An information-processing model of three cortical regions: evidence in episodic memory retrieval

Sohn, Myeong Ho; Goode, Adam; Stenger, V. Andrew; Jung, Kwan Jin; Carter, Cameron S.; Anderson, John R.

doi:10.1016/j.neuroimage.2004.11.001

Cited by 61 publications

(73 citation statements)

References 27 publications

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“…Then, after the final keypress, it came back to positive values, overshooting baseline before returning to it. We have previously found task-related differences in activity that go below baseline in this area (Danker & Anderson, 2007;Sohn et al, 2005), as have others (Grabner et al, 2007). In fact, the greater activity for retrieval found by Delazer and colleagues (Delazer et al, 2003;Delazer et al, 2005) represented differences in negative activity.…”

Section: Discussionsupporting

confidence: 64%

“…Finally, activity in the angular gyrus was difficult to predict; it is specifically implicated in multiplication, particularly when problems are solved by direct retrieval (Dehaene et al, 2003;Delazer et al, 2003). However, previous research by our group, using algebra, found deactivation in this area (Danker & Anderson, 2007;Sohn et al, 2005).…”

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confidence: 82%

“…Previously, when assessing the fit between data and model, we have assumed this deviation statistic to be distributed as a 2 (Danker & Anderson, 2007;Sohn et al, 2005). However, because successive scans are not independent observations-producing autocorrelations in the deviations from the mean between scans-the assumptions of the 2 distribution do not hold.…”

Section: ( ) ( / ) ( / )mentioning

confidence: 99%

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Neural correlates of arithmetic calculation strategies

Rosenberg‐Lee

Lovett

Anderson

2009

Cognitive, Affective, & Behavioral Neuroscience

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Section: Discussionsupporting

confidence: 64%

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confidence: 82%

Section: ( ) ( / ) ( / )mentioning

confidence: 99%

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Neural correlates of arithmetic calculation strategies

Rosenberg‐Lee

Lovett

Anderson

2009

Cognitive, Affective, & Behavioral Neuroscience

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“…The dilemma with brain reorganization 529 resources) Qin et al, 2004;Sohn et al, 2003;Sohn et al, 2005). One important caveat to this discussion regarding practice, learning, and activation in healthy adults is that, whereas PFC activation may diminish, the role of the ACC may increase as performance improves (Fincham & Anderson, 2006).…”

Section: Current Findings In Clinical Imaging Of Wmmentioning

confidence: 99%

Neuroimaging of working memory dysfunction and the dilemma with brain reorganization hypotheses

Hillary

2008

J Int Neuropsychol Soc

102

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There is a growing literature examining working memory deficits using functional imaging and there has been great convergence in the findings, to date, but interpretations have varied. Investigators consistently observed recruitment of neural resources in clinical samples, with some examiners attributing these findings to neural inefficiency and others attributing differences to neural compensation and0or brain reorganization. It is the goal of this paper to address the current interpretation of altered brain activation in clinical imaging studies of working memory dysfunction with specific emphasis on findings in prefrontal cortex (PFC). Throughout this review, the methods used to examine brain reorganization associated with working memory dysfunction are critiqued with the goal of understanding how study design has influenced data interpretation. It is proposed that much of what has been considered "aberrant" neural activity is not indicative of neural compensation, as it has been typically defined, and does not represent brain reorganization. Instead, recruitment of neural resources in PFC can be explained by a natural, and largely overlooked, role of cognitive control in accommodating neural dysfunction secondary to brain injury and disease. This paper provides predictions based on this proposition and a critique of the current methods available for testing these predictions. (JINS, 2008, 14, 526-534.) Keywords: Plasticity, Traumatic brain injury, MS, Working memory, Reorganization, Cognitive control, Prefrontal, Functional magnetic resonance imaging An Alternative to Brain Reorganization HypothesesFunctional imaging techniques afford the unique opportunity to examine the relationships between basic brain changes and the behavioral deficits associated with brain injury and disease. There is a growing functional imaging literature examining the effects of neurological insult on working memory (WM), or the ability to maintain and0or manipulate small amounts of information for brief periods of time. In the functional imaging literature examining WM dysfunction, there has been increasing reference to "neural compensation" and "brain reorganization" to describe the altered (and almost universally increased) neural activation observed when comparing clinical samples to healthy adults. Specifically, the term "compensation" has been used to describe either transient or permanent alterations in neural activity, which operates to facilitate performance. Therefore, in this paper, the term "compensation" will be used to indicate a positive relationship between task performance and brain activation. The term "brain reorganization" has typically been used to indicate that the neural networks associated with WM have been permanently altered because of neurological insult, so that insult induces a "rewiring" of WM networks. Therefore, the primary distinction between "compensation" and "brain reorganization" is that the former may be transient whereas the latter represents permanent changes in the neural networks ...

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“…We used model-based fMRI to locate two functions within the fronto-parietal network: declarative memory retrieval and updating of working memory. These functions are often assumed to be part of the fronto-parietal network, but the hypothesized locations within the network differ among studies (2,(6)(7)(8)(9)(10)(11).…”

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confidence: 99%

Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network

Borst

Anderson

2013

Proc. Natl. Acad. Sci. U.S.A.

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In this study, we used model-based functional MRI (fMRI) to locate two functions of the fronto-parietal network: declarative memory retrievals and updating of working memory. Because regions in the fronto-parietal network are by definition coherently active, locating functions within this network is difficult. To overcome this problem, we applied model-based fMRI, an analysis method that uses predictions of a computational model to inform the analysis. We applied model-based fMRI to five previously published datasets with associated computational cognitive models, and subsequently integrated the results in a meta-analysis. The meta-analysis showed that declarative memory retrievals correlated with activity in the inferior frontal gyrus and the anterior cingulate, whereas updating of working memory corresponded to activation in the inferior parietal lobule, as well as to activation around the inferior frontal gyrus and the anterior cingulate.I n this study, we used model-based functional MRI (fMRI) to locate two functions of the so-called "fronto-parietal network." The fronto-parietal network consists of brain areas that are coherently active and assumed to implement cognitive control functions: working memory, attentional selection, and error monitoring (1-5). It typically involves at least the dorsolateral prefrontal cortex, the anterior cingulate, and a region around the intraparietal sulcus. Because the regions in the fronto-parietal network are by definition active at the same time, distinguishing the precise functional characteristics of those regions is difficult with conventional fMRI methods. Model-based fMRI is particularly well suited for dissociating between highly correlated contributions to the blood oxygen level-dependent (BOLD) signal. We used model-based fMRI to locate two functions within the fronto-parietal network: declarative memory retrieval and updating of working memory. These functions are often assumed to be part of the fronto-parietal network, but the hypothesized locations within the network differ among studies (2, 6-11).Model-based fMRI is a relatively recent approach to analyzing fMRI data. Instead of using the condition structure of the experiment to inform the analysis, as in a conventional fMRI analysis, model-based fMRI uses information derived from a computational model (12, 13). For example, Daw et al. (14) fitted a mathematical reinforcement learning model to the behavior of their study participants and then used parameter values of the model as regressors in the fMRI analysis. This resulted in brain regions that correlated significantly with those parameter values, and thus with certain features of their model. Another recent study showed that modelbased fMRI can be used successfully in combination with more high-level information-processing models (15). By regressing the activity of model components (e.g., visual processing, declarative memory retrieval) against neural activity, the neural correlates of the model components can be located.To identify the neural correlates o...

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An information-processing model of three cortical regions: evidence in episodic memory retrieval

Cited by 61 publications

References 27 publications

Neural correlates of arithmetic calculation strategies

Neural correlates of arithmetic calculation strategies

Neuroimaging of working memory dysfunction and the dilemma with brain reorganization hypotheses

Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network

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