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Книги з теми "FMRI Data"

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: FMRI Data.

Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Computing brain activity maps from fMRI time-series images. Cambridge: Cambridge University Press, 2007.

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2

Faro, Scott H. BOLD fMRI: A guide to functional imaging for neuroscientists. New York: Springer, 2010.

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3

BOLD fMRI: A guide to functional imaging for neuroscientists. New York: Springer, 2010.

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4

Ashby, F. Gregory. Statistical Analysis of fMRI Data. The MIT Press, 2011. http://dx.doi.org/10.7551/mitpress/8764.001.0001.

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5

Ashby, F. Gregory. Statistical Analysis of FMRI Data. MIT Press, 2019.

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6

Ashby, F. Gregory. Statistical Analysis of FMRI Data. MIT Press, 2011.

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7

Ashby, F. Gregory. Statistical Analysis of FMRI Data. MIT Press, 2011.

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8

Ashby, F. Gregory. Statistical Analysis of FMRI Data. MIT Press, 2019.

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9

Statistical Analysis Of Fmri Data. MIT Press (MA), 2011.

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10

Statistical Analysis of FMRI Data. MIT Press, 2019.

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11

Ashby, F. Gregory. Statistical Analysis of FMRI Data. MIT Press, 2019.

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12

Hu, Xiaoping Philip, and Nanyin Zhang, eds. Temporal Features in Resting State fMRI Data. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88966-408-5.

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13

Song, Xiaopeng, Fei Du, Yajun Ma, and Zachory Wei, eds. Innovative fMRI Data Modeling Methods for Brain-Related Diseases/Disorders. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88976-650-5.

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14

King, Wayne M. Multitaper spectral estimation and time-domain cross-correlation in FMRI data analysis: Actual and simulated data. 1999.

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15

Ramani, Ramachandran, ed. Functional MRI. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190297763.001.0001.

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Анотація:

Functional MRI with BOLD (Blood Oxygen Level Dependent) imaging is one of the commonly used modalities for studying brain function in neuroscience. The underlying source of the BOLD fMRI signal is the variation in oxyhemoglobin to deoxyhemoglobin ratio at the site of neuronal activity in the brain. fMRI is mostly used to map out the location and intensity of brain activity that correlate with mental activities. In recent years, a new approach to fMRI was developed that is called resting-state fMRI. The fMRI signal from this method does not require the brain to perform any goal-directed task; it is acquired with the subject at rest. It was discovered that there are low-frequency fluctuations in the fMRI signal in the brain at rest. The signals originate from spatially distinct functionally related brain regions but exhibit coherent time-synchronous fluctuations. Several of the networks have been identified and are called resting-state networks. These networks represent the strength of the functional connectivity between distinct functionally related brain regions and have been used as imaging markers of various neurological and psychiatric diseases. Resting-state fMRI is also ideally suited for functional brain imaging in disorders of consciousness and in subjects under anesthesia. This book provides a review of the basic principles of fMRI (signal sources, acquisition methods, and data analysis) and its potential clinical applications.

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16

Kaye, Walter, and Alice V. Ely. Appetitive Regulation in Anorexia Nervosa and Bulimia Nervosa. Edited by W. Stewart Agras and Athena Robinson. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190620998.013.4.

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Anorexia and bulimia nervosa are complex disorders with dysregulated appetitive behaviors. The underlying causes of disturbed patterns of eating are unknown, but a growing body of research suggests that aberrant functioning of brain or peripheral systems may be responsible. Neuroimaging technologies, such as positron emission tomography (PET) and functional MRI (fMRI), can be used to explore whether there are perturbations of the monoamine systems, the neurocircuitry of gustatory processing in eating disorders, and their relationship to metabolic homeostatic states. Together, PET and fMRI data suggest that individuals with eating disorders have disturbance of taste- and reward-processing regions of the brain, which may contribute to eating disorder symptoms.

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17

Soriano-Mas, Carles, and Ben J. Harrison. Brain Functional Connectivity in OCD. Edited by Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0024.

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This chapter provides an overview of studies assessing alterations in brain functional connectivity in obsessive-compulsive disorder (OCD) as assessed by functional magnetic resonance imaging (fMRI). Although most of the reviewed studies relate to the analysis of resting-state fMRI data, the chapter also reviews studies that have combined resting-state with structural or task-based approaches, as well as task-based studies in which the analysis of functional connectivity was reported. The main conclusions to be drawn from this review are that patients with OCD consistently demonstrate altered patterns of brain functional connectivity in large-scale “frontostriatal” and “default mode” networks, and that the heterogeneity of OCD symptoms is likely to partly arise via distinct modulatory influences on these networks by broader disturbances of affective, motivational, and regulatory systems. The variable nature of some findings across studies as well as the influence of medications on functional connectivity measures is also discussed.

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18

Meijer, Ewout H., and Bruno Verschuere. Detection Deception Using Psychophysiological and Neural Measures. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190612016.003.0010.

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The use of physiological signals to detect deception can be traced back almost a century. Historically, the polygraph has been used—and debated. This chapter discusses the merits of polygraph testing, and to what extent the introduction of measures of brain activity—most notably functional magnetic imaging (fMRI)—have solved the problems associated with polygraph testing. It discusses the different question formats used with polygraph and brain activity measures, and argues that these formats are the main factor contributing to the tests’ validity. Moreover, the authors argue that erroneous test outcomes are caused by errors in logical inferences, and that these errors will not be remedied by new technology. The biggest challenge for the field is to find a question format that isolates deception, and to corroborate laboratory data with methodologically sound field studies.

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19

South, Mikle, John D. Herrington, and Sarah J. Paterson. Neuroimaging in Autism Spectrum Disorders. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199744312.003.0003.

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This chapter reviews several major themes in the neuroimaging of ASDs to date (see summary of representative themes in Table 3.1), including substantial and essential contributions from the modular framework. The chapter begins, however, with a discussion of several challenges related to the diversity of ASDs in terms of factors such as age, level of functioning, and symptom presentation. Progress in the ability to identify more homogenous subgroups, based on targeted phenotypic measures, opens the door to link neuroimaging with genetics findings and also with treatment outcome data. This should lead to better understanding of both the causes of ASDs and the best approaches to intervention. The chapter is divided according to two broad, related themes related to social information processing and cognitive factors in ASDs. Within these themes, the chapter considers evidence from both structural and functional imaging studies as well as relatively newer approaches to connectivity, including diffusion tensor imaging. The primary focus of this chapter is on research utilizing functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Although several early neuroimaging studies utilized positron emission tomography scanning, these studies are rare now and are not addressed in depth. New techniques such as near-infrared spectroscopy suggest tremendous promise for noninvasive imaging of expanded age groups and severity levels of ASDs; however, these studies are also few in number and are touched on only briefly.

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20

Andres, Michael, and Mauro Pesenti. Finger-based representation of mental arithmetic. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.028.

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Human beings are permanently required to process the world numerically and, consequently, to perform computations to adapt their behaviour and they have developed various calculation strategies, some of them based on specific manipulations of the fingers. In this chapter, we argue that the way we express physically numerical concepts by raising fingers while counting leads to embodied representations of numbers and calculation procedures in the adult brain. To illustrate this, we focus on number and finger interactions in the context of simple arithmetic operations. We show that the fixed order of fingers on the hand provides human beings with unique facilities to increment numerical changes or represent a cardinal value while solving arithmetic problems. In order to specify the influence of finger representation on mental arithmetic both at the cognitive and neural level, we review past and recent findings from behavioural, electrophysiological, and brain imaging studies. We start with anthropological and developmental data showing the role of fingers in the acquisition of arithmetic knowledge, then address the issue of whether number and finger interactions are also observed in adults solving arithmetic problems mentally. We suggest that arithmetic performance depends on the integrity of finger representations in children and adults. Finally, we overview the results of recent functional magnetic resonance imaging (fMRI) studies showing a common brain substrate for finger and number representations during and after the acquisition of arithmetic skills.

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21

Prasad, Supritha, and Edwin H. Cook. Novel Approaches for Treating Pediatric Psychiatric Disorders. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0067.

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Multifactorial mechanisms, including varying degrees of polygenic risk, contribute to most child onset psychiatric disorders. Methods to better understand the biological impact of inherited low-risk variation are emerging, and these studies may be useful to develop novel treatments for childhood onset psychiatric disorders. In some neurodevelopmental disorders, specifically autism spectrum disorder (ASD) and intellectual disability (ID), recurrent spontaneously mutated genes have been identified. This leads to the current focus on individual, high-risk targets (e.g., SHANK3, FMR1, MECP2, CHD8) for development of novel treatments. This chapter summarizes and begins to compare neurobiological data from several distinct single gene disorders as a means to guide further therapeutic development based on overlapping pathways of interest.

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