The Brain Ages Optimally to Model Its Environment: Evidence from Sensory Learning over the Adult Lifespan
Publication Date
January 23, 2014
Journal
PLOS Computational Biology
Authors
Rosalyn J. Moran, Mkael Symmonds, Raymond J. Dolan & Karl J. Friston
Volume
10
Issue
1
Pages
e1003422
DOI
https://dx.plos.org/10.1371/journal.pcbi.1003422
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003422
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24465195
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3900375
Europe PMC
http://europepmc.org/abstract/MED/24465195
Web of Science
000337948500022
Scopus
84896730491
Mendeley
http://www.mendeley.com/research/brain-ages-optimally-model-environment-evidence-sensory-learning-adult-lifespan
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Mendeley | Further Information

{"title"=>"The Brain Ages Optimally to Model Its Environment: Evidence from Sensory Learning over the Adult Lifespan", "type"=>"journal", "authors"=>[{"first_name"=>"Rosalyn J.", "last_name"=>"Moran", "scopus_author_id"=>"12345357400"}, {"first_name"=>"Mkael", "last_name"=>"Symmonds", "scopus_author_id"=>"6507118437"}, {"first_name"=>"Raymond J.", "last_name"=>"Dolan", "scopus_author_id"=>"55027467100"}, {"first_name"=>"Karl J.", "last_name"=>"Friston", "scopus_author_id"=>"36080215500"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"372348771", "sgr"=>"84896730491", "issn"=>"1553734X", "pmid"=>"24465195", "scopus"=>"2-s2.0-84896730491", "doi"=>"10.1371/journal.pcbi.1003422", "isbn"=>"1553734X"}, "id"=>"8c2f1d4e-4107-35e5-91e4-7f5289962a9d", "abstract"=>"The aging brain shows a progressive loss of neuropil, which is accompanied by subtle changes in neuronal plasticity, sensory learning and memory. Neurophysiologically, aging attenuates evoked responses--including the mismatch negativity (MMN). This is accompanied by a shift in cortical responsivity from sensory (posterior) regions to executive (anterior) regions, which has been interpreted as a compensatory response for cognitive decline. Theoretical neurobiology offers a simpler explanation for all of these effects--from a Bayesian perspective, as the brain is progressively optimized to model its world, its complexity will decrease. A corollary of this complexity reduction is an attenuation of Bayesian updating or sensory learning. Here we confirmed this hypothesis using magnetoencephalographic recordings of the mismatch negativity elicited in a large cohort of human subjects, in their third to ninth decade. Employing dynamic causal modeling to assay the synaptic mechanisms underlying these non-invasive recordings, we found a selective age-related attenuation of synaptic connectivity changes that underpin rapid sensory learning. In contrast, baseline synaptic connectivity strengths were consistently strong over the decades. Our findings suggest that the lifetime accrual of sensory experience optimizes functional brain architectures to enable efficient and generalizable predictions of the world.", "link"=>"http://www.mendeley.com/research/brain-ages-optimally-model-environment-evidence-sensory-learning-adult-lifespan", "reader_count"=>130, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>8, "Researcher"=>35, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>34, "Student > Postgraduate"=>7, "Student > Master"=>12, "Other"=>5, "Student > Bachelor"=>7, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>10, "Unspecified"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>8, "Researcher"=>35, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>34, "Student > Postgraduate"=>7, "Student > Master"=>12, "Other"=>5, "Student > Bachelor"=>7, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>10, "Unspecified"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>10, "Agricultural and Biological Sciences"=>19, "Philosophy"=>1, "Business, Management and Accounting"=>2, "Chemistry"=>1, "Computer Science"=>11, "Engineering"=>3, "Nursing and Health Professions"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Medicine and Dentistry"=>9, "Neuroscience"=>19, "Physics and Astronomy"=>4, "Psychology"=>47, "Social Sciences"=>1, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>9}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Psychology"=>{"Psychology"=>47}, "Unspecified"=>{"Unspecified"=>10}, "Engineering"=>{"Engineering"=>3}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>19}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>19}, "Computer Science"=>{"Computer Science"=>11}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Austria"=>1, "Hungary"=>1, "United States"=>3, "Japan"=>1, "United Kingdom"=>5, "France"=>4, "Australia"=>1, "Switzerland"=>1, "Germany"=>2, "Spain"=>1}, "group_count"=>4}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1358930"], "description"=>"<p>Note parameters are log-scaling parameters: . These operate on variables with the following prior mean – and can be found in spm_fx_nmda.m, part of the DCM_MEG toolbox in SPM (<a href=\"http://www.fil.ion.ucl.ac.uk/spm/\" target=\"_blank\">http://www.fil.ion.ucl.ac.uk/spm/</a>).</p><p>(<b>*</b> indicates age-predictive parameter).</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Computational biology", "neuroscience", "computational neuroscience", "neuroimaging", "dcm", "presented"], "article_id"=>911048, "categories"=>["Biological Sciences"], "users"=>["Rosalyn J. Moran", "Mkael Symmonds", "Raymond J. Dolan", "Karl J. Friston"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003422.t001", "stats"=>{"downloads"=>4, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Neuronal_parameters_of_the_DCM_description_and_prior_values_presented_in_Figure_3C_/911048", "title"=>"Neuronal parameters of the DCM – description and prior values presented in Figure 3C.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-23 04:25:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1358927"], "description"=>"<p>A) The Negative Free Energy (<i>F</i>) is maximized by the brain (model, <i>m</i>) to ensure homeo/allostasis. An optimal model can accurately predict incoming sensory signals <i>s</i>, (this accuracy term is the expected log-likelihood of the sensory signal <i>s</i>, under the conditional density, <i>q</i> ie. ) while ensuring generalization, when inferring new sensory causes (θ represented through their sufficient statistics <i>μ</i>). This complexity penalty is revealed during the presentation of the oddball. Given changes in synaptic efficacy of forward connections; i.e. learning the standard tone - the Kullback-Leibler (KL) divergence between the learned prior, <i>p</i> and the posterior, <i>q</i> under these new (oddball) data will be high. These effects, indicating brittle models, were hypothesized to be less pronounced in older subjects. B) An illustration of how model optimality depends on the environment. Left-most panels: In a constant environment both young (top) and old (bottom) brains have connections that convey accurate predictions (blue arrows). The sensory input, s, will result in prediction error messages (red arrows) that are cancelled by the appropriate prediction. A change in the environment (e.g., from a dog bark to a human voice) will result in prediction error signals along the human voice pathway until human voice predictions are made and cancellation occurs. This type of predictive coding scheme has been proposed as the mechanism underlying the mismatch negativity <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003422#pcbi.1003422-Wacongne1\" target=\"_blank\">[17]</a>. In this scenario, both young and old brains generate accurate predictions with similar complexity. Centre Left panels: repeated sensory input from a specific human voice results in new prediction and error pathways for that particular vocalization in a younger brain. For this environment, the younger brain is more accurate (at the penalty of higher complexity) and may outperform the older brain in terms of model quality. Centre Right panels: on return to the original environment, the older brain – that has maintained a less complex model – outperforms the younger brain. Right-most panels: In a novel environment that persists, younger brains – that support more flexible Bayesian updating – will outperform older brains. In this context, the degrees of freedom subtended by effective connections in the older brain are not sufficient to simulate the environment and provide accurate predictions.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Computational biology", "neuroscience", "computational neuroscience", "neuroimaging", "explanations", "sensory"], "article_id"=>911045, "categories"=>["Biological Sciences"], "users"=>["Rosalyn J. Moran", "Mkael Symmonds", "Raymond J. Dolan", "Karl J. Friston"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003422.g001", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hypotheses_8211_explanations_for_sensory_input_/911045", "title"=>"Hypotheses – explanations for sensory input.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-23 04:25:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1358928"], "description"=>"<p>A) Statistical parametric mapping of mismatch (standard – oddball) effect across subjects (<i>p</i><0.05 <i>FWE</i> corrected) sharing a color-coded <i>F</i> statistic on a semi-transparent canonical cortical inflated mesh. This SPM compares the power (in frequencies from 0–30 Hz, over 60–300 msec of peristimulus time), evoked by oddball stimuli with the equivalent power evoked by standard stimuli. B) Auditory evoked responses recorded at one MEG sensor over right frontal cortex. Plotted are the grand averaged evoked measurements across all sessions (shaded areas represent their standard deviation) in response to standard tones (blue) and oddball tones (green). The difference in these responses constitutes the mismatch negativity (MMN); seen here as the negative differences from 100–200 msec (white inset) – as predicted from the literature. Both types of trials were fitted for each subject in the DCM analysis. C) In the DCM, we modeled the transmission of neuronal activity from primary sensory to frontal regions using three sources reciprocally connected in each hemisphere; source location priors were as follows: left HG: x = −42, y = −22, z = 7; right HG: x = 46, y = −14, z = 8; left STG: x = −61, y = −32, z = 8; right STG: x = 59, y = −25, z = 8; left IFG: x = −46, y = 20, z = 8; right IFG: x = 46, y = 20, z = 8. Inputs entered Heschl's gyrus bilaterally and were passed via forward connections to STG within each hemisphere. STG sent top-down backward connections to HG. STG also sent forward connections up to IFG and received backward connections from IFG. Each source is modeled in the DCM with a neural mass model. The parameters of synaptic interactions within each source, as well as the extrinsic connections between sources were optimized during model inversion. The extrinsic connectivity was equipped with an additional parameter that allowed for different connection strengths during standard or oddball stimulus processing.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Computational biology", "neuroscience", "computational neuroscience", "neuroimaging"], "article_id"=>911046, "categories"=>["Biological Sciences"], "users"=>["Rosalyn J. Moran", "Mkael Symmonds", "Raymond J. Dolan", "Karl J. Friston"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003422.g002", "stats"=>{"downloads"=>3, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mismatch_Network_/911046", "title"=>"Mismatch Network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-23 04:25:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1358929"], "description"=>"<p>A) Representative example of data fit shown as a sensor space image for all MEG channels (along the x-axis) over peristimulus time (0–300 msec along the y-axis). Data are normalized to arbitrary units according to color bar. B) Subjects age as predicted by a linear regression on the DCM neuronal parameters. C) Left: Contribution of each parameter to the regression: negative log <i>p</i>-value for all 38 regression coefficients (37 DCM parameters and a constant; <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003422#pcbi-1003422-t001\" target=\"_blank\">Table 1</a>) as assessed using the appropriate <i>t</i>-statistic. The horizontal line depicts the Bonferroni-corrected significance level. One parameter has a significant <i>p</i>-value: this parameter encoded the difference in forward connectivity to right STG, between oddballs and standard and had a negative correlation with age. Right: Red is the forward connectivity parameter, illustrated within the DCM architecture, where age was predicted. D) Individual DCM parameter estimates. Left: the parameter controlling changes in connectivity from right HG to right STG identified above, plotted according to an adjusted (for the effect of remaining parameters in the regression model} age ranking. Right: a similar plot illustrating the latent connectivity strength from right HG to right STG, plotted according to age rank, adjusted as above.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Computational biology", "neuroscience", "computational neuroscience", "neuroimaging", "neuronal"], "article_id"=>911047, "categories"=>["Biological Sciences"], "users"=>["Rosalyn J. Moran", "Mkael Symmonds", "Raymond J. Dolan", "Karl J. Friston"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003422.g003", "stats"=>{"downloads"=>4, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Age_Effects_from_DCM_s_Neuronal_Parameters_/911047", "title"=>"Age Effects from DCM's Neuronal Parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-23 04:25:08"}

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Relative Metric

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