Morphological differences between minicolumns in human and nonhuman primate cortex.код для вставкиСкачать
AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 115:361–371 (2001) Morphological Differences Between Minicolumns in Human and Nonhuman Primate Cortex Daniel P. Buxhoeveden,1,2* Andrew E. Switala,1 Emil Roy,1 Mark Litaker,3 and Manuel F. Casanova1 1 Department of Psychiatry, Medical College of Georgia, Augusta, Georgia 30904 Department of Anthropology, University of South Carolina, Columbia, South Carolina 29208 3 Department of Biostatistics, Medical College of Georgia, Augusta, Georgia 30904 2 KEY WORDS brain evolution; planum temporale; language ABSTRACT Our study performed a quantitative investigation of minicolumns in the planum temporale (PT) of human, chimpanzee, and rhesus monkey brains. This analysis distinguished minicolumns in the human cortex from those of the other nonhuman primates. Human cell columns are larger, contain more neuropil space, and pack more cells into the core area of the The search for anatomical differences between the brains of humans and nonhuman primates, especially the African apes, has proven limited. This applies especially to areas of the brain long associated with language, despite the uniqueness of the human form of language among primates (Deacon, 1997). MR imaging studies report identical asymmetries of the planum temporale (PT) in both humans and the African great apes, though not in Old or New World monkeys (Gannon et al., 1998; Hopkins et al., 1998). The use of MR-imaging has also found the human frontal lobe to be no larger than expected in an ape brain our size, despite some long-held assumptions to the contrary (Semendeferi et al., 1997). It is important to note that decades ago, data countered the notion that humans had relatively more frontal lobe (Brodmann, 1912; Brummelkamp, 1939; Holloway, 1968; LeBoucq, 1928.). Arguably, information regarding cortical volume alone is limited and difficult to interpret (Deacon, 1997). This assertion does not diminish the importance of macroscopic studies, since microscopic studies also involve difficulties of their own. Rather, basing comparisons of primate brains on macroanatomical structures alone necessarily overlooks the proliferation of functional units in large regions of the cortex; each has the potential for different pathways and intracortical connections. Macroscopic examination of the brain at the gross anatomical level, therefore, often fails to adequately detect organizational differences in cortical tissue contained within these units. While it provides information at one level of inquiry (e.g., is there more or less temporal lobe?), it cannot determine what is occurring beneath the tissue. As Holloway (1968) © 2001 WILEY-LISS, INC. column than those of the other primates tested. Because the minicolumn is a basic anatomical and functional unit of the cortex, this strong evidence showed reorganization in this area of the human brain. The relationship between the minicolumn and cortical volume is also discussed. Am J Phys Anthropol 115:361–371, 2001. © 2001 Wiley-Liss, Inc. asserted, “A comparison of mass is not a comparison of equal units.” A brief list of some of the more recent comparisons between human and nonhuman primate brains includes studies of morphology and size (Conroy et al., 1998, 2000; Falk et al., 1989, 1999; Holloway, 1968, 1973, 1974; Jerison, 1977, 1985; Radinsky, 1972, 1973, 1975, 1979; Rilling and Insel, 1999; Semendeferi and Damasio, 2000; Semendeferi et al., 1997, 1998), metabolism (Armstrong, 1990), histology (Holloway, 1968; Heilbroner and Holloway, 1989), gyral folding (Armstrong et al., 1991; Zilles et al., 1988, 1989), cerebral blood flow (Falk et al., 1988; Falk, 1993; Tobias and Falk, 1988), and cerebral asymmetries (Corballis et al., 2000; Falk, 1978; Gazzaniga, 2000a,b; Holloway and De La Costelareymondie, 1982; LeMay and Geschwind, 1975; LeMay, 1976, 1985; Yeni-Komshian and Benson, 1976). To discern differences between humans and primate brains, research must explore the internal operations of the cortex in ways that best interface anatomy with physiology. To do this, we propose using the cortical minicolumn as a template. We have found significant differences between the morphology of cell columns in the human planum temporale and those of the chimpanzee or monkey (Fig. 1). Researchers have generally assumed that minicolumns share the same morphology among species (Carpenter et al., 1976; Mountcastle, 1997). How*Correspondence to: Daniel Buxhoeveden, Ph.D., Downtown VA Medical Center, 116-A, Psychiatry Service, 3B-121, Augusta, GA 30904. E-mail: [email protected] Received 19 September 2000; accepted 6 May 2001. 362 D.P. BUXHOEVEDEN ET AL. Fig. 1. Vertical cell columns in lamina III in the PT of primate cortex. Viewed at 100⫻. ever, no one has consistently examined this topic, despite indications of differences across species and between different cortical regions of the brain (Buxhoeveden et al., 1996; Peters and Sethares, 1996; Preuss et al., 1999; Schlaug et al., 1995; Seldon, 1981a,b). Recent work demonstrates that although researchers have long assumed that humans and primates have very similar primary visual cortexes, distinctive morphological differences exist between human and chimpanzee V1 in lamina 4 (Preuss et al., 1999). The minicolumn is a basic anatomical and physiological unit of many areas of the brain (Favorov and Kelly, 1994a– c; Mountcastle, 1997). However, this may not hold true for the entire cortex (Jones, 2000; Swindale, 2000). The minicolumn interfaces cellular organization with the larger macrocolumn or module (Fig. 2). The physiological basis for the minicolumn has been supported by experiments using microelectrodes, nerve regeneration, optical density, and metabolic activity (Favorov and Whitsel, 1988a,b; Favorov and Diamond, 1990; Kaas et al., 1981; Kohn et al., 1997; Lee and Whitsel, 1992; Mountcastle, 1957, 1978, 1997; Tommerdahl et al., 1993; Yuste et al., 1992). Because it is a fundamental anatomical and physiological unit of the cortex, changes in the morphology of the column may be expected to reflect alterations in function. Columns are easy to recognize with the aid of a low-power microscope in many areas of the cortex. However, morphological differences of the kind described below are not discernible to the naked eye and require quantified computerized imaging. The size of minicolumns based on in vivo physiological studies, primarily in the somatosensory and visual cortex, seems to range between 30 – 60 m (Favorov and Whitsel, 1988a,b; Favorov and Diamond, 1990; Kaas et al., 1981; Peters and Yilmaz, 1993; Peters and Walsh, 1972). The smaller columns are found in the primary visual cortex. A mean size of 56 m, based on apical dendrite clusters, has been suggested across many different species including rabbits, rats, cats, monkeys, and humans (Mountcastle, 1997). A metabolic (2DG) examination of columns in the somatosensory cortexes of cats and monkeys revealed column widths between 30 –50 m (Tommerdahl et al., 1993). It is also important to recognize that cell column size can vary between cortical areas (Buxhoeveden et al., 1996; Schlaug et al., 1995). The use of the minicolumn in clinical neurology and comparative studies is just beginning to make an impact. Recent studies reveal modular organization in the posterior portion of area 22 in humans (Galuske, 2000), and the structure of minicolumns has been studied in Alzheimer’s disease (Buldyrev et al, 2000). MATERIALS AND METHODS Materials We examined the planum temporale in the left hemispheres in a population of brains at the Yakovlev-Haleem Collection in Washington, DC. We compared nine human, eight chimpanzee, and seven rhesus monkey brains. The human samples were adult except for one 16-year-old. The chimpanzees averaged 8.6 years, with two of them at 13 years and one 4-year-old. We considered the lower ages important, since the average age of the chimpanzee brains was generally younger than that of the humans. The rhesus monkey brains were listed as either adult or young adult. We focused on cell columns in lamina III, because the pyramidal cells and the vertical organization are most pronounced in this layer. At higher magnifications, anatomical columns were often very difficult to discern in lamina IV and were essentially nonexistent in layer II. Our present method works best for vertically oriented columns, which are found most consistently in lamina III. A comparison of laminae in the human cortex demonstrates that layer III in Tpt is the most conservative, having undergone the least amount of change from the fetal radial cell column (Buxhoeveden et al., 1996, 2000). In the future, we will be examining other laminae for comparative differences across species. Potential problems include shrinkage from tissue preparation, Z-axis artifact, and the plane of cut, all of which may cause distortion. For more information, the reader is referred to Buxhoeveden et al. (2000). MORPHOLOGICAL DIFFERENCES IN MINICOLUMNS 363 rior axis in coronal section. Correction factors in this case are based on measurements of atrophy (disease) and shrinkage (processing) in each brain, i.e., (V1/V2)1/3 where V1 and V2 are the tissue volume before and after processing, respectively. In the cerebral cortex, our computerized methods detect and identify cells unidirectionally from pia to white matter or vice versa, making unnecessary corrections for shrinkage in this dimension. Because the width of the column cortex measured and the thickness of section are constant, shrinkage of the cortex will result in overcounting of neurons. Therefore, correction factors are necessary in these two dimensions, i.e., (V2/V2)2/3. Corsellis et al. (1975) argued that for nitrocellulose-embedded tissue, shrinkage considerations may be omitted for cases where cell count per unit area (defined by natural boundaries) is the only parameter considered. The material in this study limits possible confounds by defining areas based on anatomical boundaries and by embedding the material in plastic. Since brain weights are available for human and most of the monkey brains before and after fixation, appropriate correction factors can be applied. These corrections factors are minimal. The brains available for our study were fixed in 10% formalin for several months. As the distortion due to fixation stabilizes after no more than approximately 3 weeks (Quester and Schroder, 1997), all brains would have reached their stable, postfixation state when studied. Thus, the duration of fixation can be ruled out as a potential confound. Unfortunately, processing information was available only for the human brains and four monkeys, and not for the chimpanzees. However, there is no reason the chimpanzee brains should display shrinkage outside the range of the monkeys or humans, especially since all brains were handled and processed according to standardized procedures, a feature of the Yakovlev-Haleem collection. The amount of shrinkage in human brains ranged from a low of 5% to a high of 35.8%. The mean was 28.7%. For the monkeys the range was much tighter, with a low of 24.7% and a high of 34.7%. The mean was 29.4% and thus essentially identical to the humans and totally within their range. Fig. 2. The minicolumn is a bridge between macroanatomy and microanatomy. It is also a bridge between anatomy and physiology. Shrinkage To eliminate considerations of shrinkage factors in two dimensions, it is necessary to assess all neurons on a particular section delimited by anatomical boundaries (e.g., Tpt) rather than on an arbitrary grid. However, as the same section thickness is used in all cases where this neuronal population is counted or identified, shrinkage must still be corrected for depth of section, i.e., the anterior-poste- Z-axis artifact The potential for Z-axis overlap of cells from other minicolumns or minicolumns dissected by sectioning is a problem present in this kind of cytoarchitectural analysis. We have dealt with it as follows: 1) We omitted from analysis lines of cells that display a lack of continuity, such as large empty spaces in the center of the minicolumn, indicative of Z-axis artifact, and which do not match the general pattern of unequivocally clear minicolumns. 2) We used a threshold level to eliminate incomplete cellular minicolumns. From an analysis of thousands of minicolumns, the mean cell number for each lamina becomes the standard to determine the threshold 364 D.P. BUXHOEVEDEN ET AL. level. Generally, we will not consider minicolumns with fewer than 10 cells in them complete. This level is set by the operator and can be changed at any time before or after data has been acquired. 3) The 35-m depth of section was approximately equal to the radial size of a single cell column in the typical human adult cortex (taking into account shrinkage factors; see below). Though this does not eliminate the problem, it minimizes the potential for too much Z-axis overlap within one section. Plane of cut Quite possibly, the orientation of the cut can affect some of the morphological parameters we use to describe minicolumns. This can be considered a random error with bias (e.g., where length of minicolumns tends to be reduced). Since results tend to occur in one direction, no simple way exists to average its effects. Measuring segments of minicolumns helps avoid this error. Barring any undue manipulation, to the extent bias remains due to the plane of cut, it would be a constant variable with an effect equal or similar in both populations (i.e., study and control). In the past, the relationship of the VI/white matter border to the pial surface has guided our determination of orientation (Buxhoeveden et al., 1996). Others have used similar visual approaches to measure minicolumns (Kohn et al., 1997; Tommerdahl et al., 1993). With whole brains available for study, we can control for orientation, selecting the optimal plane of cut for each cortical area to be studied. The materials were Nissl-stained, celloidin-embedded, and cut in the coronal or sagittal plane at 35-m thickness. Since we found no differences between the two planes of cut, we could see no theoretical reason why analyses of cell columns would be affected unless they were uniformly ovoid in shape in one direction, which the literature does not support (Calvin, 1998; Favorov and Kelly, 1994a,b; Mountcastle, 1997). Method of visualization Our method is a modification of an earlier version described elsewhere (Buxhoeveden et al., 2000). A region of interest (ROI) must be isolated and transferred to the computer imaging system. The ROI is obtained from a microscopic field of view at a chosen magnification. Magnifications of 100⫻ are chosen to resolve both individual perikarya and the complete laminar depth. Usually, the ROI consists of the entire field of view as seen in the photomicrograph. Sometimes, however, partial sections are used due to artifacts or other distortions. Digitization The first stage of the column detection routine divides a region of interest into overlapping horizontal strips. Cell concentration v is defined for each strip by 共x兲 ⬅ 冘e ⫺8共x⫺x i 兲 2 /d 2 , i sum being taken over all cells in that strip. The characteristic scale d is the width of a box that, placed at random in the field, would enclose one cell on average. The relative maxima and minima of v mark the locations of the centers of aggregation of cell columns and the space between them, respectively. Note that the full width of the Gaussian in the definition of v is such that cells within less than 1/2 of each other contribute to the same relative maximum of v and are not resolved into distinct columns. In order that the primary contribution to v would come from a region containing, on average, one cell, the height of the horizontal strips is approximately 2d. The method defines minicolumns as vertical clusters of large neurons delimited on either side by cell-sparse areas. Imaginary lines through the sparse areas partition a field into polygonal regions. We refer to such a polygon together with the totality of small and large neurons contained within it as a minicolumn segment, yielding several descriptive statistics (Fig. 3). This study reports on five measures: column width, neuropil space, grey level ratio, core grey level ratio, and linearity (Fig. 4). Column width (CW) describes the horizontal width of a single column, or equivalently, the distance between adjacent columns. The peripheral neuropil space (NS) is the width of the cell-poor space on both sides of the column. NS is found by subtracting the average width of the column core from CW. We define the column core as that part of the column that contains 90% of the cell bodies. So that the width of the main aggregate of cells is insensitive to “outlier” points, up to 10% of the cells in the column may lie in the region designated as peripheral neuropil space. Neuropil traditionally consists of neuronal processes with high synaptic content and glia (Haug, 1986). Because our method does not count cells, we accounted for cell density by measuring the amount of area occupied by Nissl-stained segments, which we call the grey level ratio (GLR). This measure is based on the GLI method developed elsewhere and adapted specifically for use within minicolumns (Schleicher et al., 1986; Zilles et al., 1982). Before column detection, the GLR is computed from the original image by thresholding. We use the total area of Nissl-stained objects, or pixels below the threshold, divided by the image area, to estimate the cellular density within the minicolumn. While this measurement does not provide us with cell numbers, it estimates the amount of space dedicated to cell soma within a column. This measure includes neurons and glia and does not discriminate between potential differences in ratios between them. An analysis of cell size within our samples found no significant difference between the human and nonhuman primate population, which means that density differences would be due to the population of MORPHOLOGICAL DIFFERENCES IN MINICOLUMNS 365 Fig. 3. A: Large pyramidal cells, used by the column detection routine to identify areas of high cell concentration, are highlighted in black. B: White lines depict the Euclidean minimum spanning trees of the cell column segments, used by the analysis routine to describe minicolumn structure. C: Faint gray lines surrounding minimum spanning trees depict the boundaries of the areas of high cell concentration, used by the analysis routine to compute column width and peripheral neuropil space. cells within the column and not the size of the cells. The core grey level ratio (CGLR) is simply the GLR of the core region of the columns (described above). Linearity (LIN) is the similarity of the minicolumn to a straight line. It is computed from the minimum spanning tree of the column (Fig. 3B), which is the set of lines linking the cells with the smallest total length of all such sets. The diameter of the minimum spanning tree is the longest path from one cell to another. In Figure 3B, one can see the diameters by tracing the paths from the uppermost cell in each column to the lowermost cell. The straight-line distance between these two cells, divided by the length of the diameter, is LIN. As the cells of the minicolumn approach a linear arrangement, LIN approaches 1; as they deviate from linearity, LIN approaches zero. Statistical methods Mixed-model analysis of variance (ANOVA) was used to evaluate differences in CW, NS, and GLI between hemispheres across species, taking into account correlation among repeated measurements made on the same experimental units, while allowing unequal numbers of observations on the different units. A rank transformation was used, due to nonnormality of the distributions of the sample data. The statistical test, which evaluates equality Fig. 4. Schematic representation of lamina IIIb cell columns. Shaded area represents the neuropil space (so-called by Seldon, 1981) in the periphery of cell columns. This area contains mostly synapses, dendrites, and unmyelinated axons. The unshaded section is the region in which the majority of the cell soma reside. Some of the processes found in this part of the column are represented here. 366 D.P. BUXHOEVEDEN ET AL. TABLE 1. Mean values for cell column parameters (mean ⫾ sd)1 CW (m) Human Chimpanzee Rhesus 1 51.0 ⫾ 5.5 36.6 ⫾ 1.7 36.4 ⫾ 3.4 GLR 16.9 ⫾ 2.8 26.0 ⫾ 4.0 26.0 ⫾ 4.7 CGLR 34.3 ⫾ 5.7 46.6 ⫾ 4.8 44.0 ⫾ 5.9 NS (m) 21.6 ⫾ 3.8 17.3 ⫾ 1.0 15.8 ⫾ 2.8 LIN 0.80 ⫾ 0.01 0.85 ⫾ 0.01 0.84 ⫾ 0.03 CW, column width; GLR, grey level index; CGLR, core gray level ratio; NS, neuropil space; LIN, linearity. Fig. 5. Graphic representations of data described in Table 1. of differences between hemispheres across the species in this model, is the F-test for species by hemisphere interaction. The analysis including all species was followed by separate mixed-model ANOVAs for each of the species. This approach allowed evaluation of species-specific differences between hemispheres, and thus investigation of the differences underlying significant interaction terms in the overall analyses. Mixed-model ANOVA was also used to evaluate differences among species, using measurements made on the left hemisphere only. The statistical test of interest in this analysis is the F-test for the main effect of species. Post hoc t-tests were used to evaluate pairwise differences between species in analyses showing significant overall tests. other nonhuman primate brains as reported in Table 1 and Figure 5. Columns were significantly wider in human PT (P ⫽ 0.0001) when compared across species. Differences between the individual species are given in Table 2 A,B,C. The mean width for human columns was 51 m, while that of the chimpanzee was 36.6 m and that of the rhesus monkey was 36.4 m. Translation into three dimensions would yield a volume twice as large as a cell column in the nonhuman primates. Even though the chimpanzee brain on average is about four times larger than the rhesus, their minicolumns are the same size, at least in this area of cortex. RESULTS Neuropil space The basic morphology of the minicolumn in humans is clearly distinguishable from that of the The cell-poor space in the periphery of cell columns was also considerably enlarged in humans Column width MORPHOLOGICAL DIFFERENCES IN MINICOLUMNS 1 TABLE 2. Individual comparisons between primates 2A: Human vs. chimp CW GLI NS CGLR LIN 2B: Human vs. rhesus CW GLI NS CGLR LIN 2C: Chimp vs. rhesus CW GLI NS CGLR LIN % difference P value 28% 54% 20% 26% 6% 0.001 0.001 0.001 0.002 0.001 29% 54% 27% 22% 5% 0.001 0.001 0.006 0.007 0.017 1% 0% 9% 0% 1% 0.084 0.910 0.160 0.298 0.862 1 CW, column width; GLR, grey level index; CGLR, core grey level ratio; NS, neuropil space; LIN, linearity. compared to the chimpanzee and rhesus monkey (P ⫽ 0.0008). No difference between the chimpanzee and monkey NS was confirmed statistically (P ⫽ 0.16). The increase in peripheral neuropil space in human columns appeared closely related to the larger width of the columns. A ratio of NS/CW revealed similar proportions for all three species. In this regard, human columns are merely larger versions of nonhuman primate ones. Nonetheless, the expansion of this area in human columns has provided more space for dendritic branching, spines, and other features generally found in this part of the column. Grey level ratio An estimate of cell density demonstrated that in human columns cell soma occupied much less space than in either the chimpanzee or rhesus monkey (P ⫽ 0.0001). The GLRs of the nonhuman primates were indistinguishable from each other as demonstrated in Table 2C (P ⫽ 0.910). This means as a whole, the human column contains fewer Nisslstained elements. While it is well known that larger brains are less cell dense, we found that this occurs within the context of the minicolumn (Buxhoeveden et al., 1996). Core grey level ratio To examine potential differences in the core of the column, we also measured the GLR (CGLR) for this region only (Table 1). As with the overall GLR, the human core was far less cell dense than that of the other primates. However, by comparison to the overall GLR, the human core was over 2⫻ denser than the GLR for the whole column, whereas it was 1.8⫻ more for the chimpanzee and 1.7⫻ more for the monkey (P ⫽ 0.0001). Apparently, the core of the cell column is more compact in humans. These findings suggest that cells are more concentrated in the core of the human column with fewer cell bodies in the 367 periphery (Fig. 2D). However, this does not mean the minicolumns are more linear in humans, in fact, the reverse is true (see below). The CGLR is a gross measure of cell concentration versus cell-poor area. Because human cell columns are much wider and contain greater neuropil space in the periphery, the concentration of cells in the core area is relatively higher. This means the core area has not expanded in proportion to the increase in width. Human Tpt “emphasizes” neuropil space by limiting the relative expansion of neurons in the periphery which results in the core GLR being more relatively dense than in the other primates. Linearity While the human minicolumn is more compact than in other primates, it is not as linear. This agrees with earlier studies using a different measure of linearity (Buxhoeveden et al., 1996). Nonhuman primate columns are more vertically linear and therefore closer to the ontogenetic unit. Chimpanzee versus rhesus monkey Comparisons between the chimpanzee and rhesus monkey revealed very little difference at the level we were examining (Table 2C). Testing the basic morphology, our method revealed very similar columns in the left hemisphere of the chimpanzee and rhesus monkey, except for the smaller NS in the monkey. As the only notable difference between the two, it moves “away” from the human configuration. In summary, the human cell columns are absolutely larger, the peripheral neuropil space is larger, and the columns contain more non-cell space than those of non-human primates. By contrast, in the parameters we examined, only neuropil space distinguished the chimpanzee from the rhesus monkey columns, and this was not statistically significant. DISCUSSION Overall size was the most obvious distinction between the morphology of columns in humans and the nonhuman primates. Humans had much wider cell columns than either the chimpanzee or rhesus monkey, while the latter revealed essentially identical column sizes. The significance of size, whether cortical volume or minicolumn, is very complex and full of potential pitfalls (Deacon, 1997). Nonetheless, the following discussion will briefly evaluate the interaction of cortical volume and minicolumn size. Since cortical thickness varies little between primate species, this discussion will focus upon the width and not depth of columns. First, column size has been generally noted as not correlating with cortical volume when comparing diverse species, e.g., columns in rabbits and mice are as large or larger than humans in some areas of the brain (Mountcastle, 1997). The size of cell columns seemingly has less to do with brain size, and everything to do with function and organization. For ex- 368 D.P. BUXHOEVEDEN ET AL. ample, cell column size in the primary visual cortex (Area 17) in monkeys and cats may be compared. Functionally, the visual systems of cats and monkey differ, as the monkey has better color and visual acuity (Orban, 1984). The rhesus monkey has a larger brain than the cat, but their minicolumns (in this region) are much smaller, about 31 m compared to 56 m in the two-dimensional plane (Peters and Sethares, 1991b; Peters and Yilmaz, 1993). When extrapolated to three dimensions, the columns in the cat visual cortex are actually three times larger than that of the monkey (Peters and Yilmaz, 1993). Moreover, input from the monkey thalamus differs from the cats’. Furthermore, its cell columns contain relatively more cells. Overall, the monkey’s cell columns are smaller and said to be more ‘complex’ because they have relatively more cells, providing more detailed specification of information than the cat; thus, the monkey’s orientation and eye preference columns are better defined. Nonetheless, the relationship of CW to brain size may be significant if the same area is measured between species that share a close evolutionary bond. The generalized statement that cell columns do not vary between species is based on a measure of means. This assessment is taken from a sample of very diverse organisms, often comparing totally different areas of the brain. We believe a stronger correlation may exist if similar cortical areas among closely related species are examined. Unfortunately, we were not able to pursue this issue at the present time since data were not available for brain size in the chimpanzees and some of the monkeys. As our data bank increases in regards to sample size and availability of brain volumes, this question can be resolved. Every minicolumn contains certain basic elements, including neurons, apical dendrites, myelinated efferents, double-bouquet bundles; thalamic, callosal, and cortico-cortical input; dendritic arborizations and spines, synaptic contacts, as well as glia. Brain volume may not correlate with column size because neuronal elements such as cells and axons require a minimum size to function. As a result, their dimensions stay essentially the same among species. Although cell columns range from a minimum of 20 m to a maximum of 80 m (Mountcastle, 1997), cortical volume varies far more. Unfortunately, very little work has correlated cell column size with specific cortical areas. Ideally, it is as important to compare column size to specific areas, as it is to compare columns to overall brain size. Larger cell columns potentially contain more neuronal elements at the cost of fitting fewer of them into a given cortical space. In other words, by “downsizing” individual columns, a brain can pack in more of them. Presumably, column size relates directly to the nature of their functions. Column size be evaluated in the context of cortical volume. If the volume of a particular region (like PT) is much larger in a given brain than the relative increase in column size, then the columns are actually smaller in relation to the space they occupy. A 50m column in a 20cc brain is very ‘large’ compared to the same size column in a 1400cc brain. The human brain is about 13x larger than that of the monkey and over 3x larger than the chimpanzee, while its cell columns are only about a 2x larger in human PT. From this perspective, columns in human PT are actually smaller than those of the chimpanzee and rhesus monkey, relative to brain volume. The chimpanzee brain is about 4x larger than that of the rhesus monkey, yet their columns were the same size. Thus, the chimpanzee columns appear relatively smaller than those in the rhesus monkey. Increased brain size allows for more flexibility regarding the size of particular cortical regions. These regions may increase absolutely and relatively, or they may decrease relative to the large brain while remaining considerably larger than those found in a small brain. The functional operation of the mammalian brain is thought be distributed across many areas of the brain rather than being strictly modular (Mountcastle, 1997). This means that individual columns may serve multiple purposes depending on how they are recruited. In this regard, small brains may reveal yet another limitation: delegating more columns to a particular function(s) reduces the number available for something else. Thus because a small brain contains fewer processing units, with fewer interconnections, its total capabilities must be more restricted than those of a larger brain. Thus, a smaller brain will run out of options before a larger one does. It would appear that minicolumns and cortical volume are co-dependents to some degree. The composition of an individual minicolumn depends on many local factors. However, the functional capacity of minicolumns in a given region is partly influenced by the degree of potential complexity in the brain, i.e., its interconnected pathways and regions. Cortical structures in the form of minicolumns and cortical volume exert a reciprocal effect on each other. A bidirectional four-level hierarchy best describes the relationships between the size of minicolumns and brain volume: 1) The complexity of the individual column. The larger a cell column, the greater the number and variety of its functional elements. Increases in column size may specifically accommodate a large increase in incoming myelinated pathways, or handle a generalized increase in a variety of structures and functions. 2) The number of columns in a given region. A large brain can “afford” to pack in not only more columns but also more complex ones if particular areas expand. 3) The number and size of cortical regions connecting columns directly. A large brain may contain more, as well as larger, cortical regions. As the size of cortical regions increases, so does the number of columns they may MORPHOLOGICAL DIFFERENCES IN MINICOLUMNS contain. These changes increase the amount of information regions can share. 4) Secondary connections. Areas lacking direct connections may exert influence indirectly. For example, Tpt, a cortical region within the planum temporale, may not connect directly to area 9. However, it does connect to area 46, which in turn connects to area 9 (Romanski et al., 1999). Even if a column in one area lacks a direct connection to another, it may influence it indirectly, magnifying the effects of direct connections. Therefore, the greater number and intensity of direct connections within a brain, the greater may also be the indirect influences. In summary, our data suggest several conclusions. First, because human cell columns contain considerably more peripheral neuropil space, they presumably utilize their increased size for more input/output pathways and more microcircuitry. Secondly, relative to brain volume, human columns are actually smaller than those of the primates tested so that more of them can be packed into an area like PT. 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