribution. Activation was most likely to appear in regions between the electrodes where the electric potential gradient is most pronounced.
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Tsukada, Hiromichi, Hiroaki Hamada, Ken Nakae, Shin Ishii, Junichi Hata, Hideyuki Okano, and Kenji Doya. "Analysis of Structure-Function Relationship Using a Whole-Brain Dynamic Model Based on MRI Images of the Common Marmoset." In Advances in Cognitive Neurodynamics (VI), 97–102. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8854-4_12.
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Siddiqui, Faizan, Thomas Höllt, and Anna Vilanova. "Uncertainty in the DTI Visualization Pipeline." In Mathematics and Visualization, 125–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_6.
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AbstractDiffusion-Weighted Magnetic Resonance Imaging (DWI) enables the in-vivo visualization of fibrous tissues such as white matter in the brain. Diffusion-Tensor Imaging (DTI) specifically models the DWI diffusion measurements as a second order-tensor. The processing pipeline to visualize this data, from image acquisition to the final rendering, is rather complex. It involves a considerable amount of measurements, parameters and model assumptions, all of which generate uncertainties in the final result which typically are not shown to the analyst in the visualization. In recent years, there has been a considerable amount of work on the visualization of uncertainty in DWI, and specifically DTI. In this chapter, we primarily focus on DTI given its simplicity and applicability, however, several aspects presented are valid for DWI as a whole. We explore the various sources of uncertainties involved, approaches for modeling those uncertainties, and, finally, we survey different strategies to visually represent them. We also look at several related methods of uncertainty visualization that have been applied outside DTI and discuss how these techniques can be adopted to the DTI domain. We conclude our discussion with an overview of potential research directions.
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Wartman, William A. "Preprocessing General Head Models for BEM-FMM Modeling Pertinent to Brain Stimulation." In Brain and Human Body Modeling 2020, 325–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_20.
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AbstractIntroduction: Transcranial magnetic stimulation (TMS) is a major noninvasive neurostimulation method in which a coil placed near the head employs electromagnetic induction to produce electric fields and currents within the brain. To predict the actual site of stimulation, numerical simulation of the electric fields within the head using high-resolution subject-specific head models is required. A TMS modeling software toolkit has been developed based on the boundary element fast multipole method (BEM-FMM), which has several advantages over conventional finite element method (FEM) solvers.Objective: To extend the applicability of the BEM-FMM TMS simulation toolkit to head models whose meshing scheme produces a single mesh for every unique tissue instead of producing a single mesh for every unique tissue/tissue boundary.Method: The MIDA model of the IT’IS Foundation, Switzerland, comprises 115 high-resolution tissue models in the form that the BEM-FMM toolkit is modified to accept. The updated BEM-FMM toolkit is tested using this head model.Results: The BEM-FMM toolkit has been successfully modified to accept head models consisting of one unique mesh per unique tissue while still supporting its initial model format of one unique mesh per boundary between two specific tissues. Performance impacts occur in the preprocessing phase only, meaning that the charge computation method performs equally well regardless of model format.
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More, Shammi, Simon B. Eickhoff, Julian Caspers, and Kaustubh R. Patil. "Confound Removal and Normalization in Practice: A Neuroimaging Based Sex Prediction Case Study." In Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 3–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67670-4_1.
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AbstractMachine learning (ML) methods are increasingly being used to predict pathologies and biological traits using neuroimaging data. Here controlling for confounds is essential to get unbiased estimates of generalization performance and to identify the features driving predictions. However, a systematic evaluation of the advantages and disadvantages of available alternatives is lacking. This makes it difficult to compare results across studies and to build deployment quality models. Here, we evaluated two commonly used confound removal schemes–whole data confound regression (WDCR) and cross-validated confound regression (CVCR)–to understand their effectiveness and biases induced in generalization performance estimation. Additionally, we study the interaction of the confound removal schemes with Z-score normalization, a common practice in ML modelling. We applied eight combinations of confound removal schemes and normalization (pipelines) to decode sex from resting-state functional MRI (rfMRI) data while controlling for two confounds, brain size and age. We show that both schemes effectively remove linear univariate and multivariate confounding effects resulting in reduced model performance with CVCR providing better generalization estimates, i.e., closer to out-of-sample performance than WDCR. We found no effect of normalizing before or after confound removal. In the presence of dataset and confound shift, four tested confound removal procedures yielded mixed results, raising new questions. We conclude that CVCR is a better method to control for confounding effects in neuroimaging studies. We believe that our in-depth analyses shed light on choices associated with confound removal and hope that it generates more interest in this problem instrumental to numerous applications.
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Puonti, Oula, and Koen Van Leemput. "Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 9–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30858-6_2.
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Makarov, Sergey N., Jyrki Ahveninen, Matti Hämäläinen, Yoshio Okada, Gregory M. Noetscher, and Aapo Nummenmaa. "Multiscale Modeling of EEG/MEG Response of a Compact Cluster of Tightly Spaced Pyramidal Neocortical Neurons." In Brain and Human Body Modeling 2020, 195–211. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_11.
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AbstractIn this study, the boundary element fast multipole method or BEM-FMM is applied to model compact clusters of tightly spaced pyramidal neocortical neurons firing simultaneously and coupled with a high-resolution macroscopic head model. The algorithm is capable of processing a very large number of surface-based unknowns along with a virtually unlimited number of elementary microscopic current dipole sources distributed within the neuronal arbor.The realistic cluster size may be as large as 10,000 individual neurons, while the overall computation times do not exceed several minutes on a standard server. Using this approach, we attempt to establish how well the conventional lumped-dipole model used in electroencephalography/magnetoencephalography (EEG/MEG) analysis approximates a compact cluster of realistic neurons situated either in a gyrus (EEG response dominance) or in a sulcus (MEG response dominance).
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Salvador, R., M. C. Biagi, O. Puonti, M. Splittgerber, V. Moliadze, M. Siniatchkin, A. Thielscher, and G. Ruffini. "Personalization of Multi-electrode Setups in tCS/tES: Methods and Advantages." In Brain and Human Body Modeling 2020, 119–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_7.
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AbstractTranscranial current stimulation (tCS or tES) protocols yield results that are highly variable across individuals. Part of this variability results from differences in the electric field (E-field) induced in subjects’ brains during stimulation. The E-field determines how neurons respond to stimulation, and it can be used as a proxy for predicting the concurrent effects of stimulation, like changes in cortical excitability, and, ultimately, its plastic effects. While the use of multichannel systems with small electrodes has provided a more precise tool for delivering tCS, individually variable anatomical parameters like the shape and thickness of tissues affect the E-field distribution for a specific electrode montage. Therefore, using the same montage parameters across subjects does not lead to the homogeneity of E-field amplitude over the desired targets. Here we describe a pipeline that leverages individualized head models combined with montage optimization algorithms to reduce the variability of the E-field distributions over subjects in tCS. We will describe the different steps of the pipeline – namely, MRI segmentation and head model creation, target specification, and montage optimization – and discuss their main advantages and limitations.
Conference papers on the topic "Whole brain model"
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Fukushima, Makoto, Okito Yamashita, Thomas R. Knosche, and Masa-aki Sato. "MEG source reconstruction constrained by diffusion MRI based whole brain dynamical model." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556646.
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Liang, Haoyi, Natalia Dabrowska, Jaideep Kapur, and Daniel S. Weller. "Whole brain reconstruction from multilayered sections of a mouse model of status epilepticus." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335554.
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Hormuth, David A., Jared A. Weis, Stephanie B. Eldridge, Michael I. Miga, Erin C. Rericha, Vito Quaranta, and Thomas E. Yankeelov. "Abstract A09: Predicting response to whole brain radiotherapy in a murine model of glioma." In Abstracts: AACR Special Conference: Engineering and Physical Sciences in Oncology; June 25-28, 2016; Boston, MA. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.epso16-a09.
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Loureiro, R. C. V., and T. A. Smith. "Design of the ROBIN system: Whole-arm multi-model sensorimotor environment for the rehabilitation of brain injuries while sitting or standing." In 2011 IEEE 12th International Conference on Rehabilitation Robotics: Reaching Users & the Community (ICORR 2011). IEEE, 2011. http://dx.doi.org/10.1109/icorr.2011.5975511.
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Ley, Obdulia, and Yildiz Bayazitoglu. "Temperature Distribution in a Realistic Human Head During Selective and Whole Body Cooling and During Circulatory Arrest." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-61101.
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Using a realistic adult head and neck geometry and a thermal model, the transient temperature distribution is calculated during different cooling strategies and variations in cerebral blood flow. Given the importance of brain temperature in clinical therapy, temperature calculations using thermal models are necessary to optimize hypothermic therapies commonly employed for brain protection during surgery or in the treatment of brain injury. The calculations presented here show the effect of selective and whole body cooling strategies on the temperature gradients in the head; the time required to reach a stationary temperature distribution for the different cooling strategies; the importance of thermal stabilization when using deep hypothermic circulatory arrest, and the effect of selective head cooling in periods of lack of blood flow to control temperature gradients in the brain tissue produced by residual metabolic activity.
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Kanji, Suman, Benjamin Johnson, Kristina Witcher, Pooja Gulati, John Gregory Bodnar, Julie Fitzgerald, Courtney DeVries, et al. "Abstract LB-326: Unresolved microgliosis and impaired neurogenesis are associated with cognitive deficiency in a clinically relevant mouse model of fractionated whole brain radiation." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-lb-326.
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Zhu, Liang, and Chenguang Diao. "Computer-Aided Analysis of Transient and Steady State Temperature Distribution in Human Brain During Selective Cooling of Head Surface and Rewarming for Head Injury Patients." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-33686.
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In recent years, mild or moderate hypothermia during which brain temperature is reduced to 30–35°C has been proposed for clinical use as an adjunct for achieving protection from cerebral ischemia and traumatic brain injury. There are two approaches for achieving a reduction in brain temperature. One is via systemic hypothermia where the whole body is cooled. This approach may produce deleterious systemic complications and require intensive monitoring. Another approach is called selective brain cooling (SBC) in which the brain is selectively cooled while the rest of the body is kept at normal temperature. Clinically feasible SBC protocols include head hood or helmet with water or chemical cooling, head immersion in iced water, nasophyaryngeal cooling after tracheal intubation, and intro-carotid flushing. Simply packing ice or wearing cooling helmet is easy to implement. Previous theoretical study [Zhu and Diao, 2001] suggests that it is feasible to achieve mild hypothermia via head surface cooling. However, most physicians believe that it takes a much longer time to reduce the brain temperature using head surface cooling. In this study, a three-dimensional theoretical model is developed to study the transient and steady state temperature distribution in the brain during SBC. The effect of regionally varying local blood perfusion rate in the brain tissue on the temporal and spatial temperature gradient is examined. Other factors including the brain size and the thermal contact resistance between the cooling medium and the head scalp are evaluated in the simulation.
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Tan, X. Gary, and Amit Bagchi. "Modeling and Reconstruction of Multi-Fidelity Traumatic Head Injury due to Blunt Impact." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-70610.
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Traumatic brain injury (TBI) is one of the most common injuries to service members in recent conflicts. Computational models can offer insights in understanding the underlying mechanism of brain injury, which lead to the crucial development of effective personal protective equipment designed to prevent or mitigate the TBI. Historically many computational models were developed for the brain injury study. However, these models use relatively coarse mesh with a less detailed head anatomy. Many models consider the head only and thus cannot properly model the real scenario, i.e., accidental fall, blunt impact or blast loading. A whole-body finite element model can represent the real scenario but is very expensive to use. By combining the high-fidelity human head model with an articulated human body model, we developed the computational multi-fidelity human models to investigate the blunt- and blast-related TBI efficiently. A high-fidelity computational head model was generated from the high resolution image data to accurately reproduce the complex musculoskeletal and tissue structure of the head. The fast-running articulated human body model is based on the multi-body dynamics and was used to reconstruct the accidental falls. By utilizing the kinematics and force and moment at the joint of the articulated human body model, we can realistically simulate the blunt impact and assess the brain injury using the high-fidelity head model.
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Tan, X. Gary, Andrzej J. Przekwas, and Raj K. Gupta. "Macro-Micro Biomechanics Finite Element Modeling of Brain Injury Under Concussive Loadings." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-66218.
Full textAbstract:
Traumatic brain injury (TBI) occurs in many blunt, ballistic and blast impact events. During trauma axons in the white matter are especially vulnerable to injury due to the rapid mechanical loading of brain. The axonal pathology leads to cytoskeletal failure and disconnection. The microtubules are one of major structural components of the cytoskeleton filamentous network. By bridging the macroscopic forces acting on the whole brain with the cellular and subcellular failure, the macro-micro computational models in both time and space can help us better understand the complex biophysics and elucidate the injury mechanism of both severe and mild TBI (concussion). At the macroscopic scale we developed the high-fidelity anatomical human body finite element model (FEM) to predict intracranial pressures and strain and strain rate fields of brain in the blast event. The macro-scale models and the coupled blast and biomechanics approach were validated against test data of shock wave interacting with a surrogate head in the shock tube. The mechanical deformation of brain tissue was mapped to the white matter tracts to obtain local axonal strain and strain rate for the micromechanical models. We developed the micromechanical FEM of myelinated axons interconnected with the oligodendrocyte by the processes, utilizing a novel beam element free of rotational degrees of freedom (DOFs). The numerical results reveal the possible mechanism of impact-induced axon injury including demyelination, breakup of processes, and axonal varicosity. We also investigate the dynamic response of microtubules bundles under traumatic loading. Different from the commonly discrete bead-spring models, a network of microtubules cross-linked with microtubule-associated-protein (MAP) tau proteins was modeled by the nonlinear beam model. Tau protein is modeled by the rate-dependent bar element for its complicated material behavior. The model considers the rupture of microtubule and the failure of tau-tau interface and tau-microtubule interface. The simulation result of the combined effects of the failure of the cross-linked architecture and elongation and bending of the bundle are possibly correlated to the axonal undulations following traumatic loading observed in the experiments. The developed macro-micro biomechanics models can be used as a starting point for modeling the neurobiology effects and guide the design of novel injury protection strategies.
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Cuitino, Nicolas S., Benjamin Johannesson, and Assimina A. Pelegri. "A Computational Model of Continuous Hollow Cerebrovascular Arterioles Using a Fractal L-System." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88511.
Full textAbstract:
There is a need for better 3-D model representations of cerebrovasculature particularly on the order of arterioles. Such a model would have many applications and could be a useful tool for those conducting studies involving the brain and its function. The load bearing effects of the vasculature can be better studied with such a model, such as in the case of large strains. In addition, by having a continuous hollow structure, studies involving flow properties can be conducted at a whole scale rather than in a segmented view. Such studies are critical to the advancement of knowledge about the brain and its mechanics which can lead to advancements in preventative and curative care, as well as preventative safety measures. The model developed in this paper could serve as a tool in such studies. A fractal L-system is used to define the branching nature of the model. As such a growing tree structure is developed and characterized by its bifurcation at the end of a vessel segment. The index of bifurcation, α, is a parameter that controls the behavior of the two generated daughter vessels. The model presented here grows from a single parent branch into a bifurcation each of which then bifurcates as many times as specified. The length and diameter of the two daughter vessels will be a function of the respective parent’s length and diameter as well as a value α. The branching angle of the two daughter vessels will be entirely controlled by α. The hollow continuous nature of the model allows for it to be used as a representation of the arteriole structures in the brain. There is also use for such a model in other areas of the body, however, this study will focus on the representation of the cerebrovasculature. The end result is a branching tree model generated in Abaqus which is continuous, hollow and capable of extensive generation with uses in modeling complex cerebrovascular mechanics.
Reports on the topic "Whole brain model"
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Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.
Full textAbstract:
We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.