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Journal articles on the topic 'Computational neuroimaging'

Author: Grafiati

Published: 6 September 2023

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1

Stephan, Klaas E., Sandra Iglesias, Jakob Heinzle, and Andreea O. Diaconescu. "Translational Perspectives for Computational Neuroimaging." Neuron 87, no. 4 (August 2015): 716–32. http://dx.doi.org/10.1016/j.neuron.2015.07.008.

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2

Potter, Aneirin. "044 At what resolution does the brain perform computations?" Journal of Neurology, Neurosurgery & Psychiatry 93, no. 9 (August 12, 2022): e2.239. http://dx.doi.org/10.1136/jnnp-2022-abn2.88.

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Computation is the transformation of inputs into outputs through logical operations such as AND, OR, and NOT. This literature review compares models of computation at different physiological resolutions, whole brain networks, multi-cell circuits, individual synapses and individual molecular interactions and discusses if these models might be useful for bridging between functional neuroimaging with molecular models of disease. While resolution in functional neuroimaging such as EEG, MEG, PET, and fMRI is of groups of neurons pharmacotherapy alters the brain at a molecular level. Bridging this resolution gap presents many difficulties for modellers and wider connectome projects. Sufficiently detailed models can quickly outstrip computational capacity while not including sufficient detail leads to models lacking physiological validity. This is particularly problematic when connectome projects overpromise in their capacity to understand brain disorders without basis in valid physiological models. Examples of computation at network and molecular levels suggests a lack of consensus about what resolution the brain performs computations and how these computations interact. This interaction should be a goal for further research, especially given its role in linking functional neuroimaging diagnostics and pharmacological treatments in neurology.

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3

Wandell, Brian A. "COMPUTATIONAL NEUROIMAGING OF HUMAN VISUAL CORTEX." Annual Review of Neuroscience 22, no. 1 (March 1999): 145–73. http://dx.doi.org/10.1146/annurev.neuro.22.1.145.

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4

Wandell, Brian A., and Jonathan Winawer. "Computational neuroimaging and population receptive fields." Trends in Cognitive Sciences 19, no. 6 (June 2015): 349–57. http://dx.doi.org/10.1016/j.tics.2015.03.009.

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5

Friston, Karl J., and Raymond J. Dolan. "Computational and dynamic models in neuroimaging." NeuroImage 52, no. 3 (September 2010): 752–65. http://dx.doi.org/10.1016/j.neuroimage.2009.12.068.

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6

Stephan, K. E., F. Schlagenhauf, Q. J. M. Huys, S. Raman, E. A. Aponte, K. H. Brodersen, L. Rigoux, et al. "Computational neuroimaging strategies for single patient predictions." NeuroImage 145 (January 2017): 180–99. http://dx.doi.org/10.1016/j.neuroimage.2016.06.038.

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7

Di Ieva, Antonio, Mounir Boukadoum, Salim Lahmiri, and Michael D. Cusimano. "Computational Analyses of Arteriovenous Malformations in Neuroimaging." Journal of Neuroimaging 25, no. 3 (December 17, 2014): 354–60. http://dx.doi.org/10.1111/jon.12200.

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8

Poldrack, Russell A., Krzysztof J. Gorgolewski, and Gaël Varoquaux. "Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging." Annual Review of Biomedical Data Science 2, no. 1 (July 20, 2019): 119–38. http://dx.doi.org/10.1146/annurev-biodatasci-072018-021237.

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The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data-sharing resources that have been developed for neuroimaging data, as well as the role of data standards (particularly the brain imaging data structure) in enabling the automated sharing, processing, and reuse of large neuroimaging data sets. We outline how the open source Python language has provided the basis for a data science platform that enables reproducible data analysis and visualization. We also discuss how new advances in software engineering, such as containerization, provide the basis for greater reproducibility in data analysis. The emergence of this new ecosystem provides an example for many areas of science that are currently struggling with reproducibility.

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Ritter, Petra, Michael Schirner, Anthony R. McIntosh, and Viktor K. Jirsa. "The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging." Brain Connectivity 3, no. 2 (April 2013): 121–45. http://dx.doi.org/10.1089/brain.2012.0120.

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Goldstein-Piekarski, Andrea N., Bailey Holt-Gosselin, Kathleen O’Hora, and Leanne M. Williams. "Integrating sleep, neuroimaging, and computational approaches for precision psychiatry." Neuropsychopharmacology 45, no. 1 (August 19, 2019): 192–204. http://dx.doi.org/10.1038/s41386-019-0483-8.

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11

Eickhoff, S. B., and D. Bzdok. "Neuroimaging and modeling." Die Psychiatrie 11, no. 04 (October 2014): 245–53. http://dx.doi.org/10.1055/s-0038-1670776.

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SummaryThe links between symptomatic phenomenology of psychiatric disorders and their neurobiological pathophysiology are still understood only in fragments. While functional and structural neuroimaging methods have leveraged our knowledge of regional dysfunction in psychiatric disorders, emerging novel approaches more focused on brain networks or neural patterns promise additional forward progress. Activation likelihood estimation (ALE) performs quantitative large-scale aggregation of neuroimaging findings. Resting-state (RS) correlation captures networks of functional relationships between regions in the idling, non-goal-focused brain. In contrast, meta-analytic connectivity modeling (MACM) captures functional coupling between brain regions in the context of experimental paradigms. These methods may furthermore be exploited to provide data-driven parcellations (CBP, connectivity-based parcellation) of larger brain regions into distinct functional modules. Dynamic causal modeling (DCM), in turn, allows for the automatic selection among a set of connectional network models to delineate effective connectivity dynamics during experimental paradigms. Finally, machine learning (ML) allows for the automatic detection and prediction of diagnosis/treatment-response patterns in massive datasets. Capitalizing on this toolbox of computational modeling methods might considerably further psychiatry and thus benefit patients with mental disorders.

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Villoing, Daphnée, Ae-Kyoung Lee, Hyung-do Choi, and Choonsik Lee. "S VALUES FOR NEUROIMAGING PROCEDURES ON KOREAN PEDIATRIC AND ADULT HEAD COMPUTATIONAL PHANTOMS." Radiation Protection Dosimetry 185, no. 2 (March 13, 2019): 168–75. http://dx.doi.org/10.1093/rpd/ncy287.

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Abstract Over the past decades, the application of single-photon emission computed tomography and positron emission tomography in neuroimaging has markedly increased. In the current study, we used a series of Korean computational head phantoms with detailed cranial structures for 6-, 9-, 12-, 15-y-old children and adult and a Monte Carlo transport code, MCNPX, to calculate age-dependent specific absorbed fraction (SAF) for mono-energetic electrons ranging from 0.01 to 4 MeV and S values for seven radionuclides widely used in nuclear medicine neuroimaging for the combination of ten source and target regions. Compared to the adult phantom, the 6-y phantom showed up to 1.7-fold greater SAF (cerebellum < cerebellum) and up to 1.4-fold greater S values (vitreous body < lens) for 123I. The electron SAF data, combined with our previous photon SAF data, will facilitate absorbed dose calculations for various cranial structures in patients undergoing neuroimaging procedures.

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13

Shatil, Anwar S., Sohail Younas, Hossein Pourreza, and Chase R. Figley. "Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing." Magnetic Resonance Insights 8s1 (January 2015): MRI.S23558. http://dx.doi.org/10.4137/mri.s23558.

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With larger data sets and more sophisticated analyses, it is becoming increasingly common for neuroimaging researchers to push (or exceed) the limitations of standalone computer workstations. Nonetheless, although high-performance computing platforms such as clusters, grids and clouds are already in routine use by a small handful of neuroimaging researchers to increase their storage and/or computational power, the adoption of such resources by the broader neuroimaging community remains relatively uncommon. Therefore, the goal of the current manuscript is to: 1) inform prospective users about the similarities and differences between computing clusters, grids and clouds; 2) highlight their main advantages; 3) discuss when it may (and may not) be advisable to use them; 4) review some of their potential problems and barriers to access; and finally 5) give a few practical suggestions for how interested new users can start analyzing their neuroimaging data using cloud resources. Although the aim of cloud computing is to hide most of the complexity of the infrastructure management from end-users, we recognize that this can still be an intimidating area for cognitive neuroscientists, psychologists, neurologists, radiologists, and other neuroimaging researchers lacking a strong computational background. Therefore, with this in mind, we have aimed to provide a basic introduction to cloud computing in general (including some of the basic terminology, computer architectures, infrastructure and service models, etc.), a practical overview of the benefits and drawbacks, and a specific focus on how cloud resources can be used for various neuroimaging applications.

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14

Di Ieva, Antonio, Marzia Niamah, Ravi J. Menezes, May Tsao, Timo Krings, Young-Bin Cho, Michael L. Schwartz, and Michael D. Cusimano. "Computational Fractal-Based Analysis of Brain Arteriovenous Malformation Angioarchitecture." Neurosurgery 75, no. 1 (March 21, 2014): 72–79. http://dx.doi.org/10.1227/neu.0000000000000353.

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Abstract BACKGROUND: Neuroimaging is the gold standard for diagnosis and follow-up of brain arteriovenous malformations (bAVMs), but no objective parameter has been validated for the assessment of the nidus angioarchitecture and for prognostication following treatment. The fractal dimension (FD), which is a mathematical parameter able to quantify the space-filling properties and roughness of natural objects, may be useful in quantifying the geometrical complexity of bAVMs nidus. OBJECTIVE: To propose FD as a neuroimaging biomarker of the nidus angioarchitecture, which might be related to radiosurgical outcome. METHODS: We retrospectively analyzed 54 patients who had undergone stereotactic radiosurgery for the treatment of bAVMs. The quantification of the geometric complexity of the vessels forming the nidus, imaged in magnetic resonance imaging, was assessed by means of the box-counting method to obtain the fractal dimension. RESULTS: FD was found to be significantly associated with the size (P = .03) and volume (P < .001) of the nidus, in addition to several angioarchitectural parameters. A nonsignificant association between clinical outcome and FD was observed (area under the curve, 0.637 [95% confidence interval, 0.49-0.79]), indicative of a potential inverse relationship between FD and bAVM obliteration. CONCLUSION: In our exploratory methodological research, we showed that the FD is an objective computer-aided parameter for quantifying the geometrical complexity and roughness of the bAVM nidus. The results suggest that more complex bAVM angioarchitecture, having higher FD values, might be related to decreased response to radiosurgery and that the FD of the bAVM nidus could be used as a morphometric neuroimaging biomarker.

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15

O’Reilly, Jill X., and Rogier B. Mars. "Computational neuroimaging: localising Greek letters? Comment on Forstmann et al." Trends in Cognitive Sciences 15, no. 10 (October 2011): 450. http://dx.doi.org/10.1016/j.tics.2011.07.012.

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16

Dunne, Simon, and John P. O’Doherty. "Insights from the application of computational neuroimaging to social neuroscience." Current Opinion in Neurobiology 23, no. 3 (June 2013): 387–92. http://dx.doi.org/10.1016/j.conb.2013.02.007.

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17

Decety, Jean, and Stephanie Cacioppo. "The speed of morality: a high-density electrical neuroimaging study." Journal of Neurophysiology 108, no. 11 (December 1, 2012): 3068–72. http://dx.doi.org/10.1152/jn.00473.2012.

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Neuroscience research indicates that moral reasoning is underpinned by distinct neural networks including the posterior superior temporal sulcus (pSTS), amygdala, and ventromedial prefrontal cortex, which support communication between computational systems underlying affective states, cognitions, and motivational processes. To characterize real-time neural processing underpinning moral computations, high-density event-related potentials were measured in participants while they viewed short, morally laden visual scenarios depicting intentional and accidental harmful actions. Current source density maxima in the right pSTS as fast as 62 ms poststimulus first distinguished intentional vs. accidental actions. Responses in the amygdala/temporal pole (122 ms) and ventromedial prefrontal cortex (182 ms) were then evoked by the perception of harmful actions, indicative of fast information processing associated with early stages of moral cognition. Our data strongly support the notion that intentionality is the first input to moral computations. They also demonstrate that emotion acts as a gain antecedent to moral judgment by alerting the individual to the moral salience of a situation and provide evidence for the pervasive role of affect in moral sensitivity and reasoning.

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18

Munir, Kamran, Saad Liaquat Kiani, Khawar Hasham, Richard McClatchey, Andrew Branson, and Jetendr Shamdasani. "Provision of an integrated data analysis platform for computational neuroscience experiments." Journal of Systems and Information Technology 16, no. 3 (August 5, 2014): 150–69. http://dx.doi.org/10.1108/jsit-01-2014-0004.

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Purpose – The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community. Design/methodology/approach – The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses. Findings – The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases. Originality/value – The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

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19

Lerma-Usabiaga, Garikoitz, Noah Benson, Jonathan Winawer, and Brian Wandell. "Computational validity of neuroimaging software: the case of population receptive fields." Journal of Vision 20, no. 11 (October 20, 2020): 341. http://dx.doi.org/10.1167/jov.20.11.341.

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20

Fox, Peter T., Jack L. Lancaster, Angela R. Laird, and Simon B. Eickhoff. "Meta-Analysis in Human Neuroimaging: Computational Modeling of Large-Scale Databases." Annual Review of Neuroscience 37, no. 1 (July 8, 2014): 409–34. http://dx.doi.org/10.1146/annurev-neuro-062012-170320.

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21

Ressler, Kerry J., and Leanne M. Williams. "Big data in psychiatry: multiomics, neuroimaging, computational modeling, and digital phenotyping." Neuropsychopharmacology 46, no. 1 (September 12, 2020): 1–2. http://dx.doi.org/10.1038/s41386-020-00862-x.

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22

Kini, Lohith G., James C. Gee, and Brian Litt. "Computational analysis in epilepsy neuroimaging: A survey of features and methods." NeuroImage: Clinical 11 (2016): 515–29. http://dx.doi.org/10.1016/j.nicl.2016.02.013.

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23

Donnelly‐Kehoe, Patricio Andres, Guido Orlando Pascariello, Adolfo M. García, John R. Hodges, Bruce Miller, Howie Rosen, Facundo Manes, et al. "Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging." Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 11, no. 1 (December 2019): 588–98. http://dx.doi.org/10.1016/j.dadm.2019.06.002.

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24

Nadel, L., A. Samsonovich, L. Ryan, and M. Moscovitch. "Multiple trace theory of human memory: Computational, neuroimaging, and neuropsychological results." Hippocampus 10, no. 4 (2000): 352–68. http://dx.doi.org/10.1002/1098-1063(2000)10:4<352::aid-hipo2>3.0.co;2-d.

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25

Saggar, Manish, Risto Miikkulainen, and David M. Schnyer. "Behavioral, neuroimaging, and computational evidence for perceptual caching in repetition priming." Brain Research 1315 (February 2010): 75–91. http://dx.doi.org/10.1016/j.brainres.2009.11.074.

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26

Ezaki, Takahiro, Takamitsu Watanabe, Masayuki Ohzeki, and Naoki Masuda. "Energy landscape analysis of neuroimaging data." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375, no. 2096 (May 15, 2017): 20160287. http://dx.doi.org/10.1098/rsta.2016.0287.

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Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analysed and the data length. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’.

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27

Jacobs, Arthur M., and Frank Rösler. "Dondersian dreams in brain-mappers' minds, or, still no cross-fertilization between mind mappers and cognitive modelers?" Behavioral and Brain Sciences 22, no. 2 (April 1999): 293–95. http://dx.doi.org/10.1017/s0140525x99351827.

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Pulvermüller identifies two major flaws of the subtraction method of neuroimaging studies and proposes remedies. We argue that these remedies are themselves flawed and that the cognitive science community badly needs to take initial steps toward a cross-fertilization between mind mappers and cognitive modelers. Such steps could include the development of computational task models that transparently and falsifiably link the input (stimuli) and output (changes in blood flow or brain waves) of neuroimaging studies to changes in information processing activity that is the stuff of cognitive models.

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Kiar, Gregory, Pablo de Oliveira Castro, Pierre Rioux, Eric Petit, Shawn T. Brown, Alan C. Evans, and Tristan Glatard. "Comparing perturbation models for evaluating stability of neuroimaging pipelines." International Journal of High Performance Computing Applications 34, no. 5 (May 21, 2020): 491–501. http://dx.doi.org/10.1177/1094342020926237.

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With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across domains, tools, data sets, and computational infrastructures, but numerical instabilities are thought to be a core contributor. In neuroimaging, unexpected deviations have been observed when varying operating systems, software implementations, or adding negligible quantities of noise. In the field of numerical analysis, these issues have recently been explored through Monte Carlo Arithmetic, a method involving the instrumentation of floating-point operations with probabilistic noise injections at a target precision. Exploring multiple simulations in this context allows the characterization of the result space for a given tool or operation. In this article, we compare various perturbation models to introduce instabilities within a typical neuroimaging pipeline, including (i) targeted noise, (ii) Monte Carlo Arithmetic, and (iii) operating system variation, to identify the significance and quality of their impact on the resulting derivatives. We demonstrate that even low-order models in neuroimaging such as the structural connectome estimation pipeline evaluated here are sensitive to numerical instabilities, suggesting that stability is a relevant axis upon which tools are compared, alongside more traditional criteria such as biological feasibility, computational efficiency, or, when possible, accuracy. Heterogeneity was observed across participants which clearly illustrates a strong interaction between the tool and data set being processed, requiring that the stability of a given tool be evaluated with respect to a given cohort. We identify use cases for each perturbation method tested, including quality assurance, pipeline error detection, and local sensitivity analysis, and make recommendations for the evaluation of stability in a practical and analytically focused setting. Identifying how these relationships and recommendations scale to higher order computational tools, distinct data sets, and their implication on biological feasibility remain exciting avenues for future work.

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Mujica-Parodi, Lilianne R., and Helmut H. Strey. "Making Sense of Computational Psychiatry." International Journal of Neuropsychopharmacology 23, no. 5 (March 27, 2020): 339–47. http://dx.doi.org/10.1093/ijnp/pyaa013.

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Abstract In psychiatry we often speak of constructing “models.” Here we try to make sense of what such a claim might mean, starting with the most fundamental question: “What is (and isn’t) a model?” We then discuss, in a concrete measurable sense, what it means for a model to be useful. In so doing, we first identify the added value that a computational model can provide in the context of accuracy and power. We then present limitations of standard statistical methods and provide suggestions for how we can expand the explanatory power of our analyses by reconceptualizing statistical models as dynamical systems. Finally, we address the problem of model building—suggesting ways in which computational psychiatry can escape the potential for cognitive biases imposed by classical hypothesis-driven research, exploiting deep systems-level information contained within neuroimaging data to advance our understanding of psychiatric neuroscience.

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Polosecki, P., E. Castro, A. Wood, J. H. Warner, I. Rish, and G. A. Cecchi. "Computational psychiatry: Advancing predictive modeling of neurodegeneration with neuroimaging of Huntington's disease." IBM Journal of Research and Development 61, no. 2/3 (March 1, 2017): 4:1–4:10. http://dx.doi.org/10.1147/jrd.2017.2648700.

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31

Sprague, Thomas C., Geoffrey M. Boynton, and John T. Serences. "The Importance of Considering Model Choices When Interpreting Results in Computational Neuroimaging." eneuro 6, no. 6 (November 2019): ENEURO.0196–19.2019. http://dx.doi.org/10.1523/eneuro.0196-19.2019.

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32

Dinov, Ivo, Kamen Lozev, Petros Petrosyan, Zhizhong Liu, Paul Eggert, Jonathan Pierce, Alen Zamanyan, et al. "Neuroimaging Study Designs, Computational Analyses and Data Provenance Using the LONI Pipeline." PLoS ONE 5, no. 9 (September 28, 2010): e13070. http://dx.doi.org/10.1371/journal.pone.0013070.

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33

De Martino, Federico, Essa Yacoub, Valentin Kemper, Michelle Moerel, Kâmil Uludağ, Peter De Weerd, Kamil Ugurbil, Rainer Goebel, and Elia Formisano. "The impact of ultra-high field MRI on cognitive and computational neuroimaging." NeuroImage 168 (March 2018): 366–82. http://dx.doi.org/10.1016/j.neuroimage.2017.03.060.

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34

Bordier, Cecile, Francesco Puja, and Emiliano Macaluso. "Sensory processing during viewing of cinematographic material: Computational modeling and functional neuroimaging." NeuroImage 67 (February 2013): 213–26. http://dx.doi.org/10.1016/j.neuroimage.2012.11.031.

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35

Brzus, Michal, Kevin Knoernschild, Jessica C. Sieren, and Hans J. Johnson. "PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI." Algorithms 16, no. 2 (February 15, 2023): 116. http://dx.doi.org/10.3390/a16020116.

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Translation of basic animal research to find effective methods of diagnosing and treating human neurological disorders requires parallel analysis infrastructures. Small animals such as mice provide exploratory animal disease models. However, many interventions developed using small animal models fail to translate to human use due to physical or biological differences. Recently, large-animal minipigs have emerged in neuroscience due to both their brain similarity and economic advantages. Medical image processing is a crucial part of research, as it allows researchers to monitor their experiments and understand disease development. By pairing four reinforcement learning models and five deep learning UNet segmentation models with existing algorithms, we developed PigSNIPE, a pipeline for the automated handling, processing, and analyzing of large-scale data sets of minipig MR images. PigSNIPE allows for image registration, AC-PC alignment, detection of 19 anatomical landmarks, skull stripping, brainmask and intracranial volume segmentation (DICE 0.98), tissue segmentation (DICE 0.82), and caudate-putamen brain segmentation (DICE 0.8) in under two minutes. To the best of our knowledge, this is the first automated pipeline tool aimed at large animal images, which can significantly reduce the time and resources needed for analyzing minipig neuroimages.

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36

Liu, Zhaowen, Edmund T. Rolls, Zhi Liu, Kai Zhang, Ming Yang, Jingnan Du, Weikang Gong, et al. "Brain annotation toolbox: exploring the functional and genetic associations of neuroimaging results." Bioinformatics 35, no. 19 (March 11, 2019): 3771–78. http://dx.doi.org/10.1093/bioinformatics/btz128.

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Abstract Motivation Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and identify the roots of psychiatric diseases. However, the results from most neuroimaging studies, i.e. activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. Results We describe a brain annotation toolbox that generates functional and genetic annotations for neuroimaging results. The voxel-level functional description from the Neurosynth database and gene expression profile from the Allen Human Brain Atlas are used to generate functional/genetic information for region-level neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and genetic similarity network are highly correlated for major brain atlases. One application of brain annotation toolbox is to help provide functional/genetic annotations for newly discovered regions with unknown functions, e.g. the 97 new regions identified in the Human Connectome Project. Importantly, this toolbox can help understand differences between psychiatric patients and controls, and this is demonstrated using schizophrenia and autism data, for which the functional and genetic annotations for the neuroimaging changes in patients are consistent with each other and help interpret the results. Availability and implementation BAT is implemented as a free and open-source MATLAB toolbox and is publicly available at http://123.56.224.61:1313/post/bat. Supplementary information Supplementary data are available at Bioinformatics online.

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Hu, Xiaoling, Yiwen Wang, Ting Zhao, and Aysegul Gunduz. "Neural Coding for Effective Rehabilitation." BioMed Research International 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/286505.

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Successful neurological rehabilitation depends on accurate diagnosis, effective treatment, and quantitative evaluation. Neural coding, a technology for interpretation of functional and structural information of the nervous system, has contributed to the advancements in neuroimaging, brain-machine interface (BMI), and design of training devices for rehabilitation purposes. In this review, we summarized the latest breakthroughs in neuroimaging from microscale to macroscale levels with potential diagnostic applications for rehabilitation. We also reviewed the achievements in electrocorticography (ECoG) coding with both animal models and human beings for BMI design, electromyography (EMG) interpretation for interaction with external robotic systems, and robot-assisted quantitative evaluation on the progress of rehabilitation programs. Future rehabilitation would be more home-based, automatic, and self-served by patients. Further investigations and breakthroughs are mainly needed in aspects of improving the computational efficiency in neuroimaging and multichannel ECoG by selection of localized neuroinformatics, validation of the effectiveness in BMI guided rehabilitation programs, and simplification of the system operation in training devices.

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Kalavathi, P., M. Senthamilselvi, and V. Prasath. "Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques." Technologies 5, no. 2 (April 18, 2017): 16. http://dx.doi.org/10.3390/technologies5020016.

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39

Macoveanu, J., T. Klingberg, and J. Tegnér. "A biophysical model of multiple-item working memory: A computational and neuroimaging study." Neuroscience 141, no. 3 (January 2006): 1611–18. http://dx.doi.org/10.1016/j.neuroscience.2006.04.080.

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40

Foster, Jonathan K. "Hippocampus, recognition, and recall: A new twist on some old data?" Behavioral and Brain Sciences 22, no. 3 (June 1999): 449–50. http://dx.doi.org/10.1017/s0140525x99272032.

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This commentary attempts to reconcile the predictions of Aggleton & Brown's theoretical framework with previous findings obtained from experimental tests of laboratory animals with selective hippocampal lesions. Adopting a convergent operations approach, the predictions of the model are also related to human neuroimaging data and to other complementary research perspectives (cognitive, computational, psychopharmacological).

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Chatham, Christopher H., Seth A. Herd, Angela M. Brant, Thomas E. Hazy, Akira Miyake, Randy O'Reilly, and Naomi P. Friedman. "From an Executive Network to Executive Control: A Computational Model of the n-back Task." Journal of Cognitive Neuroscience 23, no. 11 (November 2011): 3598–619. http://dx.doi.org/10.1162/jocn_a_00047.

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A paradigmatic test of executive control, the n-back task, is known to recruit a widely distributed parietal, frontal, and striatal “executive network,” and is thought to require an equally wide array of executive functions. The mapping of functions onto substrates in such a complex task presents a significant challenge to any theoretical framework for executive control. To address this challenge, we developed a biologically constrained model of the n-back task that emergently develops the ability to appropriately gate, bind, and maintain information in working memory in the course of learning to perform the task. Furthermore, the model is sensitive to proactive interference in ways that match findings from neuroimaging and shows a U-shaped performance curve after manipulation of prefrontal dopaminergic mechanisms similar to that observed in studies of genetic polymorphisms and pharmacological manipulations. Our model represents a formal computational link between anatomical, functional neuroimaging, genetic, behavioral, and theoretical levels of analysis in the study of executive control. In addition, the model specifies one way in which the pFC, BG, parietal, and sensory cortices may learn to cooperate and give rise to executive control.

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Conti, Alfredo, Nicola Maria Gambadauro, Paolo Mantovani, Canio Pietro Picciano, Vittoria Rosetti, Marcello Magnani, Sebastiano Lucerna, Constantin Tuleasca, Pietro Cortelli, and Giulia Giannini. "A Brief History of Stereotactic Atlases: Their Evolution and Importance in Stereotactic Neurosurgery." Brain Sciences 13, no. 5 (May 21, 2023): 830. http://dx.doi.org/10.3390/brainsci13050830.

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Following the recent acquisition of unprecedented anatomical details through state-of-the-art neuroimaging, stereotactic procedures such as microelectrode recording (MER) or deep brain stimulation (DBS) can now rely on direct and accurately individualized topographic targeting. Nevertheless, both modern brain atlases derived from appropriate histological techniques involving post-mortem studies of human brain tissue and the methods based on neuroimaging and functional information represent a valuable tool to avoid targeting errors due to imaging artifacts or insufficient anatomical details. Hence, they have thus far been considered a reference guide for functional neurosurgical procedures by neuroscientists and neurosurgeons. In fact, brain atlases, ranging from the ones based on histology and histochemistry to the probabilistic ones grounded on data derived from large clinical databases, are the result of a long and inspiring journey made possible thanks to genial intuitions of great minds in the field of neurosurgery and to the technical advancement of neuroimaging and computational science. The aim of this text is to review the principal characteristics highlighting the milestones of their evolution.

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Thielen, Jordy, Sander E. Bosch, Tessa M. van Leeuwen, Marcel A. J. van Gerven, and Rob van Lier. "Neuroimaging Findings on Amodal Completion: A Review." i-Perception 10, no. 2 (March 2019): 204166951984004. http://dx.doi.org/10.1177/2041669519840047.

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Amodal completion is the phenomenon of perceiving completed objects even though physically they are partially occluded. In this review, we provide an extensive overview of the results obtained from a variety of neuroimaging studies on the neural correlates of amodal completion. We discuss whether low-level and high-level cortical areas are implicated in amodal completion; provide an overview of how amodal completion unfolds over time while dissociating feedforward, recurrent, and feedback processes; and discuss how amodal completion is represented at the neuronal level. The involvement of low-level visual areas such as V1 and V2 is not yet clear, while several high-level structures such as the lateral occipital complex and fusiform face area seem invariant to occlusion of objects and faces, respectively, and several motor areas seem to code for object permanence. The variety of results on the timing of amodal completion hints to a mixture of feedforward, recurrent, and feedback processes. We discuss whether the invisible parts of the occluded object are represented as if they were visible, contrary to a high-level representation. While plenty of questions on amodal completion remain, this review presents an overview of the neuroimaging findings reported to date, summarizes several insights from computational models, and connects research of other perceptual completion processes such as modal completion. In all, it is suggested that amodal completion is the solution to deal with various types of incomplete retinal information, and highly depends on stimulus complexity and saliency, and therefore also give rise to a variety of observed neural patterns.

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Li, Xiong, Yangping Qiu, Juan Zhou, and Ziruo Xie. "Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis." Current Genomics 22, no. 8 (December 2021): 564–82. http://dx.doi.org/10.2174/1389202923666211216163049.

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Background: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) in vivo. Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation and even understand the pathogenic mechanism of AD, lots of machine learning methods have been designed and successfully applied to the analysis and processing of large-scale AD biomedical data. Objective: To introduce and summarize the applications and challenges of machine learning methods in Alzheimer's disease multi-source data analysis. Methods: The literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer's disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on. Conclusion: This study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions.

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McIntosh, Randy, Sean Hill, and Olaf Sporns. "Editorial: Focus feature on consciousness and cognition." Network Neuroscience 6, no. 4 (2022): 934–36. http://dx.doi.org/10.1162/netn_e_00273.

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Abstract Consciousness and cognition are an increasing focus of theoretical and experimental research in neuroscience, leveraging the methods and tools of brain dynamics and connectivity. This Focus Feature brings together a collection of articles that examine the various roles of brain networks in computational and dynamic models, and in studies of physiological and neuroimaging processes that underpin and enable behavioral and cognitive function.

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Nomura, Emi M., and Paul J. Reber. "Combining Computational Modeling and Neuroimaging to Examine Multiple Category Learning Systems in the Brain." Brain Sciences 2, no. 2 (April 23, 2012): 176–202. http://dx.doi.org/10.3390/brainsci2020176.

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Mikołajewska, Emilia, Piotr Prokopowicz, YeeKong Chow, Jolanta Masiak, Dariusz Mikołajewski, Grzegorz Marcin Wójcik, Brian Wallace, Andy R. Eugene, and Marcin Olajossy. "From Neuroimaging to Computational Modeling of Burnout: The Traditional versus the Fuzzy Approach—A Review." Applied Sciences 12, no. 22 (November 13, 2022): 11524. http://dx.doi.org/10.3390/app122211524.

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Occupational burnout, manifested by emotional exhaustion, lack of a sense of personal achievement, and depersonalization, is not a new phenomenon, but thusfar, there is no clear definition or diagnostic guidelines. The aim of this article wasto summarize all empirical studies to date that have used medical neuroimaging techniques to provide evidence or links regarding changes in brain function in occupational burnout syndrome from a neuroscientific perspective, and then use these to propose a fuzzy-based computational model of burnout.A comprehensive literature search was conducted in two major databases (PubMed and Medline Complete). The search period was 2006–2021, and searches were limited to the English language. Each article was carefully reviewed and appropriately selected on the basis of raw data, validity of methods used, clarity of results, and scales for measuring burnout. The results showed that the brain structures of patients with job burnout that are associated with emotion, motivation, and empathy weresignificantly different from healthy controls. These altered brain regions included the thalamus, hippocampus, amygdala, caudate, striatum, dorso-lateral prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, anterior insula, inferior frontal cingulate cortex, middle frontal cingulate cortex, temporoparietal junction, and grey matter. Deepening our understanding of how these brain structures are related to burnout will pave the way for better approaches fordiagnosis and intervention. As an alternative to the neuroimaging approach, the paper presents a late proposal of the PLUS (personal living usual satisfaction) parameter. It is based on a fuzzy model, wherein the data source is psychological factors—the same or similar to the neuroimaging approach. As the novel approach to searching for neural burnout mechanisms, we have shown that computational models, including those based on fuzzy logic and artificial neural networks, can play an important role in inferring and predicting burnout. Effective computational models of burnout are possible but need further development to ensure accuracy across different populations. There is also a need to identify mechanisms and clinical indicators of chronic fatigue syndrome, stress, burnout, and natural cognitive changes associated with, for example, ageing, in order to introduce more effective differential diagnosis and screening.

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Cinciute, Sigita. "Translating the hemodynamic response: why focused interdisciplinary integration should matter for the future of functional neuroimaging." PeerJ 7 (March 25, 2019): e6621. http://dx.doi.org/10.7717/peerj.6621.

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The amount of information acquired with functional neuroimaging techniques, particularly fNIRS and fMRI, is rapidly growing and has enormous potential for studying human brain functioning. Therefore, many scientists focus on solving computational neuroimaging and Big Data issues to advance the discipline. However, the main obstacle—the accurate translation of the hemodynamic response (HR) by the investigation of a physiological phenomenon called neurovascular coupling—is still not fully overcome and, more importantly, often overlooked in this context. This article provides a brief and critical overview of significant findings from cellular biology and in vivo brain physiology with a focus on advancing existing HR modelling paradigms. A brief historical timeline of these disciplines of neuroscience is presented for readers to grasp the concept better, and some possible solutions for further scientific discussion are provided.

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Zhao, Yuxuan, David S. Matteson, Stewart H. Mostofsky, Mary Beth Nebel, and Benjamin B. Risk. "Group linear non-Gaussian component analysis with applications to neuroimaging." Computational Statistics & Data Analysis 171 (July 2022): 107454. http://dx.doi.org/10.1016/j.csda.2022.107454.

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Li, Xiang, Ning Guo, and Quanzheng Li. "Functional Neuroimaging in the New Era of Big Data." Genomics, Proteomics & Bioinformatics 17, no. 4 (August 2019): 393–401. http://dx.doi.org/10.1016/j.gpb.2018.11.005.

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