Thalamocortical Connectivity Predicts Level of Awarness in Disorders of Consciousness

Zheng, Z., Reggente, N., Lutkenhoff, E., Owen, A., Monti, M.

Amy Zheng presented this poster at SfN in Washington, D.C (2014)

SfN 2014 Poster

Brief: In this study we used probabilist tractography on diffusion tensor imaging data to look at the whole brain’s anatomical connectivity index with the Left and Right Thalamus for patients with varying disorders of consciousness. Using a searchlight mapping procedure, we trained a Support Vector Regression on all but one patient  and attempted to predict a left out subject’s “Coma Recovery Scale” value (leave-one-patient-out cross-validation). That is, we looked at each region in the brain and asked the question “Does this region’s connectivity with the thalamus provide us with significant predictive power in regards to a DOC patient’s level of awareness?”. We were able to capture a significant portion of the variance (upwards of 56% of the variance), especially when drawing features for the SVR from regions such as the Middle Frontal Gyrus, Superior Frontal Gyrus, Precuneus, Parietal Operculum, Postcentral Gyrus, Inferior Temporal, Lateral Ocipital cortex, and Supracalcarine Cortex. Of particular intrigue is the contra-lateral importance of these connections. Thus, the ability for the Thalamus to anatomically connect with these regions significantly predicts levels of consciousness.

Highlighted Figure:

Screenshot 2014-11-20 20.50.53

SfN 2014 Abstract:

A reliable neural biomarker would serve as a valuable prognostic indicator for the assessment of awareness in patients with disorders of consciousness (DOC). Previous research has suggested that DOC may reflect disconnections in the thalamocortical networks. In this current study, we used probabilistic tractography to investigate the structural connectivity between the thalamus and the rest of the brain in 23 patients with varying levels of awareness as measured by the coma recovery scale-revised (CRS-R). The CRS-R spans six subscales aimed at assessing overt consciousness: auditory, visual, motor, oromotor, communication, and arousal. We obtained a total CRS-R score for each patient by summing across all six-subscale scores, where a maximum total score would be 23 points. We employed a searchlight mapping approach by centering a 5mm sphere at each voxel in the brain. The thalamic-connectivity-index values of voxels within each sphere were used as predictors in a support-vector-regression. The predictive power of our model was assessed by a leave-one-patient-out cross-validation whereby we iteratively trained a support-vector-regression model on 22 subjects and applied that model to the left-out subject. The resulting vector of predicted CRS-R scores correlated with the actual CRS-R scores most strongly when the searchlight was centered in Middle Frontal Gyrus and Precuneus. More specifically, connections with the left thalamus in these regions accounted for upwards of 44% of the variance in CRS-R scores. Furthermore, lateral occipital cortext and Right Supracalcarine Cortex accounted for up to 56% of the variance in CRS-R scores. These results provide neural bases for the level of conscious awareness displayed by DOC patients. More specifically, this investigation highlights the importance of thalamo-prefrontal and thalamo-temporal circuits in establishing a dependable anatomical metric for calculating patients’ CRS-R scores. Such findings support the “disconnection syndrome” hypothesis by illustrating that decreases in structural connectivity throughout the brain correlates with degradations in conscious awareness.

 

References
1. Schiff, N.D., 2010. Recovery of consciousness after brain inury: a mesocircuit hypothesis.
Trends Neurosci. 33, 1-9.
2. Van der Werf, Y.D., Witter, M.P., Groenewegen, H.J., 2002. The intralaminar and midline
nuclei of the thalamus. Anatomical and functional evidence for participation in processes of
arousal and awareness. Brain Res Brain Res Rev. 39, 107-40.
3. Kim, S.P. Hwang, E., Kang, J.H., Kim, S. & Choi, J.H. Changes in the thalamocortical
connectivity connectivity during anesthesia-induced transitions in consciousness.
Neuroreport 23, 294-8 (2012).
4. Laureys S. et al. Restoration of thalamocortical connectivity after recovery from persistent
vegetative state. Lancet 355, 1790-1 (2000).
5. Lutkenhoff, E.S., McArthur, D.L., Hua, X., Thompson, P.M., Vespa, P.M., & Monti, M.M.
Thalamic atrophy in antero-medial and dorsal nuclei correlates with six-month outcome
after severe brain injury. NeuroImage: Clinical, 3, 396-404. (2013)

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

References

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.

 

Episodic memory retrieval benefits from a less modular brain network organization

Westphal, A.J., Monti, M.M., Reggente, N., Yazdanshenas, O., & Rissman, J.

Andrew Westphal presented this work at an SfN Nanosymposium in Washington D.C (2014)

SfN 2014 Abstract:

The act of retrieving a memory for a specific episode of one’s past requires the coordination of brain networks involved in controlling access to mnemonic contents and representing and monitoring the stored information. This has been shown to invoke a brain connectivity profile that diverges somewhat from the brain’s intrinsic resting state organization (Fornito et al., 2012). However, it is not yet clear to what degree this “retrieval mode” brain state differs from that observed during other complex cognitive tasks. In order to examine this further, we performed a graph theoretical analysis on fMRI functional connectivity data patterns measured while participants (N = 20) alternated between the performance of episodic source memory retrieval, analogical reasoning, and visuospatial perception tasks. In order to avoid systematic confounds, we ensured that the tasks were matched for response demands, reaction times, and bottom-up visual processing. Following preprocessing, we extracted fMRI time-courses from each 40 sec task block and concatenated these across runs to generate task-specific time-courses. We next reduced our whole brain data set to 264 functional areas, identified by resting state parcellation and meta-analysis (Power et al., 2011) and defined as spherical regions of interest (5mm radius). Pairwise correlations were then computed between all pairs of nodes for each cognitive task, and the weakest connections were thresholded out at a range of sparsity values. To capture a key global property of brain network dynamics, we analyzed how much each task-set expressed a graph theoretic measure known as modularity (Newman, 2006), which assesses the amount of connectivity within identified networks versus between networks. Our data revealed that the memory retrieval task showed significantly reduced modularity in comparison to the reasoning and perception tasks, an effect that replicated across sparsity thresholds. This suggests that the memory task-set is characterized by more widespread connectivity across the brain. Strikingly, reduced modularity in individual subjects was diagnostic of fewer memory errors and improved source monitoring. Taken together, our results suggest that memory retrieval may benefit from lower modularity, presumably because otherwise competitive brain networks supporting externally-directed and internally-directed attention must work together to link environmental stimuli with an introspective mnemonic search process.