Systems Biology Perspectives on Cerebellar Long-Term Depression

Ogasawara, Haruhiko; Doi, Tomokazu; Kawato, Mitsuo

doi:10.1159/000123040

Cited by 36 publications

(30 citation statements)

References 203 publications

(255 reference statements)

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“…Among the many models that attempt to explain cerebellar plasticity at the cellular level (Hansel et al, 2001;Kim and Linden, 2007), long-term depression (LTD) remains one of the most influential (Ogasawara et al, 2008) partly because it is consistent with classic models that explain cerebellar learning in terms of changes in the excitability of PC-parallel fiber (PF) inputs. Memory formation is explained in terms of concurrent PF and climbing fiber inputs that depress the sensitivity of PC-PF inputs, leading eventually to a reduction in simple spike frequency in in vitro cerebellar preparations.…”

Section: Cerebellar Cortical Physiology and Synaptic Plasticitymentioning

confidence: 83%

Cerebellar Plasticity and the Automation of First-Order Rules

Balsters¹,

Ramnani²

2011

J. Neurosci.

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Theories of corticocerebellar function propose roles for the cerebellum in automating motor control, a process thought to depend on plasticity in cerebellar circuits that exchange information with the motor cortex. Little is known, however, about automating behaviors beyond the motor domain. The present study tested the hypothesis that cerebellar plasticity also subserves the development of automaticity in behavior based on low-order rules. Human subjects were required to learn two sets of first-order rules in which visual stimuli of different shapes each arbitrarily instructed a particular finger movement. We used event-related functional magnetic resonance imaging to scan subjects while these response rules became increasingly automatic with practice, as assessed with a dual-task procedure. We found that the amplitude of the blood oxygenation level-dependent signal gradually decreased as a function of practice, as responses became increasingly automatic, and that this effect was greater for a set of rules that became automatic rapidly compared with a second set, which became automatic more slowly. These trial-by-trial activity changes occurred in Crus I of cerebellar cortical lobule HVIIA, in which neurons exchange information with the prefrontal cortex rather than the motor cortex. Activity in Crus I was time locked specifically to the processing of these rules, rather than to subsequent actions. The results support the hypothesis that decreases in cerebellar cortical activity underlie the automation of behavior, whether related to motor control and motor cortex or to response rules and prefrontal cortex.

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Section: Cerebellar Cortical Physiology and Synaptic Plasticitymentioning

confidence: 83%

Cerebellar Plasticity and the Automation of First-Order Rules

Balsters¹,

Ramnani²

2011

J. Neurosci.

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“…We have identified 42 probes that can be divided into two groups, those that seem to be increasingly downregulated with AD severity (CABP1 [235], [236], [237], [238], [239], [240], [241], [242], [243], CADPS2 [244], [245], [246], [247], [248], [249], COLQ [250], DMD [251], [252], [253], [254], [255], [256], ELOVL2 [257], FAIM2/LFG [258], [259], [260], [261], GABBR2 [262], [263], [264], [265], GRIA2/GLUR2 [266], [267], [268], [269], [270], [271], [272], [273], [274], [275], [276], [277], ITPR1 [278], [279], [280], [281], [282], [283], KIAA0528, LZTS1/FEZ1 [284], [285], NEFM, NRG1, NRXN1, NUFIP1 [286], [287], [288], PPT1 [289], [290], [291], [292], [293], [294], [295], [296], …”

Section: Resultsmentioning

confidence: 99%

Uncovering Molecular Biomarkers That Correlate Cognitive Decline with the Changes of Hippocampus' Gene Expression Profiles in Alzheimer's Disease

et al. 2010

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BackgroundAlzheimer's disease (AD) is characterized by a neurodegenerative progression that alters cognition. On a phenotypical level, cognition is evaluated by means of the MiniMental State Examination (MMSE) and the post-morten examination of Neurofibrillary Tangle count (NFT) helps to confirm an AD diagnostic. The MMSE evaluates different aspects of cognition including orientation, short-term memory (retention and recall), attention and language. As there is a normal cognitive decline with aging, and death is the final state on which NFT can be counted, the identification of brain gene expression biomarkers from these phenotypical measures has been elusive.Methodology/Principal FindingsWe have reanalysed a microarray dataset contributed in 2004 by Blalock et al. of 31 samples corresponding to hippocampus gene expression from 22 AD subjects of varying degree of severity and 9 controls. Instead of only relying on correlations of gene expression with the associated MMSE and NFT measures, and by using modern bioinformatics methods based on information theory and combinatorial optimization, we uncovered a 1,372-probe gene expression signature that presents a high-consensus with established markers of progression in AD. The signature reveals alterations in calcium, insulin, phosphatidylinositol and wnt-signalling. Among the most correlated gene probes with AD severity we found those linked to synaptic function, neurofilament bundle assembly and neuronal plasticity.Conclusions/SignificanceA transcription factors analysis of 1,372-probe signature reveals significant associations with the EGR/KROX family of proteins, MAZ, and E2F1. The gene homologous of EGR1, zif268, Egr-1 or Zenk, together with other members of the EGR family, are consolidating a key role in the neuronal plasticity in the brain. These results indicate a degree of commonality between putative genes involved in AD and prion-induced neurodegenerative processes that warrants further investigation.

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“…In contrast, a still popular view sees olivary discharges as event detectors which are used to induce long term plastic changes in the Purkinje cells, thereby serving as an initiator for motor learning (Jorntell and Ekerot, 2002;Ito, 2006;Ogasawara et al, 2008). The nature of the signals used for inducing these long term changes is also much debated (Simpson et al, 1996;Gibson et al, 2004;Ke et al, 2009).…”

Section: Brainstem and Cerebellummentioning

confidence: 99%