Shared and distinct contributions of rostrolateral prefrontal cortex to analogical reasoning and episodic memory retrieval

Andrew Westphal, Nicco Reggente, Kaori Ito, Jesse Rissman

Abstract: Rostrolateral prefrontal cortex (RLPFC) is widely appreciated to support higher cognitive functions, including analogical reasoning and episodic memory retrieval. However, these tasks have typically been studied in isolation, and thus it is unclear whether they involve common or distinct RLPFC mechanisms. Here, we introduce a novel functional magnetic resonance imaging (fMRI) task paradigm to compare brain activity during reasoning and memory tasks while holding bottom-up perceptual stimulation and response demands constant. Univariate analyses on fMRI data from twenty participants identified a large swath of left lateral prefrontal cortex, including RLPFC, that showed common engagement on reasoning trials with valid analogies and memory trials with accurately retrieved source details. Despite broadly overlapping recruitment, multi-voxel activity patterns within left RLPFC reliably differentiated these two trial types, highlighting the presence of at least partially distinct information processing modes. Functional connectivity analyses demonstrated that while left RLPFC showed consistent coupling with the fronto-parietal control network across tasks, its coupling with other cortical areas varied in a task-dependent manner. During the memory task, this region strengthened its connectivity with the default mode and memory retrieval networks, whereas during the reasoning task it coupled more strongly with a nearby left prefrontal region (BA 45) associated with semantic processing, as well as with a superior parietal region associated with visuospatial processing. Taken together, these data suggest a domain-general role for left RLPFC in monitoring and/or integrating task-relevant knowledge representations and showcase how its function cannot solely be attributed to episodic memory or analogical reasoning computations.

Read the full article, here: Westphal_HBM_2015

Decoding fMRI Signatures of Real-world Autobiographical Memory Retrieval

Jesse Rissman, Tiffany E. Chow, Nicco Reggente, and Anthony D. Wagner

Abstract: Extant neuroimaging data implicate frontoparietal and medial-temporal lobe regions in episodic retrieval, and the specific pattern of activity within and across these regions is diagnostic of an individual’s subjective mnemonic experience. For example, in laboratory-based paradigms, memories for recently encoded faces can be accurately decoded from single-trial fMRI patterns. Goal-directed modulation of neural memory patterns: Implications for fMRI-based memory detection. Detecting individual memories through the neural decoding of memory states and past experience. Here, we investigated the neural patterns underlying memory for real-world autobiographical events, probed at 1- to 3-week retention intervals as well as whether distinct patterns are associated with different subjective memory states. For 3 weeks, participants ( n = 16) wore digital cameras that captured photographs of their daily activities. One week later, they were scanned while making memory judgments about sequences of photos depicting events from their own lives or events captured by the cameras of others. Whole-brain multivoxel pattern analysis achieved near-perfect accuracy at distinguishing correctly recognized events from correctly rejected novel events, and decoding performance did not significantly vary with retention interval. Multivoxel pattern analysis classifiers also differentiated recollection from familiarity and reliably decoded the subjective strength of recollection, of familiarity, or of novelty. Classification-based brain maps revealed dissociable neural signatures of these mnemonic states, with activity patterns in hippocampus, medial pFC, and ventral parietal cortex being particularly diagnostic of recollection. Finally, a classifier trained on previously acquired laboratory-based memory data achieved reliable decoding of autobiographical memory states. We discuss the implications for neuroscientific accounts of episodic retrieval and comment on the potential forensic use of fMRI for probing experiential knowledge.

Read the full article, here: Decoding fMRI Signatures of Real-world Autobiographical Memory Retrieval

Individual differences in working memory performance as a function of the local integrity and regional connectivity of the hippocampus

Kommers, C.,, Reggente, N., Raccah, O., Rissman, J.

Cody Kommers, my research assistant, presented this poster at SfN in Washington, D.C (2014)

SfN 2014 Poster

SfN 2014 Abstract:

Although the hippocampus is well known to contribute to the storage and retrieval of long-term memories, emerging data suggests that the hippocampus may also contribute to the online maintenance of task-relevant representations in some tests of working memory. To the degree that hippocampal mechanisms serve to facilitate performance on short delay memory tasks, individual differences in hippocampal microstructure could contribute to across-subject variance in working memory performance. To examine the relationship between hippocampal structure and function, we obtained the diffusion-weighted images (DWI) of a large cohort of subjects from the Human Connectome Project MRI dataset. We used the DWI to compute diffusion tensor images (DTI), which in turn were used to generate whole-brain mean-diffusivity (MD) maps. MD in deep gray matter has been construed as an indirect measurement of local microstructural deficits (Kim et al., 2013). Thereby, we aimed to assess the underlying integrity of each subject’s hippocampal gray matter and use examine whether these measures can account for variance in memory performance across subjects. Hippocampal regions of interest (ROIs) were identified using Freesurfer’s automated segmentation algorithm. Average MD within the left hippocampus was found to be significantly correlated with performance on a Working Memory List Sorting Task. This result is consistent with prior work showing that hippocampal MD serves a predictor for verbal and visuospatial memory (Carlesimo et al., 2010). Furthermore, MD along the Fornix (acquired from the Johns Hopkins White Matter Atlas) also significantly correlated with performance on the same task. This result illustrates that in addition to local integrity, the health of the hippocampus’s primary output tract is equally as important in explaining behavior that purportedly depends on hippocampal circuitry. This current study extends these previous findings and contributes to the debate surrounding the role of the hippocampus in working memory. We plan to conduct further analyses aimed at characterizing the potentially important role of fronto-hippocampal connectivity in working memory performance.

Highlighted Results:

Screenshot 2014-11-20 20.30.49


1) Ranganath, C., & Blumenfeld, R.S. (2005). Doubts about double dissociations between short- and long-term memory. Trends Cogn Sci, 9(8), 374–380.
2) Rissman, J., et al. (2008). Dynamic adjustments in prefrontal, hippocampal, and inferior temporal interactions with increasing visual working memory load. Cereb Cortex, 18(7), 1618–1629.
3) van Vugt, M. K., Schulze-Bonhage, A., Litt, B., Brandt, A., & Kahana, M. J. (2010). Hippocampal gamma oscillations increase with memory load. J Neurosci, 30(7), 2694–2699.
4) von Allmen, D.Y., et al. (2013). Neural activity in the hippocampus predicts individual visual short-term memory capacity. Hippocampus.
5) Winston, G.P., et al. (2013). Structural correlates of impaired working memory in hippocampal sclerosis. Epilepsia, 54(7), 1143–1153.
6) Yee, L.T.S., et al. (2014) Short-term retention of relational memory in amnesia revisited: accurate performance depends on hippocampal integrity. Frontiers in human neuroscience 8, 16.
7) Van Essen, D.C., et al. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage 80(2013):62-79.
8) Kim, H.J., et al. (2013) Alterations of mean diffusivity in brain white matter and deep gray matter in Parkinson’s disease. Neuroscience Letters 550: 64-68.
9) den Heijer, T., et al. (2012). Structural and diffusion MRI measures of the hippocampus and memory performance. NeuroImage, 63(4), 1782–1789.
10) Carlesimo, G.A., et al. (2010). Hippocampal mean diffusivity and memory in healthy elderly individuals: a cross-sectional study. Neurology, 74(3), 194–200.


Predicting individual differences in cognitive gains from videogame training using machine learning analyses of fMRI functional connectivity patterns

CNS 2014 Abstract: Nikolaidis, A., Reggente, N. et al


Using the the task-dependent functional connectivity across nodes within established cortical network from both before and after videogame training, we were able to asses the “plasticity” of these networks as a function of training. The plasticity of these networks, combined with graph theoretical metrics were used as features in a leave-one-out Ridge Regression that was able to account for upwards of 80% of the variance in individual difference scores in learning. Each network contributed varying levels of accuracy to classification depending on its involvement in the subject’s instructed priorities within the task. For example, plasticity in the Cingulo-Opercular network preferentially predicted(upwards of 55% of variance accounted for) learning in a training strategy that relied more heavily on executive control of attention and goal directed behavior.

Predicting individual differences in cognitive gains from videogame training using machine learning analyses of fMRI functional connectivity patterns

Aki Nikolaidis1, Nicco Reggente2, Drew Goatz3, Kathryn Hurley3, Andrew Westphal2, Arthur F. Kramer1; 1University of Illinois, Urbana Champaign, Beckman Institute, 2University of California, Los Angeles, Psychology Department, 3University of Illinois, Bioengineering Department

One of the important questions in cognitive training, and learning and memory more broadly, is how pre-existing individual differences in brain connectivity influence the effect of training. In this study, we use the fMRI functional connectivity of multiple networks, including the frontal-parietal and motor networks, to predict individual differences in learning over the course of 30 hours of cognitive training with the Space Fortress videogame. We used various metrics of functional connectivity and graph theory-derived parameters from 45 young adult participants as features to train adaptive multivariate regression models. Using a leave-one-participant-out cross-validation procedure, we find that we can predict a significant percentage of the variance in learning performance (defined as pre-post differences in Space Fortress score). By analyzing the performance of different regression models, we find that distinct brain networks contain different types of information regarding individual differences in learning rate. Furthermore, using both support vector regression and ridge regression we demonstrate how different feature and model parameters have important effects on model performance, and we consider how these parameters may have limited previous research using such techniques. We discuss implications of our results for cognitive training, as well as the continued use of machine learning and graph theoretical analyses in cognitive neuroscience.

Disentangling Disorders of Consciousnes: Insights from DTI and MVPA

CNS 2014 Poster: Zheng, Z., Reggente, N. et al. CNS 2014


For this poster we used Diffusion Tensor Imaging (DTI) techniques to compute thalamo-cortical probabilistic tractography maps. That is, for every voxel in the Thalamus, we determined every other voxel in the brain’s probabilist structural connectivity with that voxel. We did this for a total of 23 patients diagnosed with disorders of consciousness that fell into one of three possible clinical stratification (Vegetative State(VS), Minimally Conscious State+(MCS+), Minimally Conscious State-(MCS-)). We utilized a support vector machine to observe the patterns local anatomical connectivity of regions of cortex with the thalamus unique to subjects within each of the three groupings. We then used this classifier to “diagnose” unseen subjects in an extensive cross-validation. At times, particularly when using the patterns of activity local to the left prefrontal cortex, we were able to achieve 100% accuracy when distinguishing between VS and MCS+. We used a searchlight-mapping technique to determine which regions in the brain were most informative to classification. We are hoping that this technology could be found useful as a diagnostic aid that relies on the underlying pathology of patients with disorders of consciousness as opposed to the purely behavioral diagnoses currently utilized.

CNS 2014 Abstract:

Disentangling Disorders of Consciousness: Insights from DTI and MVPA

Zhong A. Zheng1, Nicco Reggente1, Evan S. Lutkenhoff1, Adrian Owen2, Martin M. Monti1; 1University of California, Los Angeles, 2University of Western Ontario

The stratification of individuals surviving severe brain injury in Minimally Conscious State (MCS) and Vegetative State (VS) patients is, currently, entirely based on behavioral criteria. This approach is problematic for at least two reasons: (i) behavioral assessments are known to be susceptible to sizeable misdiagnosis (~40%); (ii) this stratification of patients is entirely blind to the underlying pathology. To address both issues, we employed diffusion probabilistic tractography to assess projections from thalamic nuclei in 8 MCS plus (+) patients, who exhibit high-level behavioral responses, 8 MCS minus (-) patients, who only show low-level responses, and 8 VS patients. Evaluation of thalamo-cortical connectivity revealed more connections from the lateral-group nuclei to prefrontal, motor, and sensory regions in MCS+, as compared to VS. Additionally, tractography maps from thalamic nuclei were used as patterns in a logistic regression classification scheme. Using the ventral lateral nucleus’ whole-brain tractography maps as patterns, a leave-two-patients-out cross-validation correctly classified 6/8 VS patients and 7/8 MCS+ patients. This classification relied mostly on increased thalamo-frontal connections in MCS+ patients, as compared to VS. These results suggest that DTI combined with machine learning classification may facilitate the diagnostic distinction between VS and subcategories of MCS by uncovering the neural markers and pathological changes underlying disorders of consciousness.

This work was a collaboration with Zhong (Amy) Sheng Zheng.


Memory recall for high value items correlates with individual differences in white matter pathways associated with reward processing and fronto-temporal communication

Publication: Coming Soon

SfN 2013 Poster: Reggente_SfN_2013


We used DTI to explore anatomical accounts for individual differences in the behavioral effects of value-directed remembering. We examined connectivity within the mesolimbic system by using probabilistic tractography algorithms to compute anatomical connectivity between each subject’s nucleus accumbens(NAcc) and ventral tegmental area(VTA). We also extracted mean fractional anisotropy (FA) values from each subject’s uncinate fasciculus(UF). Subject’s engaged in a value-directed remembering task where each word was paired with either high or low values. Subject’s mean FA along their UF was strongly correlated with the mean number of high value words they reported during recall (r=.746, p=.0001), but not with number of low value words recalled(r=.219, p=.81). The difference between these two correlations was statistically significant (Z=2.46, p=.006). The number of streamlines (i.e, the Anatomical Connectivity Index) from left NAcc to left VTA correlated with Selectivity Index(i.e how preferentially selective they were in selectively recalling high value words more so than low value words) (r=.482, p=.018).

SfN 2013 Abstract:

Memory recall for high value items correlates with individual differences in white matter pathways associated with reward processing and fronto-temporal communication

Reggente, N., Cohen, M.S., Zheng, Z., De Shetler N.G., Castel, A.D., Knowlton, B.J., Rissman, J.

When given a long list of items to remember, people will often prioritize the memorization of the most important items. In an experimental setting, importance can be operationalized by reward values assigned to each item. Prior neuroimaging studies (e.g., Adcock et al., 2006) have found that high value cues engage the mesolimbic dopamingeric reward circuitry of the brain, including the nucleus accumbens (NAcc) and the ventral tegmental area (VTA), which it turn leads to an up-regulation of medial temporal lobe encoding processes and better memory for the high value items. Value cues may also trigger the use of elaborative semantic encoding strategies, which depend on interactions between frontal and temporal lobe structures. In the present study, we used diffusion tensor imaging (DTI) to examine whether individual differences in anatomical connectivity within these circuits are predictive of value-induced modulation of memory. DTI data were collected from 19 healthy adult subjects, who also underwent fMRI scanning as they performed a value-directed memory task. In this task, subjects encoded lists of words with arbitrarily assigned point values and then completed a free recall test after each list. Our fMRI results revealed that subjects whose recall performance exhibited the greatest sensitivity to item value preferentially recruited left ventrolateral prefrontal cortex (VLPFC) duringthe encoding of high value items relative to low value items. While this effect may partially be driven by individual differences in the cognitive strategies that foster deep semantic encoding, we predicted that the robustness of the white matter pathways connecting the VLPFC with the temporal lobe might also be a determinant of recall performance for high value items. To explore this possibility, we measured the mean fractional anisotropy (FA) of each subject’s left uncinate fasciculus, a pathway thought to play a critical role in semantic processing (Harvey et al., 2013). This measure showed a significant positive correlation with the mean number of high value items that a subject recalled, but did not correlate with the mean number of low value items recalled. Given prior findings on reward-induced modulation of memory, we also examined the white matter connections between reward-related regions such as the NAcc and VTA using probabilistic tractography. As predicted, the number of fibers projecting from left NAcc to VTA correlated with individual differences in high value but not low value memory. Together, these findings provide novel insights into the neuroanatomical pathways that support verbal memory encoding and its value-incentivized modulation.