PubMed · 16457052
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Martha F Coyne. 2005. Technical tips: polarity and localization.. https://pubmed.ncbi.nlm.nih.gov/16457052/
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Population maps of the corpus callosum (CC) and cortical lobe connections were generated by combining cortical gray matter parcellation with the diffusion tensor fiber tractography of individual subjects. This method is based on the fact that the cortical lobes of both hemispheres are interconnected by the corpus callosal fibers. T1-weighted structural MRIs and diffusion tensor MRIs (DT-MRI) of 22 right-handed, healthy subjects were used. Forty-seven cortical parcellations in the dorsal prefrontal cortex, ventral prefrontal cortex, sensory-motor cortex, parietal cortex, temporal cortex, and occipital cortex were semi-automatically derived from structural MRIs, registered to DT-MRI, and used to identify callosal fibers. The probabilistic connections to each cortex were mapped on entire mid-sagittal CC voxels that had anatomical homology between subjects as determined by spatial registration. According to the population maps of the callosal connections, the ventral prefrontal cortex and parts of the dorsal prefrontal cortex both project fibers through the genu and rostrum. The CC regions through which the superior frontal cortex passes extend into the posterior body. Fibers arising from the parietal lobe and occipital lobe run mainly through the splenium, while fibers arising from the sensory-motor cortex pass through the isthmus. In general, dorsal or medial cortical lobes project fibers through the dorsal region of the CC, while lateral cortical lobes project fibers through the ventral region of the CC. The probabilistic subdivision of the CC by connecting cortical gray matter provides a more precise understanding of the CC.
Using high-resolution MRI, it is now possible to examine the living human cortex down to a resolution of less than 300 mum. Thus, in vivo imaging is now approaching the resolution that has been successfully used in histological analysis of the cerebral cortex for many years, e.g., low-magnification light microscopy. This allows unprecedented views of cortical microstructure that reflect defined histological features, specifically, individual cortical layers. As in histological brain mapping, it is possible to use the changes in the cortical lamination patterns to define individual cortical areas. This allows in vivo neuroanatomical maps to be generated for individual subjects and precise correlation of the results from functional imaging studies in these subjects with their own microanatomical information. To this end, we adapted the well-established observer-independent cytoarchitectonic mapping techniques for defining cortical borders based on changes in cortical lamination for in vivo parcellation of high-resolution structural MR images.
Using functional imaging, we investigated the effects of two different tasks on activation in the lateral occipital complex (LOC). Alternating blocks of intact and scrambled objects were presented. In one task, subjects responded when an object repeated (matching task). In a second task subjects silently named objects (naming task). Identical objects (tools, animals and letters) were presented for both tasks. A relative measure of the number of voxels activated in LOC in left and right hemispheres was calculated for each task across a range of thresholds. Also the effects of task demands on category specific areas in LOC were examined. The object matching task resulted in proportionally more activity in the right hemisphere. The object naming task resulted in proportionally more activity in the left hemisphere, most prominently in the anterior portion of LOC. Effectively, changing the task changed the lateralization of activation to intact objects in LOC. In contrast, changing the task did not change the lateralization of category-specific activations. The results suggest that there are task-related top-down influences on the activation of neural populations in LOC as a whole, but the lateralization of category-specific regions in LOC is independent of task demands and may reflect bottom-up processing.