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Статті в журналах з теми "Brain encoding"

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Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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1

Heinze, S., G. Sartory, B. W. Mueller, M. Forsting, and M. Jueptner. "Brain activation during verbal encoding." Schizophrenia Research 60, no. 1 (March 2003): 220. http://dx.doi.org/10.1016/s0920-9964(03)81186-6.

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2

Duss, Simone B., Thomas P. Reber, Jürgen Hänggi, Simon Schwab, Roland Wiest, René M. Müri, Peter Brugger, Klemens Gutbrod, and Katharina Henke. "Unconscious relational encoding depends on hippocampus." Brain 137, no. 12 (September 27, 2014): 3355–70. http://dx.doi.org/10.1093/brain/awu270.

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3

Henin, Simon, Anita Shankar, Helen Borges, Adeen Flinker, Werner Doyle, Daniel Friedman, Orrin Devinsky, György Buzsáki, and Anli Liu. "Spatiotemporal dynamics between interictal epileptiform discharges and ripples during associative memory processing." Brain 144, no. 5 (April 23, 2021): 1590–602. http://dx.doi.org/10.1093/brain/awab044.

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Abstract We describe the spatiotemporal course of cortical high-gamma activity, hippocampal ripple activity and interictal epileptiform discharges during an associative memory task in 15 epilepsy patients undergoing invasive EEG. Successful encoding trials manifested significantly greater high-gamma activity in hippocampus and frontal regions. Successful cued recall trials manifested sustained high-gamma activity in hippocampus compared to failed responses. Hippocampal ripple rates were greater during successful encoding and retrieval trials. Interictal epileptiform discharges during encoding were associated with 15% decreased odds of remembering in hippocampus (95% confidence interval 6–23%). Hippocampal interictal epileptiform discharges during retrieval predicted 25% decreased odds of remembering (15–33%). Odds of remembering were reduced by 25–52% if interictal epileptiform discharges occurred during the 500–2000 ms window of encoding or by 41% during retrieval. During encoding and retrieval, hippocampal interictal epileptiform discharges were followed by a transient decrease in ripple rate. We hypothesize that interictal epileptiform discharges impair associative memory in a regionally and temporally specific manner by decreasing physiological hippocampal ripples necessary for effective encoding and recall. Because dynamic memory impairment arises from pathological interictal epileptiform discharge events competing with physiological ripples, interictal epileptiform discharges represent a promising therapeutic target for memory remediation in patients with epilepsy.

4

Fazio, P., A. Cantagallo, L. Craighero, A. D'Ausilio, A. C. Roy, T. Pozzo, F. Calzolari, E. Granieri, and L. Fadiga. "Encoding of human action in Broca's area." Brain 132, no. 7 (May 14, 2009): 1980–88. http://dx.doi.org/10.1093/brain/awp118.

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5

Staurenghi, Erica, Gabriella Testa, Valerio Leoni, Rebecca Cecci, Lucrezia Floro, Serena Giannelli, Eugenio Barone, et al. "Altered Brain Cholesterol Machinery in a Down Syndrome Mouse Model: A Possible Common Feature with Alzheimer’s Disease." Antioxidants 13, no. 4 (April 3, 2024): 435. http://dx.doi.org/10.3390/antiox13040435.

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Down syndrome (DS) is a complex chromosomal disorder considered as a genetically determined form of Alzheimer’s disease (AD). Maintenance of brain cholesterol homeostasis is essential for brain functioning and development, and its dysregulation is associated with AD neuroinflammation and oxidative damage. Brain cholesterol imbalances also likely occur in DS, concurring with the precocious AD-like neurodegeneration. In this pilot study, we analyzed, in the brain of the Ts2Cje (Ts2) mouse model of DS, the expression of genes encoding key enzymes involved in cholesterol metabolism and of the levels of cholesterol and its main precursors and products of its metabolism (i.e., oxysterols). The results showed, in Ts2 mice compared to euploid mice, the downregulation of the transcription of the genes encoding the enzymes 3-hydroxy-3-methylglutaryl-CoA reductase and 24-dehydrocholesterol reductase, the latter originally recognized as an indicator of AD, and the consequent reduction in total cholesterol levels. Moreover, the expression of genes encoding enzymes responsible for brain cholesterol oxidation and the amounts of the resulting oxysterols were modified in Ts2 mouse brains, and the levels of cholesterol autoxidation products were increased, suggesting an exacerbation of cerebral oxidative stress. We also observed an enhanced inflammatory response in Ts2 mice, underlined by the upregulation of the transcription of the genes encoding for α-interferon and interleukin-6, two cytokines whose synthesis is increased in the brains of AD patients. Overall, these results suggest that DS and AD brains share cholesterol cycle derangements and altered oxysterol levels, which may contribute to the oxidative and inflammatory events involved in both diseases.

6

Rudenga, K. J., R. Sinha, and D. M. Small. "Stress impacts brain encoding of food." Appetite 52, no. 3 (June 2009): 855. http://dx.doi.org/10.1016/j.appet.2009.04.167.

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7

Turella, Luca, Raffaella Rumiati, and Angelika Lingnau. "Hierarchical Action Encoding Within the Human Brain." Cerebral Cortex 30, no. 5 (January 14, 2020): 2924–38. http://dx.doi.org/10.1093/cercor/bhz284.

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Abstract Humans are able to interact with objects with extreme flexibility. To achieve this ability, the brain does not only control specific muscular patterns, but it also needs to represent the abstract goal of an action, irrespective of its implementation. It is debated, however, how abstract action goals are implemented in the brain. To address this question, we used multivariate pattern analysis of functional magnetic resonance imaging data. Human participants performed grasping actions (precision grip, whole hand grip) with two different wrist orientations (canonical, rotated), using either the left or right hand. This design permitted to investigate a hierarchical organization consisting of three levels of abstraction: 1) “concrete action” encoding; 2) “effector-dependent goal” encoding (invariant to wrist orientation); and 3) “effector-independent goal” encoding (invariant to effector and wrist orientation). We found that motor cortices hosted joint encoding of concrete actions and of effector-dependent goals, while the parietal lobe housed a convergence of all three representations, comprising action goals within and across effectors. The left lateral occipito-temporal cortex showed effector-independent goal encoding, but no convergence across the three levels of representation. Our results support a hierarchical organization of action encoding, shedding light on the neural substrates supporting the extraordinary flexibility of human hand behavior.

8

Miller, Michael B., Alan Kingstone, and Michael S. Gazzaniga. "Hemispheric Encoding Asymmetry is More Apparent Than Real." Journal of Cognitive Neuroscience 14, no. 5 (July 1, 2002): 702–8. http://dx.doi.org/10.1162/08989290260138609.

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Previous neuroimaging studies have claimed a left hemisphere specialization for episodic “encoding” and a right hemisphere specialization for episodic “retrieval.” Yet studies of split-brain patients indicate relatively minor memory impairment after disconnection of the two hemispheres. This suggests that both hemispheres are capable of encoding and retrieval. In the present experiment, we examined the possible limits on encoding capacity of each hemisphere by manipulating the “depth” of processing during the encoding of unfamiliar faces and familiar words in the left and right hemispheres of two split-brain patients. Results showed that only the left hemisphere benefited from deeper (more elaborate) encoding of familiar words, and only the right hemisphere benefited from deeper encoding of unfamiliar faces. Our findings are consistent with the view that hemispheric asymmetries in episodic encoding are related to hemisphere-specific processing of particular stimuli. Convergent with recent neuroimaging studies, these results with split-brain patients also suggest that these hemispheric differences are not due to unique specializations in each half brain for encoding memories, but rather, are due to preferential recruitment of the synaptically closer prefrontal cortex to posterior regions processing material-specific information.

9

Tulving, Endel, Hans J. Markowitsch, Shitij Kapur, Reza Habib, and Sylvain Houle. "Novelty encoding networks in the human brain." NeuroReport 5, no. 18 (December 1994): 2525–28. http://dx.doi.org/10.1097/00001756-199412000-00030.

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10

Bird, C. M., S. C. Berens, A. J. Horner, and A. Franklin. "Categorical encoding of color in the brain." Proceedings of the National Academy of Sciences 111, no. 12 (March 3, 2014): 4590–95. http://dx.doi.org/10.1073/pnas.1315275111.

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11

Petersson, Karl Magnus, Johan Sandblom, Christina Elfgren, and Martin Ingvar. "Instruction-specific brain activations during episodic encoding." NeuroImage 20, no. 3 (November 2003): 1795–810. http://dx.doi.org/10.1016/s1053-8119(03)00414-2.

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12

Yoder, Ryan M., Benjamin J. Clark, and Jeffrey S. Taube. "Origins of landmark encoding in the brain." Trends in Neurosciences 34, no. 11 (November 2011): 561–71. http://dx.doi.org/10.1016/j.tins.2011.08.004.

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13

Navratilova, Edita, Christopher W. Atcherley, and Frank Porreca. "Brain Circuits Encoding Reward from Pain Relief." Trends in Neurosciences 38, no. 11 (November 2015): 741–50. http://dx.doi.org/10.1016/j.tins.2015.09.003.

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14

Van Leemput, K. "Encoding Probabilistic Brain Atlases Using Bayesian Inference." IEEE Transactions on Medical Imaging 28, no. 6 (June 2009): 822–37. http://dx.doi.org/10.1109/tmi.2008.2010434.

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15

Kaski, Diego, Shamim Quadir, Yuliya Nigmatullina, Paresh A. Malhotra, Adolfo M. Bronstein, and Barry M. Seemungal. "Temporoparietal encoding of space and time during vestibular-guided orientation." Brain 139, no. 2 (December 30, 2015): 392–403. http://dx.doi.org/10.1093/brain/awv370.

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16

Morcom, A. M., C. D. Good, R. S. J. Frackowiak, and M. D. Rugg. "Age effects on the neural correlates of successful memory encoding." Brain 126, no. 1 (January 1, 2003): 213–29. http://dx.doi.org/10.1093/brain/awg020.

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17

Jeong, Dong-Ki, Hyoung-Gook Kim, and Jin-Young Kim. "Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions." Bioengineering 10, no. 9 (September 4, 2023): 1040. http://dx.doi.org/10.3390/bioengineering10091040.

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To understand human emotional states, local activities in various regions of the cerebral cortex and the interactions among different brain regions must be considered. This paper proposes a hierarchical emotional context feature learning model that improves multichannel electroencephalography (EEG)-based emotion recognition by learning spatiotemporal EEG features from a local brain region to a global brain region. The proposed method comprises a regional brain-level encoding module, a global brain-level encoding module, and a classifier. First, multichannel EEG signals grouped into nine regions based on the functional role of the brain are input into a regional brain-level encoding module to learn local spatiotemporal information. Subsequently, the global brain-level encoding module improved emotional classification performance by integrating local spatiotemporal information from various brain regions to learn the global context features of brain regions related to emotions. Next, we applied a two-layer bidirectional gated recurrent unit (BGRU) with self-attention to the regional brain-level module and a one-layer BGRU with self-attention to the global brain-level module. Experiments were conducted using three datasets to evaluate the EEG-based emotion recognition performance of the proposed method. The results proved that the proposed method achieves superior performance by reflecting the characteristics of multichannel EEG signals better than state-of-the-art methods.

18

Deneris, E. S., J. Boulter, J. Connolly, E. Wada, K. Wada, D. Goldman, L. W. Swanson, J. Patrick, and S. Heinemann. "Genes encoding neuronal nicotinic acetylcholine receptors." Clinical Chemistry 35, no. 5 (May 1, 1989): 731–37. http://dx.doi.org/10.1093/clinchem/35.5.731.

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Abstract Four genes (alpha 2, alpha 3, alpha 4, and beta 2), which encode proteins homologous to the Torpedo electric organ and vertebrate muscle nicotinic acetylcholine receptors, have been identified by cloning rat brain cDNAs. Injection of transcripts derived from these cDNAs into Xenopus laevis oocytes results in the formation of three nicotinic acetylcholine receptors. Two of these receptors, alpha 3/beta 2 and alpha 4/beta 2, have the characteristics of ganglionic nicotinic receptors. The third (alpha 2/beta 2) exhibits a previously undescribed pharmacology and thus represents a novel subtype that may be expressed in the brain. The wide distribution of alpha 2, alpha 3, alpha 4, and beta 2 transcripts in the brain indicates that neuronal nicotinic acetylcholine receptors are a major neurotransmitter receptor system.

19

Fletcher, P. "The functional roles of prefrontal cortex in episodic memory. I. Encoding." Brain 121, no. 7 (July 1, 1998): 1239–48. http://dx.doi.org/10.1093/brain/121.7.1239.

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20

Anderson, Nicole D., Tetsuya Iidaka, Roberto Cabeza, Shitij Kapur, Anthony R. McIntosh, and Fergus I. M. Craik. "The Effects of Divided Attention on Encoding- and Retrieval-Related Brain Activity: A PET Study of Younger and Older Adults." Journal of Cognitive Neuroscience 12, no. 5 (September 2000): 775–92. http://dx.doi.org/10.1162/089892900562598.

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Divided attention (DA) disrupts episodic encoding, but has little effect on episodic retrieval. Furthermore, normal aging is associated with episodic memory impairments, and when young adults are made to encode information under DA conditions, their memory performance is reduced and resembles that of old adults working under full attention (FA) conditions. Together, these results suggest a common neurocognitive mechanism by which aging and DA during encoding disrupt memory performance. In the current study, we used PET to investigate younger and older adults' brain activity during encoding and retrieval under FA and DA conditions. In FA conditions, the old adults showed reduced activity in prefrontal regions that younger adults activated preferentially during encoding or retrieval, as well as increased activity in prefrontal regions young adults did not activate. These results indicate that prefrontal functional specificity of episodic memory is reduced by aging. During encoding, DA reduced memory performance, and reduced brain activity in left-prefrontal and medial-temporal lobe regions for both age groups, indicating that DA during encoding interferes with encoding processes that lead to better memory performance. During retrieval, memory performance and retrieval-related brain activity were relatively immune to DA for both age groups, suggesting that DA during retrieval does not interfere with the brain systems necessary for successful retrieval. Finally, left inferior prefrontal activity was reduced similarly by aging and by DA during encoding, suggesting that the behavioral correspondence between these effects is the result of a reduced ability to engage in elaborate encoding operations.

21

Gallistel, C. R. "Finding numbers in the brain." Philosophical Transactions of the Royal Society B: Biological Sciences 373, no. 1740 (January 2018): 20170119. http://dx.doi.org/10.1098/rstb.2017.0119.

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After listing functional constraints on what numbers in the brain must do, I sketch the two's complement fixed-point representation of numbers because it has stood the test of time and because it illustrates the non-obvious ways in which an effective coding scheme may operate. I briefly consider its neurobiological implementation. It is easier to imagine its implementation at the cell-intrinsic molecular level, with thermodynamically stable, volumetrically minimal polynucleotides encoding the remembered numbers, than at the circuit level, with plastic synapses encoding them. This article is part of a discussion meeting issue ‘The origins of numerical abilities’.

22

Friedman, David, and Ray Johnson. "Inefficient Encoding as an Explanation for Age-Related Deficits in Recollection-Based Processing." Journal of Psychophysiology 28, no. 3 (September 1, 2014): 148–61. http://dx.doi.org/10.1027/0269-8803/a000122.

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A cardinal feature of aging is a decline in episodic memory (EM). Nevertheless, there is evidence that some older adults may be able to “compensate” for failures in recollection-based processing by recruiting brain regions and cognitive processes not normally recruited by the young. We review the evidence suggesting that age-related declines in EM performance and recollection-related brain activity (left-parietal EM effect; LPEM) are due to altered processing at encoding. We describe results from our laboratory on differences in encoding- and retrieval-related activity between young and older adults. We then show that, relative to the young, in older adults brain activity at encoding is reduced over a brain region believed to be crucial for successful semantic elaboration in a 400–1,400-ms interval (left inferior prefrontal cortex, LIPFC; Johnson, Nessler, & Friedman, 2013 ; Nessler, Friedman, Johnson, & Bersick, 2007 ; Nessler, Johnson, Bersick, & Friedman, 2006 ). This reduced brain activity is associated with diminished subsequent recognition-memory performance and the LPEM at retrieval. We provide evidence for this premise by demonstrating that disrupting encoding-related processes during this 400–1,400-ms interval in young adults affords causal support for the hypothesis that the reduction over LIPFC during encoding produces the hallmarks of an age-related EM deficit: normal semantic retrieval at encoding, reduced subsequent episodic recognition accuracy, free recall, and the LPEM. Finally, we show that the reduced LPEM in young adults is associated with “additional” brain activity over similar brain areas as those activated when older adults show deficient retrieval. Hence, rather than supporting the compensation hypothesis, these data are more consistent with the scaffolding hypothesis, in which the recruitment of additional cognitive processes is an adaptive response across the life span in the face of momentary increases in task demand due to poorly-encoded episodic memories.

23

Friedman, David, Doreen Nessler, and Ray Johnson. "Memory Encoding and Retrieval in the Aging Brain." Clinical EEG and Neuroscience 38, no. 1 (January 2007): 2–7. http://dx.doi.org/10.1177/155005940703800105.

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24

Xiao, Ruiting, Toshimitsu Takahashi, Masahiko Inase, Takashi Tsukiura, Kenji Kawano, and Toshio Iijima. "Brain activations during encoding pictures of different familiarities." NeuroImage 11, no. 5 (May 2000): S387. http://dx.doi.org/10.1016/s1053-8119(00)91318-1.

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25

Rolls, Edmund T., and Alessandro Treves. "The neuronal encoding of information in the brain." Progress in Neurobiology 95, no. 3 (November 2011): 448–90. http://dx.doi.org/10.1016/j.pneurobio.2011.08.002.

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26

Tallot, Lucille, and Valérie Doyère. "Neural encoding of time in the animal brain." Neuroscience & Biobehavioral Reviews 115 (August 2020): 146–63. http://dx.doi.org/10.1016/j.neubiorev.2019.12.033.

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27

Haxby, J. V., L. G. Ungerleider, B. Horwitz, J. M. Maisog, S. I. Rapoport, and C. L. Grady. "Face encoding and recognition in the human brain." Proceedings of the National Academy of Sciences 93, no. 2 (January 23, 1996): 922–27. http://dx.doi.org/10.1073/pnas.93.2.922.

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28

Bueti, D., B. Bahrami, V. Walsh, and G. Rees. "Encoding of Temporal Probabilities in the Human Brain." Journal of Neuroscience 30, no. 12 (March 24, 2010): 4343–52. http://dx.doi.org/10.1523/jneurosci.2254-09.2010.

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29

Attali, Eve, Francesca De Anna, Bruno Dubois, and Gianfranco Dalla Barba. "Confabulation in Alzheimer's disease: poor encoding and retrieval of over-learned information." Brain 132, no. 1 (October 1, 2008): 204–12. http://dx.doi.org/10.1093/brain/awn241.

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30

Pasquereau, Benjamin, Mahlon R. DeLong, and Robert S. Turner. "Primary motor cortex of the parkinsonian monkey: altered encoding of active movement." Brain 139, no. 1 (October 21, 2015): 127–43. http://dx.doi.org/10.1093/brain/awv312.

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31

Daselaar, S. M., D. J. Veltman, S. A. R. B. Rombouts, J. G. W. Raaijmakers, and C. Jonker. "Neuroanatomical correlates of episodic encoding and retrieval in young and elderly subjects." Brain 126, no. 1 (January 1, 2003): 43–56. http://dx.doi.org/10.1093/brain/awg005.

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32

Herholz, Karl, Patricia Ehlen, Josef Kessler, Timm Strotmann, Elke Kalbe, and Hans-Joachim Markowitsch. "Brain activation during face-name encoding and the effects of age and encoding success." NeuroImage 11, no. 5 (May 2000): S379. http://dx.doi.org/10.1016/s1053-8119(00)91310-7.

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33

Herting, Megan M., and Bonnie J. Nagel. "Differences in Brain Activity during a Verbal Associative Memory Encoding Task in High- and Low-fit Adolescents." Journal of Cognitive Neuroscience 25, no. 4 (April 2013): 595–612. http://dx.doi.org/10.1162/jocn_a_00344.

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Aerobic fitness is associated with better memory performance as well as larger volumes in memory-related brain regions in children, adolescents, and elderly. It is unclear if aerobic exercise also influences learning and memory functional neural circuitry. Here, we examine brain activity in 17 high-fit (HF) and 17 low-fit (LF) adolescents during a subsequent memory encoding paradigm using fMRI. Despite similar memory performance, HF and LF youth displayed a number of differences in memory-related and default mode (DMN) brain regions during encoding later remembered versus forgotten word pairs. Specifically, HF youth displayed robust deactivation in DMN areas, including the ventral medial PFC and posterior cingulate cortex, whereas LF youth did not show this pattern. Furthermore, LF youth showed greater bilateral hippocampal and right superior frontal gyrus activation during encoding of later remembered versus forgotten word pairs. Follow-up task-dependent functional correlational analyses showed differences in hippocampus and DMN activity coupling during successful encoding between the groups, suggesting aerobic fitness during adolescents may impact functional connectivity of the hippocampus and DMN during memory encoding. To our knowledge, this study is the first to examine the influence of aerobic fitness on hippocampal function and memory-related neural circuitry using fMRI. Taken together with previous research, these findings suggest aerobic fitness can influence not only memory-related brain structure, but also brain function.

34

Thotipalayam Andavan Mohanprakash, Madhumitha Kulandaivel, Samuel Rosaline, Pasham Nithish Reddy, Shankar Nayak Bhukya, Ravindra Namdeorao Jogekar, and Rengaraj Gurumoorthy Vidhya. "Detection of Brain Cancer through Enhanced Particle Swarm Optimization in Artificial Intelligence Approach." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 2 (November 1, 2023): 174–86. http://dx.doi.org/10.37934/araset.33.2.174186.

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Brain cancer is deadly and requires prompt detection and treatment. We propose a complete brain cancer detection method using binary encoding, adaptive thresholding, edge-based segmentation, particle swarm optimization (PSO), wavelet transform, and neural networks. First, binary encoding converts categorical patient data and medical history information into binary vectors for fast analysis. Adaptive thresholding then handles image lighting and contrast to optimize brain image segmentation. Brain tumor boundaries are identified via edge-based segmentation. This method isolates tumor areas for investigation by recognizing significant pixel intensities. Particle swarm optimization optimizes segmentation algorithm settings, enhancing efficiency and accuracy. Wavelet transform captures local and global brain picture changes, extracting tumor-related information. This method gives a complete visual representation, improving categorization. Finally, utilizing the collected attributes, a neural network model classifies brain pictures as malignant or non-cancerous. The neural network learns the complicated correlations between retrieved variables and brain cancer to classify accurately and automatically. A dataset of brain pictures, comprising malignant and non-cancerous instances, evaluates the proposed approach. The proposed approach accurately detects brain tumors in experiments. Binary encoding, adaptive thresholding, edge-based segmentation, particle swarm optimization, wavelet transform, and neural networks can help medical professionals diagnose and treat brain cancer early.

35

Hasan, Khader M., Dennis L. Parker, and Andrew L. Alexander. "MAGNETIC RESONANCE WATER SELF-DIFFUSION TENSOR ENCODING OPTIMIZATION METHODS FOR FULL BRAIN ACQUISITION." Image Analysis & Stereology 21, no. 2 (May 3, 2011): 87. http://dx.doi.org/10.5566/ias.v21.p87-96.

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Water diffusion tensor magnetic resonance imaging (DT-MRI) is a non-invasive and sensitive modality that is becoming increasingly popular in diagnostic radiology. DT-MRI provides in vivo directional information about the organization and microdynamics of deep brain tissue that is not available by other MRI relaxationbased methods. The DT-MRI experiment involves a host of imaging and diffusion parameters that influence the efficiency (signal-to-noise ratio per unit time), accuracy, and specificity of the information sought. These parameters may include typical imaging parameters such as TE, TR, slice thickness, sampling rate, etc. The DTI relevant parameter space includes pulse duration, separation, direction, number of directions (Ne), order, sign and strength of the diffusion encoding gradient pulses. The goal of this work is to present and compare different tensor encoding strategies used to obtain the DT-MRI information for the whole brain. In this paper an evaluation of tensor encoding advantage is presented using a multi-dimensional non-parametric Bootstrap resampling method. This work also explores the relationship between different tensor encoding schemes using the analytical encoding approach. This work shows that the minimum energy optimization approach can produce uniformly distributed tensor encoding that are comparable to the icosahedral sets. The minimum condition encoding sets are not uniformly distributed and are shown to be suboptimal and related to a commonly used heuristic tensor encoding set. This work shows that the icosahedral set is the only uniformly distributed set with Ne = 6. At equal imaging time, the Bootstrap experiments show that optimal tensor encoding sets can have 6 < Ne < 24.

36

Lewis, S. A., and N. J. Cowan. "Genetics, evolution, and expression of the 68,000-mol-wt neurofilament protein: isolation of a cloned cDNA probe." Journal of Cell Biology 100, no. 3 (March 1, 1985): 843–50. http://dx.doi.org/10.1083/jcb.100.3.843.

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A 1.2-kilobase (kb) cDNA clone (NF68) encoding the mouse 68,000-mol-wt neurofilament protein is described. The clone was isolated from a mouse brain cDNA library by low-stringency cross-hybridization with a cDNA probe encoding mouse glial fibrillary acidic protein (Lewis et al., 1984, Proc. Natl. Acad. Sci. USA., 81:2743-2746). The identity of NF68 was established by hybrid selection using mouse brain polyA+ mRNA, and cell-free translation of the selected mRNA species. The cell-free translation product co-migrated with authentic 68,000-mol-wt neurofilament protein on an SDS/polyacrylamide gel, and was immunoprecipitable with a monospecific rabbit anti-bovine neurofilament antiserum. In addition, DNA sequence analysis of NF68 showed 90% homology at the amino acid level compared with the sequence of the porcine 68,000-mol-wt neurofilament protein. At high stringency, NF68 detects a single genomic sequence encoding the mouse 68,000-mol-wt neurofilament protein. Two mRNA species of 2.5 kb and 4.0 kb are transcribed from the single gene in mouse brain. The level of expression of these mRNAs remains almost constant in postnatal mouse brains of all ages and, indeed, in the adult. At reduced stringency, NF68 detects a number of mRNAs that are expressed in mouse brain, one of which encodes the 150,000-mol-wt neurofilament protein. The NF68 probe cross-hybridizes at high stringency with genomic sequences in species as diverse as human, chicken, and (weakly) frog, but not with DNA from Drosophila or sea urchin.

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Becker, Nina, Grégoria Kalpouzos, Jonas Persson, Erika J. Laukka, and Yvonne Brehmer. "Differential Effects of Encoding Instructions on Brain Activity Patterns of Item and Associative Memory." Journal of Cognitive Neuroscience 29, no. 3 (March 2017): 545–59. http://dx.doi.org/10.1162/jocn_a_01062.

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Evidence from neuroimaging studies suggests a critical role of hippocampus and inferior frontal gyrus (IFG) in associative relative to item encoding. Here, we investigated similarities and differences in functional brain correlates for associative and item memory as a function of encoding instruction. Participants received either incidental (animacy judgments) or intentional encoding instructions while fMRI was employed during the encoding of associations and items. In a subsequent recognition task, memory performance of participants receiving intentional encoding instructions was higher compared with those receiving incidental encoding instructions. Furthermore, participants remembered more items than associations, regardless of encoding instruction. Greater brain activation in the left anterior hippocampus was observed for intentionally compared with incidentally encoded associations, although activity in this region was not modulated by the type of instruction for encoded items. Furthermore, greater activity in the left anterior hippocampus and left IFG was observed during intentional associative compared with item encoding. The same regions were related to subsequent memory of intentionally encoded associations and were thus task relevant. Similarly, connectivity of the anterior hippocampus to the right superior temporal lobe and IFG was uniquely linked to subsequent memory of intentionally encoded associations. Our study demonstrates the differential involvement of anterior hippocampus in intentional relative to incidental associative encoding. This finding likely reflects that the intent to remember triggers a specific binding process accomplished by this region.

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Golby, A. "Memory encoding in Alzheimer's disease: an fMRI study of explicit and implicit memory." Brain 128, no. 4 (February 2, 2005): 773–87. http://dx.doi.org/10.1093/brain/awh400.

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39

Herrojo Ruiz, María, Marco Rusconi, Christof Brücke, John-Dylan Haynes, Thomas Schönecker, and Andrea A. Kühn. "Encoding of sequence boundaries in the subthalamic nucleus of patients with Parkinson’s disease." Brain 137, no. 10 (July 16, 2014): 2715–30. http://dx.doi.org/10.1093/brain/awu191.

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40

Sun, Shun-Chang, Di Ma, Mei-Yi Li, Ru-Xu Zhang, Cheng Huang, Hua-Jie Huang, Yong-zhi Xie, et al. "Mutations in C1orf194, encoding a calcium regulator, cause dominant Charcot-Marie-Tooth disease." Brain 142, no. 8 (June 14, 2019): 2215–29. http://dx.doi.org/10.1093/brain/awz151.

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Abstract Charcot-Marie-Tooth disease is a hereditary motor and sensory neuropathy exhibiting great clinical and genetic heterogeneity. Here, the identification of two heterozygous missense mutations in the C1orf194 gene at 1p21.2-p13.2 with Charcot-Marie-Tooth disease are reported. Specifically, the p.I122N mutation was the cause of an intermediate form of Charcot-Marie-Tooth disease, and the p.K28I missense mutation predominately led to the demyelinating form. Functional studies demonstrated that the p.K28I variant significantly reduced expression of the protein, but the p.I122N variant increased. In addition, the p.I122N mutant protein exhibited the aggregation in neuroblastoma cell lines and the patient’s peroneal nerve. Either gain-of-function or partial loss-of-function mutations to C1ORF194 can specify different causal mechanisms responsible for Charcot-Marie-Tooth disease with a wide range of clinical severity. Moreover, a knock-in mouse model confirmed that the C1orf194 missense mutation p.I121N led to impairments in motor and neuromuscular functions, and aberrant myelination and axonal phenotypes. The loss of normal C1ORF194 protein altered intracellular Ca2+ homeostasis and upregulated Ca2+ handling regulatory proteins. These findings describe a novel protein with vital functions in peripheral nervous systems and broaden the causes of Charcot-Marie-Tooth disease, which open new avenues for the diagnosis and treatment of related neuropathies.

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Otten, Leun J., Josefin Sveen, and Angela H. Quayle. "Distinct Patterns of Neural Activity during Memory Formation of Nonwords versus Words." Journal of Cognitive Neuroscience 19, no. 11 (November 2007): 1776–89. http://dx.doi.org/10.1162/jocn.2007.19.11.1776.

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Research into the neural underpinnings of memory formation has focused on the encoding of familiar verbal information. Here, we address how the brain supports the encoding of novel information that does not have meaning. Electrical brain activity was recorded from the scalps of healthy young adults while they performed an incidental encoding task (syllable judgments) on separate series of words and “nonwords” (nonsense letter strings that are orthographically legal and pronounceable). Memory for the items was then probed with a recognition memory test. For words as well as nonwords, event-related potentials differed depending on whether an item would subsequently be remembered or forgotten. However, the polarity and timing of the effect varied across item type. For words, subsequently remembered items showed the usually observed positive-going, frontally distributed modulation from around 600 msec after word onset. For nonwords, by contrast, a negative-going, spatially widespread modulation predicted encoding success from 1000 msec onward. Nonwords also showed a modulation shortly after item onset. These findings imply that the brain supports the encoding of familiar and unfamiliar letter strings in qualitatively different ways, including the engagement of distinct neural activity at different points in time. The processing of semantic attributes plays an important role in the encoding of words and the associated positive frontal modulation.

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Esposito, Alessandro, Antonio Falace, Matias Wagner, Moran Gal, Davide Mei, Valerio Conti, Tiziana Pisano, et al. "Biallelic DMXL2 mutations impair autophagy and cause Ohtahara syndrome with progressive course." Brain 142, no. 12 (November 5, 2019): 3876–91. http://dx.doi.org/10.1093/brain/awz326.

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Esposito et al. identify biallelic loss-of-function mutations in DMXL2, encoding a v-ATPase regulatory protein, in three sibling pairs exhibiting Ohtahara syndrome with a progressive course. Patient-derived fibroblasts and Dmxl2-silenced mouse hippocampal neurons show defective lysosomal function and autophagy, resulting in the latter in impaired neuronal development and synapse formation.

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Xu, Lichao, Minpeng Xu, Tzyy-Ping Jung, and Dong Ming. "Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface." Cognitive Neurodynamics 15, no. 4 (April 10, 2021): 569–84. http://dx.doi.org/10.1007/s11571-021-09676-z.

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Lang, YiRan, Ping Du, and Hyung-Cheul Shin. "Encoding-based brain-computer interface controlled by non-motor area of rat brain." Science China Life Sciences 54, no. 9 (September 2011): 841–53. http://dx.doi.org/10.1007/s11427-011-4214-6.

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Bottary, R. M., S. M. Kark, R. T. Daley, J. D. Payne, and E. A. Kensinger. "0111 Emotional Memory-Associated Voxel-Extent Reactivation During Episodic Memory Retrieval Varies as a Function of Post-Learning Sleep." Sleep 43, Supplement_1 (April 2020): A44. http://dx.doi.org/10.1093/sleep/zsaa056.109.

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Abstract Introduction Slow wave sleep (SWS) and rapid eye-movement (REM) sleep enhance neutral and emotional memory consolidation, respectively. Emotional episodic memory retrieval is also enhanced when encoding-specific functional brain patterns are reactivated at retrieval, especially in ventral visual stream and frontal brain regions as well as amygdala. Here we investigate how sleep impacts the association between memory-dependent brain pattern reactivation and episodic memory retrieval. Methods Healthy adults (N = 22; 11F, 11M; age: 19–29 years) were scanned during an incidental encoding task and a surprise recognition memory task 24h later. Overnight sleep was monitored with polysomnography. During encoding, participants viewed line drawings of negative, neutral, and positive images, each followed by their full-colored photo. At recognition, participants distinguished new from encoded line drawings. Brain reactivation was measured at the single-subject level as the percentage of voxels activated at encoding that were also activated during successful recognition (reactivation%); this metric was calculated independently in whole-brain and 3 ROI-based maps (inferior temporal lobe (ITL), medial prefrontal cortex, and amygdala). Multiple linear regression was performed to predict memory performance from functional brain reactivation and sleep physiology. Results In whole-brain analyses, the association between negative memory performance and reactivation% decreased with greater REM sleep amount. This interaction approached significance for positive, but was not significant for neutral, memory performance. Additionally, the association between neutral, but not emotional, memory performance and reactivation% decreased with greater amounts of SWS sleep. In ROI-based analyses, positive, but not negative or neutral, memory performance was independently predicted by REM sleep amount and ITL reactivation%. No effects of SWS amount were observed in ROI-based analyses. Conclusion Greater amounts of sleep decreased the association between brain reactivation and memory performance. Sufficient sleep may change cortical representations of episodic memories, resulting in less reliance on encoding-related reactivation during memory retrieval. Support NSF Grant BCS 1539361

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Witte, Matthias. "Role of local field potentials in encoding hand movement kinematics." Journal of Neurophysiology 106, no. 4 (October 2011): 1601–3. http://dx.doi.org/10.1152/jn.00269.2011.

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How the brain orchestrates the musculoskeletal system to produce complex three-dimensional movements is still poorly understood. Despite first promising results in brain-machine interfaces that translate cortical activity to control output, there is an ongoing debate about which brain signals provide richest information related to movement planning and execution. Novel results by Bansal and colleagues (2011) now suggest that neuronal spiking and local field potentials jointly encode kinematics during skilled reach and grasp movements.

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Hazeltine, E. "Attention and stimulus characteristics determine the locus of motor- sequence encoding. A PET study." Brain 120, no. 1 (January 1, 1997): 123–40. http://dx.doi.org/10.1093/brain/120.1.123.

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Golby, A. J. "Material-specific lateralization in the medial temporal lobe and prefrontal cortex during memory encoding." Brain 124, no. 9 (September 1, 2001): 1841–54. http://dx.doi.org/10.1093/brain/124.9.1841.

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49

Senkfor, Ava J., Cyma Van Petten, and Marta Kutas. "Episodic Action Memory for Real Objects: An ERP Investigation With Perform, Watch, and Imagine Action Encoding Tasks Versus a Non-Action Encoding Task." Journal of Cognitive Neuroscience 14, no. 3 (April 1, 2002): 402–19. http://dx.doi.org/10.1162/089892902317361921.

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Cognitive research shows that people typically remember actions they perform better than those that they only watch or imagine doing, but also at times misremember doing actions they merely imagined or planned to do (source memory errors). Neural research suggests some overlap between brain regions engaged during action production, motor imagery, and action observation. The present study evaluates the similar-ities/differences in brain activity during the retrieval of various types of action and nonaction memories. Participants study real objects in one of four encoding conditions: performing an action, watching the experimenter perform an action, or imagining an action with an object, or a nonmotoric task of estimating an object's cost. At test, participants view color photos of the objects, and make source memory judgments about the initial encoding episodes. Event-related potentials (ERPs) during test reveal (1) content-specific brain activity depending on the nature of the encoding task, and (2) a hand tag, i.e., sensitivity to the hand with which an object had been manipulated at study. At fronto-central sites, ERPs are similar for the three action-retrieval conditions, which are distinct from those to the cost-encoded objects. At occipital sites ERPs distinguished objects from encoding conditions with visual motion (Perform and Watch) from those without visual motion (Imagine and Cost). Results thus suggest some degree of recapitulation of encoding brain activity during retrieval of memories with qualitatively distinct attributes.

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Wechselberger, C., and G. Kreil. "Structure of two cDNAs encoding cholecystokinin precursors from the brain of Xenopus laevis." Journal of Molecular Endocrinology 14, no. 3 (June 1995): 357–64. http://dx.doi.org/10.1677/jme.0.0140357.

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ABSTRACT The skin secretions of many frogs, including Xenopus laevis, contain caerulein, a peptide related to mammalian cholecystokinin. We have screened a cDNA library prepared from the brain of this frog using a cloned cDNA encoding one of the caerulein precursors as a probe. Two clones were isolated which contained inserts encoding cholecystokinin precursors. It was found that the predicted precursor polypeptides resembled their mammalian counterparts rather than the caerulein precursors from the same species. The corresponding mRNAs of different size encoding the Xenopus cholecystokinin precursors are expressed in brain and in the gastrointestinal tract, but not in skin. The smaller mRNA was also detected in lung. These data demonstrate that a polypeptide homologous to mammalian cholecystokinin precursors was present early in the evolution of vertebrates. The possible evolution of the genes encoding the more complex caerulein precursors is discussed.