Relevant bibliographies by topics / Brain variability

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Brain variability'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Brain variability"

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Kaas, Jon H., and Christine E. Collins. "Variability in the sizes of brain parts." Behavioral and Brain Sciences 24, no. 2 (April 2001): 288–90. http://dx.doi.org/10.1017/s0140525x01333952.

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Rogachov, Anton, Joshua C. Cheng, Nathalie Erpelding, Kasey S. Hemington, Adrian P. Crawley, and Karen D. Davis. "Regional brain signal variability." PAIN 157, no. 11 (November 2016): 2483–92. http://dx.doi.org/10.1097/j.pain.0000000000000665.

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Croxson, Paula L., Stephanie J. Forkel, Leonardo Cerliani, and Michel Thiebaut de Schotten. "Structural Variability Across the Primate Brain: A Cross-Species Comparison." Cerebral Cortex 28, no. 11 (October 13, 2017): 3829–41. http://dx.doi.org/10.1093/cercor/bhx244.

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Abstract A large amount of variability exists across human brains; revealed initially on a small scale by postmortem studies and, more recently, on a larger scale with the advent of neuroimaging. Here we compared structural variability between human and macaque monkey brains using grey and white matter magnetic resonance imaging measures. The monkey brain was overall structurally as variable as the human brain, but variability had a distinct distribution pattern, with some key areas showing high variability. We also report the first evidence of a relationship between anatomical variability and evolutionary expansion in the primate brain. This suggests a relationship between variability and stability, where areas of low variability may have evolved less recently and have more stability, while areas of high variability may have evolved more recently and be less similar across individuals. We showed specific differences between the species in key areas, including the amount of hemispheric asymmetry in variability, which was left-lateralized in the human brain across several phylogenetically recent regions. This suggests that cerebral variability may be another useful measure for comparison between species and may add another dimension to our understanding of evolutionary mechanisms.
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Garrett, D. D., N. Kovacevic, A. R. McIntosh, and C. L. Grady. "The Importance of Brain Variability." NeuroImage 47 (July 2009): S81. http://dx.doi.org/10.1016/s1053-8119(09)70579-8.

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Shemie, Sam D. "Variability of Brain Death Practices." Critical Care Medicine 32, no. 12 (December 2004): 2564–65. http://dx.doi.org/10.1097/01.ccm.0000153903.72797.0b.

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Oliver, Isaura, Jaroslav Hlinka, Jakub Kopal, and Jörn Davidsen. "Quantifying the Variability in Resting-State Networks." Entropy 21, no. 9 (September 11, 2019): 882. http://dx.doi.org/10.3390/e21090882.

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Recent precision functional mapping of individual human brains has shown that individual brain organization is qualitatively different from group average estimates and that individuals exhibit distinct brain network topologies. How this variability affects the connectivity within individual resting-state networks remains an open question. This is particularly important since certain resting-state networks such as the default mode network (DMN) and the fronto-parietal network (FPN) play an important role in the early detection of neurophysiological diseases like Alzheimer’s, Parkinson’s, and attention deficit hyperactivity disorder. Using different types of similarity measures including conditional mutual information, we show here that the backbone of the functional connectivity and the direct connectivity within both the DMN and the FPN does not vary significantly between healthy individuals for the AAL brain atlas. Weaker connections do vary however, having a particularly pronounced effect on the cross-connections between DMN and FPN. Our findings suggest that the link topology of single resting-state networks is quite robust if a fixed brain atlas is used and the recordings are sufficiently long—even if the whole brain network topology between different individuals is variable.
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SHERIDAN, M. R., and K. A. FLOWERS. "MOVEMENT VARIABILITY AND BRADYKINESIA IN PARKINSON'S DISEASE." Brain 113, no. 4 (1990): 1149–61. http://dx.doi.org/10.1093/brain/113.4.1149.

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Nolano, M., V. Provitera, and L. Santoro. "Internodal length variability of dermal myelinated fibres." Brain 133, no. 6 (February 15, 2010): e142-e142. http://dx.doi.org/10.1093/brain/awq004.

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Wang, Yifeng, Yujia Ao, Qi Yang, Yang Liu, Yujie Ouyang, Xiujuan Jing, Yajing Pang, Qian Cui, and Huafu Chen. "Spatial variability of low frequency brain signal differentiates brain states." PLOS ONE 15, no. 11 (November 12, 2020): e0242330. http://dx.doi.org/10.1371/journal.pone.0242330.

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Temporal variability of the neural signal has been demonstrated to be closely related to healthy brain function. Meanwhile, the evolving brain functions are supported by dynamic relationships among brain regions. We hypothesized that the spatial variability of brain signal might provide important information about brain function. Here we used the spatial sample entropy (SSE) to investigate the spatial variability of neuroimaging signal during a steady-state presented face detection task. Lower SSE was found during task state than during resting state, associating with more repetitive functional interactions between brain regions. The standard deviation (SD) of SSE during the task was negatively related to the SD of reaction time, suggesting that the spatial pattern of neural activity is reorganized according to particular cognitive function and supporting the previous theory that greater variability is associated with better task performance. These results were replicated with reordered data, implying the reliability of SSE in measuring the spatial organization of neural activity. Overall, the present study extends the research scope of brain signal variability from the temporal dimension to the spatial dimension, improving our understanding of the spatiotemporal characteristics of brain activities and the theory of brain signal variability.
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McIntosh, Anthony Randal, Natasa Kovacevic, and Roxane J. Itier. "Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development." PLoS Computational Biology 4, no. 7 (July 4, 2008): e1000106. http://dx.doi.org/10.1371/journal.pcbi.1000106.

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Dissertations / Theses on the topic "Brain variability"

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Liu, Mianxin. "The brain at criticality : variability of brain spontaneous activity and relevance to brain functions." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/809.

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The brain activities are characterized by spontaneous and persistent irregular fluctuations in space and time. Criticality theory from statistical physics has been proposed as a principle to explain the variability in normal brain spontaneous activity and has suggested the functional benefits of variability, such as maximized dynamic range of response to stimuli and information capacity. In parallel, the brains show variability in other aspects, such as the structural heterogeneity across brain regions, the intra-individual variability across experimental trials, and the behavior difference across groups and individuals. The associations between the variability of spontaneous activities and these different types of structural, intra and inter-individual variabilities remain elusive. My doctoral study thus aimed to bridge the brain variability and the above-mentioned variations based on criticality theory and analysis of empirical data. As a preparatory analysis, we first collected evidence to prove criticality in human functional magnetic resonance imaging (fMRI) data. The advanced statistical criteria were used to exclude potential artefacts that can induce power-law scaling without the mechanism of criticality. In the first part of the study, we addressed methodological issue and tested whether several measures of either spatial or temporal complexity due to experimental limitations could be reliable proxy of spatiotemporal variability (related to criticality) in vivo. The high spatiotemporal resolutions of whole-cortex optical voltage imaging in mice brain during the waking up from anesthesia enabled simultaneous investigation of functional connectivity (FC), Multi-Scale Entropy (MSE, measure of temporal variability), Regional Entropy (RE, quantity of spatiotemporal variability) and the interdependency among them under different brain states. The results suggested that MSE and FC could be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. This study also lays methodological basis for the third study in this thesis. In the second study, we explored the interaction between spontaneous activity and evoked activity from mice brain imaging under whisker stimulus. The whisker stimulus will first evoke the local activation in sensory cortex and then trigger whole-cortex activity with variable patterns in different experimental trials. This trial-to-trial variability in the cortical evoked component was then attributed to the changes of ongoing activity state at stimulus onset. The study links ongoing activity variability and evoked activity variability, which further consolidates the association between ongoing activity and brain functions. In the third study, we measured the signal variability of the whole brain from resting state fMRI, and developed the multivariate pattern of cortical entropy, called entropy profile, as reliable and interpretable biomarker of individual difference in cognitive ability. We showed that the whole cortical entropy profile from resting- state fMRI is a robust personalized measure. We tested the predictive power for general and specific cognitive abilities based on cortical entropy profiles with out- of-sample prediction. Furthermore, we revealed the anatomical features underlying cross-region and cross-individual variations in cortical entropy profiles. This study provides new potential biomarker based on brain spontaneous variability which could benefit the applications in psychology and psychiatry studies. The whole study laid a foundation for brain criticality-/variability-based studies and applications and broadened our understanding of the associations between neural structures, functional dynamics and cognitive ability
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Arzounian, Dorothée. "Sensory variability and brain state : models, psychophysics, electrophysiology." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCB055/document.

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La même entrée sensorielle ne provoque pas toujours la même réaction. Dans les expériences en laboratoire, un stimulus donné peut engendrer une réponse différente à chaque nouvel essai, en particulier à proximité du seuil sensoriel. Ce phénomène est généralement attribué à une source de bruit non spécifique qui affecte la représentation sensorielle du stimulus ou le processus décisionnel. Dans cette thèse, nous examinons l'hypothèse selon laquelle cette variabilité des réponses peut être attribuée en partie à des fluctuations mesurables et spontanées de l'état cérébral. Dans ce but, nous développons et évaluons deux ensembles d'outils. L’un est un ensemble de modèles et de méthodes psychophysiques permettant de suivre les variations de la performance perceptive avec une bonne résolution temporelle et avec précision, sur différentes échelles de temps. Ces méthodes s’appuient sur des procédures adaptatives initialement développées pour mesurer efficacement les seuils de perception statiques et sont étendues ici dans le but de suivre des seuils qui varient au cours du temps. Le deuxième ensemble d'outils que nous développons comprend des méthodes d'analyse de données pour extraire de signaux d’électroencéphalographie (EEG) une quantité prédictive de la performance comportementale à diverses échelles de temps. Nous avons appliqué ces outils à des enregistrements conjoints d’EEG et de données comportementales collectées pendant que des auditeurs normo-entendants réalisaient une tâche de discrimination de fréquence sur des stimuli auditifs proche du seuil de discrimination. Contrairement à ce qui a été rapporté dans la littérature concernant des stimuli visuels, nous n'avons pas trouvé de preuve d’un quelconque effet des oscillations EEG spontanées de basse fréquence sur la performance auditive. En revanche, nous avons trouvé qu'une part importante de la variabilité des jugements peut s’expliquer par des effets de l'historique récent des stimuli et des réponses sur la décision prise à un moment donné
The same sensory input does not always trigger the same reaction. In laboratory experiments, a given stimulus may elicit a different response on each trial, particularly near the sensory threshold. This is usually attributed to an unspecific source of noise that affects the sensory representation of the stimulus or the decision process. In this thesis we explore the hypothesis that response variability can in part be attributed to measurable, spontaneous fluctuations of ongoing brain state. For this purpose, we develop and test two sets of tools. One is a set of models and psychophysical methods to follow variations of perceptual performance with good temporal resolution and accuracy on different time scales. These methods rely on the adaptive procedures that were developed for the efficient measurements of static sensory thresholds and are extended here for the purpose of tracking time-varying thresholds. The second set of tools we develop encompass data analysis methods to extract from electroencephalography (EEG) signals a quantity that is predictive of behavioral performance on various time scales. We applied these tools to joint recordings of EEG and behavioral data acquired while normal listeners performed a frequency-discrimination task on near-threshold auditory stimuli. Unlike what was reported in the literature for visual stimuli, we did not find evidence for any effects of ongoing low-frequency EEG oscillations on auditory performance. However, we found that a substantial part of judgment variability can be accounted for by effects of recent stimulus-response history on an ongoing decision
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Mikhael, Shadia S. "Brain cortical variability, software, and clinical implications." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33210.

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It is essential to characterize and quantify naturally occurring morphometric changes in the human brain when investigating the onset or progression of neurodegenerative disorders. The aim of this thesis is to characterize the properties and measure the performance of several popular automated magnetic resonance image analysis tools dedicated to brain morphometry. The thesis begins with an overview of morphometric analysis methods, followed by a literature review focusing on cortical parcellation protocols. Our work identified unanimous protocol weaknesses across all packages in particular issues when addressing cortical variability. The next chapters present a ground truth dataset and a dedicated software to analyse manually parcellated data. The dataset (https://datashare.is.ed.ac.uk/handle/10283/2936) includes 10 healthy middle-aged subjects, whose metrics we used as reference against automated tools. To develop the ground truth dataset, we also present a manual parcellation protocol (https://datashare.is.ed.ac.uk/handle/10283/3148) providing step-by-step instructions for outlining three cortical gyri known to vary with ageing and dementia: the superior frontal gyrus, the cingulate gyrus and the supramarginal gyrus. The software, Masks2Metrics (https://datashare.is.ed.ac.uk/handle/10283/3018), was built in Matlab to calculate cortical thickness, white matter surface area, and grey matter volume from 3D binary masks. Characterizing these metrics allowed further understanding of the assumptions made by software when creating and measuring anatomical parcels. Next, we present results from processing the raw T1-weighted volumes in the latest versions of several automated image analysis tools-FreeSurfer (versions 5.1 and 6.0), BrainGyrusMapping, and BrainSuite (version 13a)- against our ground truth. Tool repeatability for the same system was confirmed as multiple runs yielded identical results. Compared to our ground truth, the closest results were generated by BrainGyrusMapping for volume metrics and by FreeSurfer 6.0 for thickness and surface area metrics. In conclusion, our work sheds light on the significance of clearly detailed parcellation protocols and accurate morphometric tools due to the implications that they both will have. We therefore recommend extra caution when selecting image analysis tools for a study, and the use of independent publicly available ground truth datasets and metrics tools to assist with the selection process.
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Iordanov, Todor [Verfasser]. "Mapping brain response variability in schizophrenia / Todor Iordanov." Konstanz : Bibliothek der Universität Konstanz, 2012. http://d-nb.info/1025637275/34.

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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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Newell, Miranda E. "The connection between emotion, brain lateralization, and heart-rate variability /." Download the thesis in PDF, 2005. http://www.lrc.usuhs.mil/dissertations/pdf/Newell2005.pdf.

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Helps, Suzannah Katherine. "Response variability in ADHD : exploring the possible role of spontaneous brain activity." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/72432/.

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Attention-Deficit/Hyperactivity Disorder (ADHD) is the most common psychiatric disorder of childhood and manifests as symptoms of developmentally inappropriate inattention, impulsivity and hyperactivity. Although numerous deficits have been identified in ADHD, one of the most consistent findings is that patients with ADHD are more variable in the speed of their reaction time (RT) responses on neuropsychological tasks than control children. In 2008, the default-mode interference hypothesis of ADHD was introduced by Sonuga-Barke and Castellanos as a biologically plausible account of this increased within-subject variability in ADHD. This hypothesis suggests that some patients with ADHD might not effectively attenuate low frequency resting brain activity from rest to task and that these low frequency oscillations may then intrude onto task performance and cause periodic attention lapses. These periodic attention lapses would manifest as increased variability in RT data. The present thesis provided the first test of this hypothesis using DC-EEG. We assessed the power in very low frequency EEG bands (< .1 Hz) during rest and during goal-directed task performance in two samples. First was a sample of adults who self-reported either high- or low-ADHD scores, and second was a clinic referred sample of adolescent boys with ADHD and age- and gender-matched controls. We found that in both samples, low frequency EEG was generally attenuated from rest to task, but the degree of this attenuation was lower in ADHD or inattentive participants compared to controls. We also found that periodicity was evident in RT data, and that there was synchrony between low frequency fluctuations in RT data and low frequency EEG. These findings provide some initial support for the default mode interference hypothesis. The findings also highlight the potential involvement of low frequency electrodynamics in attentional processes and in the pathophysiology of ADHD.
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Kumral, Deniz [Verfasser]. "Variability in heart and brain activity across the adult lifespan / Deniz Kumral." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2021. http://d-nb.info/1234983168/34.

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Irfanoglu, Mustafa O. "Robust Variability Analysis Using Diffusion Tensor Imaging." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306946868.

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Smith, Rosalind Lauren. "Quantification and localization of gait variability as biomarkers for mild traumatic brain injury." Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/740.

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Motion capture technology and Magnetic Resonance Imaging with Diffusion Tensor Imaging (MRI-DTI) were used in this work to detect subtle abnormalities in patients with mild traumatic brain injury (MTBI). A new concept, termed dynamic variability, is introduced in this work to quantify and localize gait variability. Three chronic MTBI patients were recruited from the Veterans Affair Medical Center in Iowa City, IA, and three healthy controls with height, weight, and gender matched to the patients were recruited from the Reserve Officers' Training Corps in Iowa City, IA. Kinematic and kinetic data of the subjects were collected during the performance of three gait testing scenarios. The first test involved single-task walking and was used as a baseline. The second and third tests were dual tasks that involved walking while performing a cognitive or motor task and were designed to magnify gait abnormalities. The results showed that MTBI patients had reduced gait velocity, shortened stride length, and larger step width; findings that are consistent with those published in the literature. The new dynamic variability factor found that, as compared to controls, MTBI patients had more variability in their hip and ankle joint moments. MRI-DTI has been used to detect dysfunction of the major white matter tracts in chronic MTBI patients; although, the sample size of this study was too small to detect a difference between the MTBI and control subjects. The imaging and gait abnormalities are suggestive of frontal lobe-white matter tracts dysfunction.
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Books on the topic "Brain variability"

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Patrick, McNamara. Mind and variability: Mental Darwinism, memory, and self. Westport, Conn: Praeger, 1999.

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McNamara, Patrick. Mind and variability: Mental Darwinism, memory, and self. Westport, Conn: Praeger, 1999.

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Diversity in the neuronal machine: Order and variability in interneuronal microcircuits. New York: Oxford University Press, 2006.

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Negrello, Mario. Invariants of Behavior: Constancy and Variability in Neural Systems. New York, NY: Springer Science+Business Media, LLC, 2011.

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Wiethoff, Marion. Task analysis is heart work: The investigation of heart rate variability : a tool for task analysis in cognitive work. [Delft, The Netherlands: Delft University Press, 1997.

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Houle, Mélina. Croissance, dimorphisme sexuel et variabilité morphométrique du crâne entre différentes populations de lynx du Canada (Lynx canadensis) au Québec. Québec: Ministère des ressources naturelles et de la faune, 2005.

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Dynamic Brain: An Exploration of Neuronal Variability and Its Functional Significance. Oxford University Press, Incorporated, 2011.

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Ding, Mingzhou. Dynamic Brain: An Exploration of Neuronal Variability and Its Functional Significance. Oxford University Press, Incorporated, 2010.

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Montgomery, Jr, Erwin B. Deep Brain Stimulation Programming. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190259600.001.0001.

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This second edition of the book continues the basic premise that a thorough knowledge of the mechanisms by which neurons respond to electrical stimulation, how to control the stimulation and the regional anatomy allows the Deep Brain Stimulation (DBS) programmer to effectively and efficiently help patients reach optimal control of their disorder. There are a great many variables that influence the patient’s response to DBS, such as the exact nature of the patient’s individual symptoms and disabilities and the variability of the surgical placement of stimulating leads. The complexity has expanded because rapid increases in technology, both current and anticipated. The book makes no assumptions as to the prior knowledge or expertise. As the brain fundamentally is an electrical device, the book begins explaining the relevant electronics, building a nearly intuitive knowledge of how electrons are affected by electrical and magnetic forces and how the actions of the programmer controls electrical charges that ultimately activate neurons, which themselves are electrical devices.
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Wijdicks, Eelco F. M. International Criteria of Brain Death. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190662493.003.0003.

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Brain death criteria have mostly developed when there are organ donation policies in place. The variability in criteria and practices around the world is striking but also inherently problematic, with no consensus in sight. This chapter surveys the criteria across the continents, including in Canada, Europe, South America, Africa, Asian and the Middle East, and Australia and New Zealand. There is a specific focus on the brain death criteria in the United Kingdom and alleged contrasts with U.S. guidelines. A discussion of how best to achieve uniform criteria, despite obstacles, is described. Toward that effort a case is made to and allow the diagnosis of brain death if, after excluding any possible confounder, all brainstem reflexes have disappeared and the patient has become demonstrably apneic.
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Book chapters on the topic "Brain variability"

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Walter, G. F. "Dysontogenetic Brain Tumours. Morphological Variability and Problems of Classification." In Brain Oncology Biology, diagnosis and therapy, 87–90. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3347-7_16.

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Rios Piedra, Edgar A., Benjamin M. Ellingson, Ricky K. Taira, Suzie El-Saden, Alex A. T. Bui, and William Hsu. "Brain Tumor Segmentation by Variability Characterization of Tumor Boundaries." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 206–16. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-55524-9_20.

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Matić, Vladimir, Perumpillichira J. Cherian, Devy Widjaja, Katrien Jansen, Gunnar Naulaers, Sabine Van Huffel, and Maarten De Vos. "Heart Rate Variability in Newborns with Hypoxic Brain Injury." In Oxygen Transport to Tissue XXXV, 43–48. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7411-1_7.

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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.
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Kirkness, Catherine J., Robert L. Burr, and Pamela H. Mitchell. "Intracranial pressure variability and long-term outcome following traumatic brain injury." In Acta Neurochirurgica Supplements, 105–8. Vienna: Springer Vienna, 2008. http://dx.doi.org/10.1007/978-3-211-85578-2_21.

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Fernández-Aguilar, Luz, Arturo Martínez-Rodrigo, José Moncho-Bogani, Antonio Fernández-Caballero, and José Miguel Latorre. "Emotion Detection in Aging Adults Through Continuous Monitoring of Electro-Dermal Activity and Heart-Rate Variability." In Understanding the Brain Function and Emotions, 252–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19591-5_26.

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Ulaş, Aydın, Mehmet Gönen, Umberto Castellani, Vittorio Murino, Marcella Bellani, Michele Tansella, and Paolo Brambilla. "A Localized MKL Method for Brain Classification with Known Intra-class Variability." In Machine Learning in Medical Imaging, 152–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35428-1_19.

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Fillard, Pierre, Vincent Arsigny, Xavier Pennec, Paul M. Thompson, and Nicholas Ayache. "Extrapolation of Sparse Tensor Fields: Application to the Modeling of Brain Variability." In Lecture Notes in Computer Science, 27–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11505730_3.

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Liu, Ran, Cem Subakan, Aishwarya H. Balwani, Jennifer Whitesell, Julie Harris, Sanmi Koyejo, and Eva L. Dyer. "A Generative Modeling Approach for Interpreting Population-Level Variability in Brain Structure." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 257–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59722-1_25.

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Anderson, J., C. Rennie, E. Gordon, and R. Meares. "The Quantification of Variability in Event-Related Potentials and Its Application to Schizophrenia." In Imaging of the Brain in Psychiatry and Related Fields, 239–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77087-6_34.

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Conference papers on the topic "Brain variability"

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Gowreesunker, B. V., A. H. Tewfik, V. A. Tadipatri, N. F. Ince, J. Ashe, and G. Pellizzer. "Overcoming measurement time variability in brain machine interface." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5332568.

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Balwani, Aishwarya H., and Eva L. Dyer. "Modeling Variability in Brain Architecture with Deep Feature Learning." In 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2019. http://dx.doi.org/10.1109/ieeeconf44664.2019.9048805.

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Goetz, Stefan M., and A. V. Peterchev. "A model of variability in brain stimulation evoked responses." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6347467.

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Lindh, Daniel, Ilja Sligte, Kimron Shapiro, and Ian Charest. "Brain and DCNN representational geometries predict variability in conscious access." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1287-0.

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Changoluisa, Vinicio, Pablo Varona, and Francisco B. Rodriguez. "An electrode selection approach in P300-based BCIs to address inter- and intra-subject variability." In 2018 6th International Conference on Brain-Computer Interface (BCI). IEEE, 2018. http://dx.doi.org/10.1109/iww-bci.2018.8311497.

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Cubo, Ruben, Markus Fahlstrcom, Elena Jiltsova, Helena Andersson, and Alexander Medvedev. "Semi-Individualized electrical models in deep brain stimulation: A variability analysis." In 2017 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2017. http://dx.doi.org/10.1109/ccta.2017.8062514.

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Ergin, Aysegul, Mei Wang, Shailendra Joshi, and Irving J. Bigio. "Optical Monitoring of Tracers and Mitoxantrone in Rabbit Brain and the Variability in Blood-Brain Barrier Disruption." In Optical Molecular Probes, Imaging and Drug Delivery. Washington, D.C.: OSA, 2011. http://dx.doi.org/10.1364/omp.2011.omc3.

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Thomas, Kavitha P., Cuntai Guan, Lau Chiew Tong, and A. P. Vinod. "A Study on the impact of spectral variability in brain-computer interface." In 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010. IEEE, 2010. http://dx.doi.org/10.1109/iscas.2010.5537303.

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Ferrari, Marco, Qingnong Wei, Roberto A. De Blasi, Valentina Quaresima, and Giovanni Zaccanti. "Variability of human brain and muscle optical pathlength in different experimental conditions." In OE/LASE'93: Optics, Electro-Optics, & Laser Applications in Science& Engineering, edited by Britton Chance and Robert R. Alfano. SPIE, 1993. http://dx.doi.org/10.1117/12.154666.

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Cheng, Lin, Hong Zhu, Yang Zhu, Naying He, Yang Yang, Huawei Ling, Shanbao Tong, Yi Fu, and Junfeng Sun. "Decreased variability of dynamic phase synchronization in brain networks during hand movement." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8037771.

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You might also be interested in the extended bibliographies on the topic 'Brain variability' for particular source types:
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