Voxel-based statistical analysis of thalamic glucose metabolism in traumatic brain injury: relationship with consciousness and cognition

Objective: To study the relationship between thalamic glucose metabolism and neurological outcome after severe traumatic brain injury (TBI). Methods: Forty-nine patients with severe and closed TBI and 10 healthy control subjects with 18F-FDG PET were studied. Patients were divided into three groups: MCS&VS group (n ¼ 17), patients in a vegetative or a minimally conscious state; In-PTA group (n ¼ 12), patients in a state of post-traumatic amnesia (PTA); and Out-PTA group (n ¼ 20), patients who had emerged from PTA. SPM5 software implemented in MATLAB 7 was used to determine the quantitative differences between patients and controls. FDG-PET images were spatially normalized and an automated thalamic ROI mask was generated. Group differences were analysed with two sample voxel-wise t-tests. Results: Thalamic hypometabolism was the most prominent in patients with low consciousness (MCS&VS group) and the thalamic hypometabolism in the In-PTA group was more prominent than that in the Out-PTA group. Healthy control subjects showed the greatest thalamic metabolism. These differences in metabolism were more pronounced in the internal regions of the thalamus. Conclusions: The results confirm the vulnerability of the thalamus to suffer the effect of the dynamic forces generated during a TBI. Patients with thalamic hypometabolism could represent a sub-set of subjects that are highly vulnerable to neurological disability after TBI.Lull Noguera, N.; Noé, E.; Lull Noguera, JJ.; Garcia Panach, J.; Chirivella, J.; Ferri, J.; López-Aznar, D.... (2010). Voxel-based statistical analysis of thalamic glucose metabolism in traumatic brain injury: relationship with consciousness and cognition. Brain Injury. 24(9):1098-1107. doi:10.3109/02699052.2010.494592S10981107249Gallagher, C. N., Hutchinson, P. J., & Pickard, J. D. (2007). Neuroimaging in trauma. Current Opinion in Neurology, 20(4), 403-409. doi:10.1097/wco.0b013e32821b987bWoischneck, D., Klein, S., Rei�berg, S., D�hring, W., Peters, B., & Firsching, R. (2001). Classification of Severe Head Injury Based on Magnetic Resonance Imaging. Acta Neurochirurgica, 143(3), 263-271. doi:10.1007/s007010170106Grados, M. A. (2001). Depth of lesion model in children and adolescents with moderate to severe traumatic brain injury: use of SPGR MRI to predict severity and outcome. Journal of Neurology, Neurosurgery & Psychiatry, 70(3), 350-358. doi:10.1136/jnnp.70.3.350Meythaler, J. M., Peduzzi, J. D., Eleftheriou, E., & Novack, T. A. (2001). Current concepts: Diffuse axonal injury–associated traumatic brain injury. Archives of Physical Medicine and Rehabilitation, 82(10), 1461-1471. doi:10.1053/apmr.2001.25137Scheid, R., Walther, K., Guthke, T., Preul, C., & von Cramon, D. Y. (2006). Cognitive Sequelae of Diffuse Axonal Injury. Archives of Neurology, 63(3), 418. doi:10.1001/archneur.63.3.418Brandstack, N., Kurki, T., Tenovuo, O., & Isoniemi, H. (2006). MR imaging of head trauma: Visibility of contusions and other intraparenchymal injuries in early and late stage. Brain Injury, 20(4), 409-416. doi:10.1080/02699050500487951Xu, J., Rasmussen, I.-A., Lagopoulos, J., & Håberg, A. (2007). Diffuse Axonal Injury in Severe Traumatic Brain Injury Visualized Using High-Resolution Diffusion Tensor Imaging. Journal of Neurotrauma, 24(5), 753-765. doi:10.1089/neu.2006.0208Levine, B., Fujiwara, E., O’connor, C., Richard, N., Kovacevic, N., Mandic, M., … Black, S. E. (2006). In Vivo Characterization of Traumatic Brain Injury Neuropathology with Structural and Functional Neuroimaging. Journal of Neurotrauma, 23(10), 1396-1411. doi:10.1089/neu.2006.23.1396Metting, Z., Rödiger, L. A., De Keyser, J., & van der Naalt, J. (2007). Structural and functional neuroimaging in mild-to-moderate head injury. The Lancet Neurology, 6(8), 699-710. doi:10.1016/s1474-4422(07)70191-6Nakayama, N. (2006). Relationship between regional cerebral metabolism and consciousness disturbance in traumatic diffuse brain injury without large focal lesions: an FDG-PET study with statistical parametric mapping analysis. Journal of Neurology, Neurosurgery & Psychiatry, 77(7), 856-862. doi:10.1136/jnnp.2005.080523Nakayama, N. (2006). Evidence for white matter disruption in traumatic brain injury without macroscopic lesions. Journal of Neurology, Neurosurgery & Psychiatry, 77(7), 850-855. doi:10.1136/jnnp.2005.077875O’Leary, D. D. M., Schlaggar, B. L., & Tuttle, R. (1994). Specification of Neocortical Areas and Thalamocortical Connections. Annual Review of Neuroscience, 17(1), 419-439. doi:10.1146/annurev.ne.17.030194.002223Mitelman, S. A., Byne, W., Kemether, E. M., Newmark, R. E., Hazlett, E. A., Haznedar, M. M., & Buchsbaum, M. S. (2006). Metabolic thalamocortical correlations during a verbal learning task and their comparison with correlations among regional volumes. Brain Research, 1114(1), 125-137. doi:10.1016/j.brainres.2006.07.043Laureys, S., Faymonville, M., Luxen, A., Lamy, M., Franck, G., & Maquet, P. (2000). Restoration of thalamocortical connectivity after recovery from persistent vegetative state. The Lancet, 355(9217), 1790-1791. doi:10.1016/s0140-6736(00)02271-6Laureys, S., Goldman, S., Phillips, C., Van Bogaert, P., Aerts, J., Luxen, A., … Maquet, P. (1999). Impaired Effective Cortical Connectivity in Vegetative State: Preliminary Investigation Using PET. NeuroImage, 9(4), 377-382. doi:10.1006/nimg.1998.0414Laureys, S., Owen, A. M., & Schiff, N. D. (2004). Brain function in coma, vegetative state, and related disorders. The Lancet Neurology, 3(9), 537-546. doi:10.1016/s1474-4422(04)00852-xGuye, M., Bartolomei, F., & Ranjeva, J.-P. (2008). Imaging structural and functional connectivity: towards a unified definition of human brain organization? Current Opinion in Neurology, 24(4), 393-403. doi:10.1097/wco.0b013e3283065cfbPrice, C. J., & Friston, K. J. (2002). Functional Imaging Studies of Neuropsychological Patients: Applications and Limitations. Neurocase, 8(5), 345-354. doi:10.1076/neur.8.4.345.16186Kim, J., Avants, B., Patel, S., Whyte, J., Coslett, B. H., Pluta, J., … Gee, J. C. (2008). Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study. NeuroImage, 39(3), 1014-1026. doi:10.1016/j.neuroimage.2007.10.005Maxwell, W. L., MacKinnon, M. A., Smith, D. H., McIntosh, T. K., & Graham, D. I. (2006). Thalamic Nuclei After Human Blunt Head Injury. Journal of Neuropathology & Experimental Neurology, 65(5), 478-488. doi:10.1097/01.jnen.0000229241.28619.75SIDAROS, A., SKIMMINGE, A., LIPTROT, M., SIDAROS, K., ENGBERG, A., HERNING, M., … ROSTRUP, E. (2009). Long-term global and regional brain volume changes following severe traumatic brain injury: A longitudinal study with clinical correlates. NeuroImage, 44(1), 1-8. doi:10.1016/j.neuroimage.2008.08.030Ashburner, J., & Friston, K. J. (2000). Voxel-Based Morphometry—The Methods. NeuroImage, 11(6), 805-821. doi:10.1006/nimg.2000.0582Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001). A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains. NeuroImage, 14(1), 21-36. doi:10.1006/nimg.2001.0786Giacino, J. T., Ashwal, S., Childs, N., Cranford, R., Jennett, B., Katz, D. I., … Zasler, N. D. (2002). The minimally conscious state: Definition and diagnostic criteria. Neurology, 58(3), 349-353. doi:10.1212/wnl.58.3.349Gispert, J. ., Pascau, J., Reig, S., Martínez-Lázaro, R., Molina, V., García-Barreno, P., & Desco, M. (2003). Influence of the normalization template on the outcome of statistical parametric mapping of PET scans. NeuroImage, 19(3), 601-612. doi:10.1016/s1053-8119(03)00072-7Ashburner, J., & Friston, K. J. (1999). Nonlinear spatial normalization using basis functions. Human Brain Mapping, 7(4), 254-266. doi:10.1002/(sici)1097-0193(1999)7:43.0.co;2-gTzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., … Joliot, M. (2002). Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain. NeuroImage, 15(1), 273-289. doi:10.1006/nimg.2001.0978Genovese, C. R., Lazar, N. A., & Nichols, T. (2002). Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate. NeuroImage, 15(4), 870-878. doi:10.1006/nimg.2001.1037LAUREYS, S., LEMAIRE, C., MAQUET, P., PHILLIPS, C., & FRANCK, G. (1999). Cerebral metabolism during vegetative state and after recovery to consciousness. Journal of Neurology, Neurosurgery & Psychiatry, 67(1), 121-122. doi:10.1136/jnnp.67.1.121Tommasino, C., Grana, C., Lucignani, G., Torri, G., & Fazio, F. (1995). Regional Cerebral Metabolism of Glucose in Comatose and Vegetative State Patients. Journal of Neurosurgical Anesthesiology, 7(2), 109-116. doi:10.1097/00008506-199504000-00006ANDERSON, C. V., WOOD, D.-M. G., BIGLER, E. D., & BLATTER, D. D. (1996). Lesion Volume, Injury Severity, and Thalamic Integrity following Head Injury. Journal of Neurotrauma, 13(2), 59-65. doi:10.1089/neu.1996.13.59Ge, Y., Patel, M. B., Chen, Q., Grossman, E. J., Zhang, K., Miles, L., … Grossman, R. I. (2009). Assessment of thalamic perfusion in patients with mild traumatic brain injury by true FISP arterial spin labelling MR imaging at 3T. Brain Injury, 23(7-8), 666-674. doi:10.1080/02699050903014899Uzan, M. (2003). Thalamic proton magnetic resonance spectroscopy in vegetative state induced by traumatic brain injury. Journal of Neurology, Neurosurgery & Psychiatry, 74(1), 33-38. doi:10.1136/jnnp.74.1.33OMMAYA, A. K., & GENNARELLI, T. A. (1974). CEREBRAL CONCUSSION AND TRAUMATIC UNCONSCIOUSNESS. Brain, 97(1), 633-654. doi:10.1093/brain/97.1.633Giacino, J., & Whyte, J. (2005). The Vegetative and Minimally Conscious States. Journal of Head Trauma Rehabilitation, 20(1), 30-50. doi:10.1097/00001199-200501000-00005Zeman, A. (2001). Consciousness. Brain, 124(7), 1263-1289. doi:10.1093/brain/124.7.1263Kinney, H. C., Korein, J., Panigrahy, A., Dikkes, P., & Goode, R. (1994). Neuropathological Findings in the Brain of Karen Ann Quinlan -- The Role of the Thalamus in the Persistent Vegetative State. New England Journal of Medicine, 330(21), 1469-1475. doi:10.1056/nejm199405263302101Saeeduddin Ahmed, Rex Bierley, Java. (2000). Post-traumatic amnesia after closed head injury: a review of the literature and some suggestions for further research. Brain Injury, 14(9), 765-780. doi:10.1080/026990500421886Wilson, J. T., Hadley, D. M., Wiedmann, K. D., & Teasdale, G. M. (1995). Neuropsychological consequences of two patterns of brain damage shown by MRI in survivors of severe head injury. Journal of Neurology, Neurosurgery & Psychiatry, 59(3), 328-331. doi:10.1136/jnnp.59.3.328Wilson, J. T., Teasdale, G. M., Hadley, D. M., Wiedmann, K. D., & Lang, D. (1994). Post-traumatic amnesia: still a valuable yardstick. Journal of Neurology, Neurosurgery & Psychiatry, 57(2), 198-201. doi:10.1136/jnnp.57.2.198Fearing, M. A., Bigler, E. D., Wilde, E. A., Johnson, J. L., Hunter, J. V., Xiaoqi Li, … Levin, H. S. (2008). Morphometric MRI Findings in the Thalamus and Brainstem in Children After Moderate to Severe Traumatic Brain Injury. Journal of Child Neurology, 23(7), 729-737. doi:10.1177/0883073808314159Little, D. M., Kraus, M. F., Joseph, J., Geary, E. K., Susmaras, T., Zhou, X. J., … Gorelick, P. B. (2010). Thalamic integrity underlies executive dysfunction in traumatic brain injury. Neurology, 74(7), 558-564. doi:10.1212/wnl.0b013e3181cff5d

Polymorphisms in Genes Involved in Osteoblast Differentiation and Function Are Associated with Anthropometric Phenotypes in Spanish Women

Much of the genetic variance associated with osteoporosis is still unknown. Bone mineral density (BMD) is the main predictor of osteoporosis risk, although other anthropometric phenotypes have recently gained importance. The aim of this study was to analyze the association of SNPs in genes involved in osteoblast differentiation and function with BMD, body mass index (BMI), and waist (WC) and hip (HC) circumferences. Four genes that affect osteoblast differentiation and/or function were selected from among the differentially expressed genes in fragility hip fracture (FOXC1, CTNNB1, MEF2C, and EBF2), and an association study of four single-nucleotide polymorphisms (SNPs) was conducted in a cohort of 1001 women. Possible allelic imbalance was also studied for SNP rs87939 of the CTNNB1 gene. We found significant associations of SNP rs87939 of the CTNNB1 gene with LS-sBMD, and of SNP rs1366594 of the MEF2C gene with BMI, after adjustment for confounding variables. The SNP of the MEF2C gene also showed a significant trend to association with FN-sBMD (p = 0.009). A possible allelic imbalance was ruled out as no differences for each allele were detected in CTNNB1 expression in primary osteoblasts obtained from homozygous women. In conclusion, we demonstrated that two SNPs in the MEF2C and CTNNB1 genes, both implicated in osteoblast differentiation and/or function, are associated with BMI and LS-sBMD, respectively

SNPs in bone-related miRNAs are associated with the osteoporotic phenotype

Biogenesis and function of microRNAs can be influenced by genetic variants in the pri-miRNA sequences leading to phenotypic variability. This study aims to identify single nucleotide polymorphisms (SNPs) affecting the expression levels of bone-related mature microRNAs and thus, triggering an osteoporotic phenotype. An association analysis of SNPs located in pri-miRNA sequences with bone mineral density (BMD) was performed in the OSTEOMED2 cohort (n = 2183). Functional studies were performed for assessing the role of BMD-associated miRNAs in bone cells. Two SNPs, rs6430498 in the miR-3679 and rs12512664 in the miR-4274, were significantly associated with femoral neck BMD. Further, we measured these BMD-associated microRNAs in trabecular bone from osteoporotic hip fractures comparing to non-osteoporotic bone by qPCR. Both microRNAs were found overexpressed in fractured bone. Increased matrix mineralization was observed after miR-3679-3p inhibition in human osteoblastic cells. Finally, genotypes of rs6430498 and rs12512664 were correlated with expression levels of miR-3679 and miR-4274, respectively, in osteoblasts. In both cases, the allele that generated higher microRNA expression levels was associated with lower BMD values. In conclusion, two osteoblast-expressed microRNAs, miR-3679 and miR-4274, were associated with BMD; their overexpression could contribute to the osteoporotic phenotype. These findings open new areas for the study of bone disorder