This is a good question! Resting-state functional connectivity or working memory performance using independent component analysis may be able to track some long lasting changes in functional brain networks. I write about this more in a fairly recent article:

https://www.researchgate.net/publication/256202035_Tracking_the_neuroplastic_changes_associated_with_transcranial_direct_current_stimulation_a_push_for_multimodal_imaging

I would be interested to know more about your training as that would determine which tasks may be the most informative with respect to measuring change. For instance, it would be ideal to have a design where you can test for near and far transfer -- based on your training condition.

-Michael

Article Tracking the neuroplastic changes associated with transcrani...

Very good question - I'd say it depends on your definition of plasticity and the response you expect. A few thoughts -

If you expect additional regions to be recruited or existing regions to be more strongly recruited, I agree suprathreshold counts and some other measures are problematic, one approach we've tried is SPM's multivariate Bayesian decoding:

https://www.researchgate.net/publication/51639284_Decoding_episodic_memory_in_ageing_A_Bayesian_analysis_of_activity_patterns_predicting_memory

Connectivity approaches approaches can also be useful, see our recent discussion of the related issues of compensation and reorganisation:

https://www.researchgate.net/publication/271333506_Neural_Reorganization_and_Compensation_in_Aging

Lovden and others (2010) also have provided some definitions of plasticity in relation to training interventions and the kinds of neural responses to be expected:

https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ppmQdDEAAAAJ&citation_for_view=ppmQdDEAAAAJ:2osOgNQ5qMEC

https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ppmQdDEAAAAJ&citation_for_view=ppmQdDEAAAAJ:2osOgNQ5qMEC

Article Decoding episodic memory in ageing: A Bayesian analysis of a...

Article Neural Reorganization and Compensation in Aging

Thank you for the references. Neural efficiency and capacity both describe brain activity responses to increasing cognitive load. These can both be considered parameters of limited growth models. So I am thinking of plasticity as external changes (training, aging, disease) resulting in changes in the load to brain activity relationship. Then the question is how to best quantify such parameters.

Dear Jason,

Given your particular interest in cognitive load, functional brain networks, training, and growth models, I highly recommend the following papers, which layout the theoretical framework for inducing neuroplastic changes, and for identifying metrics to assess such change. -Michael.

Mercado, E. (2008). Neural and cognitive plasticity: from maps to minds. Psychological Bulletin, 134(1), 109–137.

Abstract:

"Some species and individuals are able to learn cognitive skills more flexibly than others. Learning experiences and cortical function are known to contribute to such differences, but the specific factors that determine an organism's intellectual capacities remain unclear. Here, an integrative framework is presented suggesting that variability in cognitive plasticity reflects neural constraints on the precision and extent of an organism's stimulus representations. Specifically, it is hypothesized that cognitive plasticity depends on the number and diversity of cortical modules that an organism has available as well as the brain's capacity to flexibly reconfigure and customize networks of these modules. The author relates this framework to past proposals on the neural mechanisms of intelligence, including (a) the relationship between brain size and intellectual capacity; (b) the role of prefrontal cortex in cognitive control and the maintenance of stimulus representations; and (c) the impact of neural plasticity and efficiency on the acquisition and performance of cognitive skills. The proposed framework provides a unified account of variability in cognitive plasticity as a function of species, age, and individual, and it makes specific predictions about how manipulations of cortical structure and function will impact intellectual capacity."

Lövdén, M., Bäckman, L., Lindenberger, U., Schaefer, S., & Schmiedek, F. (2010). A theoretical framework for the study of adult cognitive plasticity. Psychological Bulletin, 136(4), 659–676.

Abstract

"Does plasticity contribute to adult cognitive development, and if so, in what ways? The vague and overused concept of plasticity makes these controversial questions difficult to answer. In this article, we refine the notion of adult cognitive plasticity and sharpen its conceptual distinctiveness. According to our framework, adult cognitive plasticity is driven by a prolonged mismatch between functional organismic supplies and environmental demands and denotes the brain's capacity for anatomically implementing reactive changes in behavioral flexibility (i.e., the possible range of performance and function). We distinguish between 2 interconnected but distinct cognitive outcomes of adult cognitive plasticity: alterations in processing efficiency and alterations in representations. We demonstrate the usefulness of our framework in evaluating and interpreting (a) increments in frontal brain activations in the course of normal aging and (b) the effects of cognitive training in adulthood and old age. Finally, we outline new research questions and predictions generated by the present framework and recommend design features for future cognitive-training studies."

Non-fMRI approaches can also be useful for this... e.g. utilizing Magnetoencephalography (MEG) one can perform very interesting analyses that can be bandpass filtered into ranges known to correlate with specific networks (such as beta band rhythms that are associated with motor network).  More recently I have seen both resting state and task modulated functional connectivity done using approaches such as graph theory, in which one can observe plastic changes in the network associated with task performance.

"I am looking to go beyond voxel counts of suprathreshold activity." 

- This is a fundamental point in fMRI studies. I hope the paper and comments below are helpful.

www.fil.ion.ucl.ac.uk/spm/doc/papers/karl_detecting.pdf

and a comment from Matthew Brett

" Sometimes you may want to use a more liberal threshold that does not allow strong control of false positives - for example to report 'suggestive' rather than 'significant' results.

A perfectly reasonable option is to use a reduced threshold, still correcting for multiple comparisons.

Another is to report your UnthresholdedEffectMaps.

Another method that has been widely used is to use an UncorrectedThreshold. In my (MatthewBrett) view, this method can be extremely misleading to your readers, and should be avoided.

PrinciplesMultipleComparisons (2007-02-24 13:09:31  http://imaging.mrc-cbu.cam.ac.uk/basewiki/MatthewBrett "

MEG is definitely a very good tool. I explored resting-state and task-state functional connectivity using fMRI and graph theory, whereas have no experience on MEG until now. Considering its high temporal resolution, I expect interesting findings from MEG data.

Certainly rationally and objectively controlling processing choices and thresholds within subject over time is challenging.  One approach we have taken is to have a hands-off approach to this, in which one maximizes the reliability of the map in an automated fashion.  Although reliability is clearly not accuracy, when we further compared such an approach to direct cortical stimulation in a cohort of brain tumour patients, we showed in a recent article that maximizing fMRI map reliability also minimized the distance to the relevant site of cortical stimulation.  This would indicate that more reliable was also more accurate.  Given that this thresholding decision (which is a pseudo ROC approach) is data-driven, it can provide a rational framework for following intrasubject data over time.

Article Improving fMRI reliability in presurgical mapping for brain tumours

We just published this study tracking motor plasticity over three years using fMRI after severe TBI - more than six years post injury. cheers, R

Article: Long-Term Motor Recovery After Severe Traumatic Brain Injury

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