360 research outputs found

    Topography of the Chimpanzee Corpus Callosum

    Get PDF
    The corpus callosum (CC) is the largest commissural white matter tract in mammalian brains, connecting homotopic and heterotopic regions of the cerebral cortex. Knowledge of the distribution of callosal fibers projecting into specific cortical regions has important implications for understanding the evolution of lateralized structures and functions of the cerebral cortex. No comparisons of CC topography in humans and great apes have yet been conducted. We investigated the topography of the CC in 21 chimpanzees using high-resolution magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Tractography was conducted based on fiber assignment by continuous tracking (FACT) algorithm. We expected chimpanzees to display topographical organization similar to humans, especially concerning projections into the frontal cortical regions. Similar to recent studies in humans, tractography identified five clusters of CC fibers projecting into defined cortical regions: prefrontal; premotor and supplementary motor; motor; sensory; parietal, temporal and occipital. Significant differences in fractional anisotropy (FA) were found in callosal regions, with highest FA values in regions projecting to higher-association areas of posterior cortical (including parietal, temporal and occipital cortices) and prefrontal cortical regions (p<0.001). The lowest FA values were seen in regions projecting into motor and sensory cortical areas. Our results indicate chimpanzees display similar topography of the CC as humans, in terms of distribution of callosal projections and microstructure of fibers as determined by anisotropy measures

    Tractography of the Spider Monkey (\u3cem\u3eAteles geoffroyi\u3c/em\u3e) Corpus Callosum Using Diffusion Tensor Magnetic Resonance Imaging

    Get PDF
    The objective of this research was to describe the organization, connectivity and microstructure of the corpus callosum of the spider monkey (Ateles geoffroyi). Non-invasive magnetic resonance imaging and diffusion-tensor imaging were obtained from three subjects using a 3T Philips scanner. We hypothesized that the arrangement of fibers in spider monkeys would be similar to that observed in other non-human primates. A repeated measure (n = 3) of fractional anisotropy values was obtained of each subject and for each callosal subdivision. Measurements of the diffusion properties of corpus callosum fibers exhibited a similar pattern to those reported in the literature for humans and chimpanzees. No statistical difference was reached when comparing this parameter between the different CC regions (p = 0.066). The highest fractional anisotropy values corresponded to regions projecting from the corpus callosum to the posterior cortical association areas, premotor and supplementary motor cortices. The lowest fractional anisotropy corresponded to projections to motor and sensory cortical areas. Analyses indicated that approximately 57% of the fibers projects to the frontal cortex and 43% to the post-central cortex. While this study had a small sample size, the results provided important information concerning the organization of the corpus callosum in spider monkeys

    Data-driven corpus callosum parcellation method through diffusion tensor imaging

    Get PDF
    The corpus callosum (CC) is a set of neural fibers in the cerebral cortex, responsible for facilitating inter-hemispheric communication. The CC structural characteristics appear as an essential element for studying healthy subjects and patients diagnosed with neurodegenerative diseases. Due to its size, the CC is usually divided into smaller regions, also known as parcellation. Since there are no visible landmarks inside the structure indicating its division, CC parcellation is a challenging task and methods proposed in the literature are geometric or atlas-based. This paper proposed an automatic data-driven CC parcellation method, based on diffusion data extracted from diffusion tensor imaging that uses the Watershed transform. Experiments compared parcellation results of the proposed method with results of three other parcellation methods on a data set containing 150 images. Quantitative comparison using the Dice coefficient showed that the CC parcels given by the proposed method has a mean overlap higher than 0,9 for some parcels and lower than 0,6 for other parcels. Poor overlap results were confirmed by the statistically significant differences obtained for diffusion metrics values in each parcel, when using different parcellation methods. The proposed method was also validated by using the CC tractography and was the only study that proposed a non-geometric approach for the CC parcellation, based only on the diffusion data of each subject analyzed59Advanced signal processing methods in medical imaging2242122432COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPnão tem2013/07559-

    Automatic Dti-based Parcellation Of The Corpus Callosum Through The Watershed Transform

    Get PDF
    Introduction: Parcellation of the corpus callosum (CC) in the midsagittal cross-section of the brain is of utmost importance for the study of diffusion properties within this structure. The complexity of this operation comes from the absence of macroscopic anatomical landmarks to help in dividing the CC into different callosal areas. In this paper we propose a completely automatic method for CC parcellation using diffusion tensor imaging (DTI). Methods: A dataset of 15 diffusion MRI volumes from normal subjects was used. For each subject, the midsagital slice was automatically detected based on the Fractional Anisotropy (FA) map. Then, segmentation of the CC in the midsgital slice was performed using the hierarchical watershed transform over a weighted FA-map. Finally, parcellation of the CC was obtained through the application of the watershed transform from chosen markers. Results: Parcellation results obtained were consistent for fourteen of the fifteen subjects tested. Results were similar to the ones obtained from tractography-based methods. Tractography confirmed that the cortical regions associated with each obtained CC region were consistent with the literature. Conclusions: A completely automatic DTI-based parcellation method for the CC was designed and presented. It is not based on tractography, which makes it fast and computationally inexpensive. While most of the existing methods for parcellation of the CC determine an average behavior for the subjects based on population studies, the proposed method reflects the diffusion properties specific for each subject. Parcellation boundaries are found based on the diffusion properties within each individual CC, which makes it more reliable and less affected by differences in size and shape among subjects.302132143Aboitiz, F., Scheibel, A.B., Fisher, R.S., Zaidel, E., Fiber composition of the human corpus callosum (1992) Brain Research, 598 (1-2), pp. 143-153. , http://dx.doi.org/10.1016/0006-8993(92)90178-CBasser, P.J., Pierpaoli, C., Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI (1996) Journal of Magnetic Resonance, Series B, 111 (3), pp. 209-219. , http://dx.doi.org/10.1006/jmrb.1996.0086Basser, P.J., Mattiello, J., LeBihan, D., MR diffusion tensor spectroscopy and imaging (1994) Biophysical Journal, 66 (1), pp. 259-267. , http://dx.doi.org/10.1016/S0006-3495(94)80775-1Beucher, S., Lantuéjoul, C., (1979) Use of watersheds in contour detection, , In: International Workshop on Image Processing: Proceedings of the International Workshop on Image Processing: Real-time Edge and Motion Detection/EstimationRennes, FranceBeucher, S., Meyer, F., (1992) The morphological approach to segmentation: The Watershed Transformation, pp. 433-481. , Mathematical Morphology in Image Processing (CRC Press)Biegon, A., Eberling, J.L., Richardson, B.C., Roos, M.S., Wong, S.T., Reed, B.R., Jagust, W.J., Human corpus callosum in aging and alzheimer's disease: A magnetic resonance imaging study (1994) Neurobiology of Aging, 15 (4), pp. 393-397. , http://dx.doi.org/10.1016/0197-4580(94)90070-1Chepuri, N.B., Yen, Y.F., Burdette, J.H., Li, H., Moody, D.M., Maldjian, J.A., Diffusion anisotropy in the corpus callosum (2002) American journal of Neuroradiology, 23 (5), pp. 803-808. , PMid: 12006281DeLacoste-Utamsing, C., Holloway, R., Sexual dimorphism in the human corpus callosum (1982) Science, 216 (4553), pp. 1431-1432. , http://dx.doi.org/10.1126/science.7089533Digabel, H., Lantuéjoul, C., Iterative algorithms, pp. 85-99. , In: European Symposium Quantitative Analysis of Microstructures in Material Science, Biology and Medicine: Proceedings of the 2nd European Symposium Quantitative Analysis of Microstructures in Material Science, Biology and Medicine1978Dougherty, R.F., Ben-Shachar, M., Bammer, R., Brewer, A.A., Wandell, B.A., Functional organization of human occipital-callosal fiber tracts (2005) Proceedings of the National Academy of Sciences of the United States of America, 102 (20), pp. 7350-7355. , http://dx.doi.org/10.1073/pnas.0500003102, PMid: 15883384 PMCid: PMC1129102Duara, R., Kushch, A., Gross-Glenn, K., Barker, W.W., Jallad, B., Pascal, S., Loewenstein, D.A., Lubs, H., Neuroanatomic differences between dyslexic and normal readers on magnetic resonance imaging scans (1991) Archives of Neurology, 48 (4), pp. 410-416. , http://dx.doi.org/10.1001/archneur.1991.00530160078018, PMid: 2012516Falcão, A.X., Stolfi, J., Lotufo, R.A., The image foresting transform: Theory, algorithms, and applications (2004) IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (1), pp. 19-29. , http://dx.doi.org/10.1109/TPAMI.2004.1261076, PMid: 15382683Freitas, P., Rittner, L., Appenzeller, S., Lotufo, R.A., Watershed-based segmentation of the midsagittal section of the corpus callosum in diffusion MRI IEEE Computer Society, pp. 274-280. , In: Graphics, Patterns and Images, Conference on: Proceedings of the 24th Conference on Graphics, Patterns and Images2011Grimaud, M., A new measure of contrast: The dynamics (1992) Image Algebra and Morphological Image Processing III, 1769, pp. 292-305. , http://dx.doi.org/10.1117/12.60650Habib, M., Gayraud, D., Oliva, A., Regis, J., Salamon, G., Khalil, R., Effects of handedness and sex on the morphology of the corpus callosum: A study with brain magnetic resonance imaging (1991) Brain and Cognition, 16 (1), pp. 41-61. , http://dx.doi.org/10.1016/0278-2626(91)90084-LHampel, H., Teipel, S.J., Alexander, G.E., Horwitz, B., Teichberg, D., Schapiro, M.B., Rapoport, S.I., Corpus callosum atrophy is a possible indicator of region-and cell type-specific neuronal degeneration in Alzheimer disease: A magnetic resonance imaging analysis (1998) Archives of Neurology, 55 (2), pp. 193-198. , http://dx.doi.org/10.1001/archneur.55.2.193, PMid: 9482361Hofer, S., Frahm, J., Topography of the human corpus callosum revisited-comprehensive fiber tractography using diffusion tensor magnetic resonance imaging (2006) NeuroImage, 32 (3), pp. 989-994. , http://dx.doi.org/10.1016/j.neuroimage.2006.05.044, PMid: 16854598Huang, H., Zhang, J., Jiang, H., Wakana, S., Poetscher, L., Miller, M.I., van Zijl, P.C., Mori, S., DTI tractography based parcellation of white matter: Application to the mid-sagittal morphology of corpus callosum (2005) NeuroImage, 26 (1), pp. 195-205. , http://dx.doi.org/10.1016/j.neuroimage.2005.01.019, PMid: 15862219Johnson, S.C., Farnworth, T., Pinkston, J.B., Bigler, E.D., Blatter, D.D., Corpus callosum surface area across the human adult life span: Effect of age and gender (1994) Brain Research Bulletin, 35 (4), pp. 373-377. , http://dx.doi.org/10.1016/0361-9230(94)90116-3Körbes, A., Lotufo, R.A., Analysis of the watershed algorithms based on the Breadth-First and Depth-First exploring methods (2009) IEEE Computer Society, pp. 133-140. , http://dx.doi.org/10.1109/SIBGRAPI.2009.43, In: Computer Graphics and Image Processing, Brazilian Symposium on: Proceedings of the 22th Brazilian Symposium on Computer Graphics and Image Processing2009Rio de Janeiro, BrazilLarsen, J.P., Höien, T., Odegaard, H., Magnetic resonance imaging of the corpus callosum in developmental dyslexia (1992) Cognitive Neuropsychology, 9 (2), pp. 123-134. , http://dx.doi.org/10.1080/02643299208252055Lotufo, R.A., Falcão, A.X., The ordered queue and the optimality of the watershed approaches Kluwer Academic Publishersv, pp. 341-350. , http://dx.doi.org/10.1007/0-306-47025-X_37, In: Mathematical Morphology and its Applications to Image and Signal Processing: Proceedings of the 5th International Symposium on Mathematical Morphology and its Applications to Image and Signal Processing2000, 18Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C.M., Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging (1999) Annals of Neurology, 45 (2), pp. 265-269. , http://dx.doi.org/10.1002/1531-8249(199902)45:2265::AID-ANA213.0.CO;2-3Narr, K.L., Thompson, P.M., Sharma, T., Moussal, J., Cannestra, A.F., Toga, A.W., Mapping morphology of the corpus callosum in schizophrenia (2000) Cerebral cortex (New York, NY, 1991), 10 (1), pp. 40-49. , http://dx.doi.org/10.1093/cercor/10.1.40Narr, K.L., Cannon, T.D., Woods, R.P., Thompson, P.M., Kim, S., Asunction, D., van Erp, T.G., Toga, A.W., Genetic Contributions to Altered Callosal Morphology in Schizophrenia The Journal of Neuroscience, 22 (9), pp. 3720-3729. , PMid: 11978848O'Dwyer, L., Lamberton, F., Bokde, A.L.W., Ewers, M., Faluyi, Y.O., Tanner, C., Mazoyer, B., Hampel, H., Multiple indices of diffusion identifies white matter damage in mild cognitive impairment and Alzheimer's disease (2011) PLoS one, 6 (6), pp. e21745. , http://dx.doi.org/10.1371/journal.pone.0021745, PMid: 21738785 PMCid: PMC3128090Oh, J.S., Suk Park, K., Chan Song, I., Ju Kim, S., Hwang, J., Chung, A., Kyoon Lyoo, I., Fractional anisotropy-based divisions of midsagittal corpus callosum (2005) Neuroreport, 16 (4), pp. 317-320. , http://dx.doi.org/10.1097/00001756-200503150-00002Park, H.J., Kim, J.J., Lee, S.K., Seok, J.H., Chun, J., Kim, D.I., Lee, J.D., Corpus callosal connection mapping using cortical gray matter parcellation and DT-MRI (2008) Human Brain Mapping, 29 (5), pp. 503-516. , http://dx.doi.org/10.1002/hbm.20314, PMid: 17133394Park, J.S., Yoon, U., Kwak, K.C., Seo, S.W., Kim, S.I., Na, D.L., Lee, J.M., The relationships between extent and microstructural properties of the midsagittal corpus callosum in human brain (2011) NeuroImage, 56 (1), pp. 174-184. , http://dx.doi.org/10.1016/j.neuroimage.2011.01.065, PMid: 21281715Rajapakse, J.C., Giedd, J.N., Rumsey, J.M., Vaituzis, A.C., Hamburger, S.D., Rapoport, J.L., Regional MRI measurements of the corpus callosum: A methodological and developmental study (1996) Brain and Development, 18 (5), pp. 379-388. , http://dx.doi.org/10.1016/0387-7604(96)00034-4Rumsey, J.M., Casanova, M., Mannheim, G.B., Patronas, N., De Vaughn, N., Hamburger, S.D., Aquino, T., Corpus callosum morphology, as measured with MRI, in dyslexic men Biological Psychiatry, 39 (9), pp. 769-775. , http://dx.doi.org/10.1016/0006-3223(95)00225-1Rosas, H.D., Lee, S.Y., Bender, A.C., Zaleta, A.K., Vangel, M., Yu, P., Fischl, B., Hersch, S.M., Altered white matter microstructure in the corpus callosum in Huntington's disease: Implications for cortical disconnection (2010) NeuroImage, 49 (4), pp. 2995-3004. , http://dx.doi.org/10.1016/j.neuroimage.2009.10.015, PMid: 19850138 PMCid: PMC3725957Thompson, P.M., Narr, K.L., Blanton, R.E., Toga, A.W., Mapping structural alterations of the corpus callosum during brain development and degeneration (2003) Proceedings of the NATO ASI on the corpus callosum, pp. 93-130Von Plessen, K., Lundervold, A., Duta, N., Heiervang, E., Klauschen, F., Smievoll, A.I., Ersland, L., Hugdahl, K., Less developed corpus callosum in dyslexic subjects-a structural MRI study (2002) Neuropsychologia, 40 (7), pp. 1035-1044. , http://dx.doi.org/10.1016/S0028-3932(01)00143-9Wahl, M., Lauterbach-Soon, B., Hattingen, E., Jung, P., Singer, O., Volz, S., Klein, J.C., Ziemann, U., Human motor corpus callosum: Topography, somatotopy, and link between microstructure and function (2007) Journal of Neuroscience, 27 (45), pp. 12132-12138. , http://dx.doi.org/10.1523/JNEUROSCI.2320-07.2007, PMid: 17989279Witelson, S.F., Hand and sex differences in the isthmus and genu of the human corpus callosum. A postmortem morphological study (1989) Brain, 112 (PART 3), pp. 799-835. , http://dx.doi.org/10.1093/brain/112.3.799, PMid: 2731030Witelson, S.F., Goldsmith, C.H., The relationship of hand preference to anatomy of the corpus callosum in men (1991) Brain Research, 545 (1-2), pp. 175-182. , http://dx.doi.org/10.1016/0006-8993(91)91284-

    Fractional Anisotropy in Corpus Callosum Is Associated with Facilitation of Motor Representation during Ipsilateral Hand Movements

    Get PDF
    BACKGROUND: Coactivation of primary motor cortex ipsilateral to a unilateral movement (M1(ipsilateral)) has been observed, and the magnitude of activation is influenced by the contracting muscles. It has been suggested that the microstructural integrity of the callosal motor fibers (CMFs) connecting M1 regions may reflect the observed response. However, the association between the structural connectivity of CMFs and functional changes in M1(ipsilateral) remains unclear. The purpose of this study was to investigate the relationship between functional changes within M1(ipsilateral) during unilateral arm or leg movements and the microstructure of the CMFs connecting both homotopic representations (arm or leg). METHODS: Transcranial magnetic stimulation was used to assess changes in motor evoked potentials (MEP) in an arm muscle during unilateral movements compared to rest in fifteen healthy adults. Functional magnetic resonance imaging was then used to identify regions of M1 associated with either arm or leg movements. Diffusion-weighted imaging data was acquired to generate CMFs for arm and leg areas using the areas of activation from the functional imaging as seed masks. Individual values of regional fractional anisotropy (FA) of arm and leg CMFs was then calculated by examining the overlap between CMFs and a standard atlas of corpus callosum. RESULTS: The change in the MEP was significantly larger in the arm movement compared to the leg movement. Additionally, regression analysis revealed that FA in the arm CMFs was positively correlated with the change in MEP during arm movement, whereas a negative correlation was observed during the leg movement. However, there was no significant relationship between FA in the leg CMF and the change in MEP during the movements. CONCLUSIONS: These findings suggest that individual differences in interhemispheric structural connectivity may be used to explain a homologous muscle-dominant effect within M1(ipsilateral) hand representation during unilateral movement with topographical specificity

    Automatic DTI-based parcellation of the corpus callosum through the watershed transform

    Get PDF
    Parcellation of the corpus callosum (CC) in the midsagittal cross-section of the brain is of utmost importance for the study of diffusion properties within this structure. The complexity of this operation comes from the absence of macroscopic anatomical landmarks to help in dividing the CC into different callosal areas. In this paper we propose a completely automatic method for CC parcellation using diffusion tensor imaging (DTI). A dataset of 15 diffusion MRI volumes from normal subjects was used. For each subject, the midsagital slice was automatically detected based on the Fractional Anisotropy (FA) map. Then, segmentation of the CC in the midsgital slice was performed using the hierarchical watershed transform over a weighted FA-map. Finally, parcellation of the CC was obtained through the application of the watershed transform from chosen markers. Parcellation results obtained were consistent for fourteen of the fifteen subjects tested. Results were similar to the ones obtained from tractography-based methods. Tractography confirmed that the cortical regions associated with each obtained CC region were consistent with the literature. A completely automatic DTI-based parcellation method for the CC was designed and presented. It is not based on tractography, which makes it fast and computationally inexpensive. While most of the existing methods for parcellation of the CC determine an average behavior for the subjects based on population studies, the proposed method reflects the diffusion properties specific for each subject. Parcellation boundaries are found based on the diffusion properties within each individual CC, which makes it more reliable and less affected by differences in size and shape among subjects302132143CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPnão temnão temnão te

    Transcallosal sensorimotor fiber tract structure‐function relationships

    Full text link
    Recent studies have demonstrated neuroanatomically selective relationships among white matter tract microstructure, physiological function, and task performance. Such findings suggest that the microstructure of transcallosal motor fibers may reflect the capacity for interhemispheric inhibition between the primary motor cortices, although full characterization of the transcallosal inhibitory sensorimotor network is lacking. Thus, the goal of this study was to provide a comprehensive description of transcallosal fibers connecting homologous sensorimotor cortical regions and to identify the relationship(s) between fiber tract microstructure and interhemispheric inhibition during voluntary cortical activity. To this end, we assessed microstructure of fiber tracts connecting homologous sensorimotor regions of the cortex with diffusion tensor imaging. We also assessed interhemispheric inhibition by eliciting the ipsilateral silent period (iSP) within the same participants. We mapped mutually exclusive transcallosal connections between homologous sensorimotor regions and computed quantitative metrics of each fiber tract. Paralleling work in non‐human primates, we found the densest interhemispheric sensorimotor connections to be between the medial motor areas. Additionally, we provide a midsagittal callosal atlas in normalized Montreal Neurological Institute (MNI) space for future studies to use when investigating callosal fiber tracts connecting primary and secondary sensorimotor cortices. Finally, we report a strong, positive relationship ( r = 0.76) between strength of interhemispheric inhibition (iSP) and microstructure of interhemispheric fibers that is specific to tracts connecting the primary motor cortices. Thus, increased fiber microstructure in young adults predicts interhemispheric inhibitory capacity. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96360/1/21437_ftp.pd

    Glioma infiltration of the corpus callosum: early signs detected by DTI

    Get PDF
    The most frequent primary brain tumors, anaplastic astrocytomas (AA) and glioblastomas (GBM): tend to invasion of the surrounding brain. Histopathological studies found malignant cells in macroscopically unsuspicious brain parenchyma remote from the primary tumor, even affecting the contralateral hemisphere. In early stages, diffuse interneural infiltration with changes of the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) is suspected. The purpose of this study was to investigate the value of DTI as a possible instrument of depicting evidence of tumor invasion into the corpus callosum (CC). Preoperatively, 31 patients with high-grade brain tumors (8 AA and 23 GBM) were examined by MRI at 3 T, applying a high-resolution diffusion tensor imaging (DTI) sequence. ADC- and FA-values were analyzed in the tumor-associated area of the CC as identified by fiber tracking, and were compared to matched healthy controls. In (MR-)morphologically normal appearing CC the ADC values were elevated in the tumor patients (n = 22; 0.978 × 10(−3) mm²/s) compared to matched controls (0.917 × 10(−3) mm²/s, p < 0.05), and the corresponding relative FA was reduced (rFA: 88 %, p < 0.01). The effect was pronounced in case of affection of the CC visible on MRI (n = 9; 0.978 × 10(−3) mm²/s, p < 0.05; rFA: 72 %, p < 0.01). Changes in diffusivity and anisotropy in the CC can be interpreted as an indicator of tumor spread into the contralateral hemisphere not visible on conventional MRI
    corecore