Tuberculosis remains a major world-wide health threat which demands the discovery and characterisation of new drug targets in order to develop future antimycobacterials. The regeneration of methionine consumed during polyamine biosynthesis is an important pathway present in many microorganisms. The final step of this pathway, the conversion of ketomethiobutyrate to methionine, can be performed by aspartate, tyrosine, or branched-chain amino acid aminotransferases depending on the particular species examined.Mycobacterium tuberculosis H37Rv has been cloned, expressed, and characterised. The enzyme was found to be a member of the aminotransferase IIIa subfamily, and closely related to the corresponding aminotransferase in Bacillus subtilis, but not to that found in B. anthracis or B. cereus. The amino donor preference for the formation of methionine from ketomethiobutyrate was for isoleucine, leucine, valine, glutamate, and phenylalanine. The enzyme catalysed branched-chain amino acid and ketomethiobutyrate transamination with a Km of 1.77 – 7.44 mM and a Vmax of 2.17 – 5.70 μmol/min/mg protein, and transamination of ketoglutarate with a Km of 5.79 – 6.95 mM and a Vmax of 11.82 – 14.35 μmol/min/mg protein. Aminooxy compounds were examined as potential enzyme inhibitors, with O-benzylhydroxylamine, O-t-butylhydroxylamine, carboxymethoxylamine, and O-allylhydroxylamine yielding mixed-type inhibition with Ki values of 8.20 – 21.61 μM. These same compounds were examined as antimycobacterial agents against M. tuberculosis and a lower biohazard M. marinum model system, and were found to completely prevent cell growth. O-Allylhydroxylamine was the most effective growth inhibitor with an MIC of 78 μM against M. marinum and one of 156 μM against M. tuberculosis.The gene encoding for branched-chain amino acid aminotransferase in M. tuberculosis. This enzyme can be inhibited by selected aminooxy compounds, which also have effectiveness in preventing cell growth in culture. These compounds represent a starting point for the synthesis of branched-chain aminotransferase inhibitors with higher activity and lower toxicity.Methionine formation from ketomethiobutyrate is catalysed by a branched-chain amino acid aminotransferase in  Tuberculosis remains one of the leading causes of worldwide mortality and morbidity, infecting an estimated 8 million people annually with approximately 2 million deaths . The sitKlebsiella pneumoniae [Bacillus subtilis [Mycobacterium spp., only methionine adenosyltransferase has been cloned, expressed, and fully characterised [Polyamine synthesis and its associated methionine (Met) regeneration pathway Figure  are knoweumoniae -11 and tsubtilis -14 Selecsubtilis ,15-20. Fcterised .K. pneumoniae, B. subtilis, and B. anthracis [Plasmodium falciparum, Trypanosoma brucei brucei, Giardia intestinalis, and Crithidia fasciculata, this reaction is catalysed by the subfamily Ia enzyme aspartate aminotransferase [K. pneumoniae, however, the reaction was performed by the close homologue tyrosine aminotransferase, which is also a member of subfamily Ia [B. subtilis, B. cereus, and B. anthracis were recently found to catalyse Met regeneration via a branched-chain amino acid aminotransferase (BCAT) [B. subtilis and B. cereus/B. anthracis utilised BCAT enzymes from separate subfamilies (IIIa vs. IIIb respectively). As Mycobacterium spp. also appear to have no subfamily Ia aminotransferase sequences  by an aminotransferase. The specific aminotransferase responsible for the reaction has been identified and characterised in a number of microorganisms, including malaria, African trypanosomes, nthracis ,16,17. Ie (BCAT) . IntriguM. tuberculosis H37Rv was found to contain a single gene with a very high sequence homology to either B. subtilis YbgE or YwaA, which are both known to be subfamily IIIa BCATs [B. subtilis YheM, B. cereus BCAT, or B. anthracis BCAT, which are all subfamily IIIb aminotransferases [M. tuberculosis BCAT gene, Rv2210c, has not been previously cloned, expressed, or characterised. It is interesting to note that the M. tuberculosis genome contains a single BCAT homologue and no obvious DAAT homologue.The complete, published genome of Ia BCATs ,23. In csferases . This reMycobacterium spp. uncovered a single gene in M. leprae, M. bovis, M. marinum, M. ulcerans, M. avium, and M. smegmatis with an extremely high identity to Rv2110c. Together, with other subfamily IIIa aminotransferases, the putative mycobacterial sequences were aligned and a cladogram constructed  is the PLP binding site and would be expected to be invariant. If one excludes the only DAAT in Figure E. coli BCAT. Clearly, sequence conservation is very low across family III.The low level of sequence conservation outside of the genus can be seen in the alignment of selected BCAT sequences shown in Figure oli BCAT , only K2M. tuberculosis BCAT was cloned as a deca-histidine fusion protein for expression in E. coli. To prevent complete inclusion of the recombinant protein, it was necessary to induce expression with a relatively low concentration of IPTG (0.1 mM) at 20°C for 20 hr. Under these conditions, sufficient soluble material was produced and purified over Ni2+ affinity columns  and the human mitochondrial BCAT [M. tuberculosis BCAT. However, while the human mitochondrial BCAT is also a family IIIa aminotransferase, there are some clear differences when compared to the M. tuberculosis enzyme. The human enzyme will not accept aromatic amino acids, whereas the tuberculosis BCAT would use phenylalanine as an amino donor. In addition, the human enzyme contains the redox-active motif CXXC at positions 311–314  with a high similarity to the B. subtilis ykrV gene product. YkrV was found to be a subfamily If aminotransferase and could also catalyse the conversion of KMTB to Met using glutamine as the only effective amino donor [B. subtilis YkrV could transaminate KMTB with glutamine, B. subtilis cell homogenates did not produce Met from KMTB when supplemented with glutamine [M. smegmatis grown in Middlebrook 7H9 incomplete medium were only able to produce Met from KMTB when supplemented with valine, isoleucine, leucine, glutamate, or phenylalanine, as was seen for the recombinant M. tuberculosis BCAT in figure Several interesting findings arose during the course of this investigation. First, while no donor . Therefolutamine . Similarence Rv088c with aM. tuberculosis was found to contain no putative gene product with significant homology to a DAAT. In fact, the organism appeared to contain no subfamily IIIb aminotransferases. The physiological significance of a lack of a DAAT is unclear, but many organisms do not contain a homologue of this enzyme. With DAAT, there might be a diminished capacity to catabolise D-amino acids for energy, although the same reactions could be performed by a D-amino acid oxidase. M. tuberculosis is known to be reliant on carbohydrate catabolism during the active growth phase and lipid metabolism during the chronic, dormant phase [nt phase . TherefoM. tuberculosis was found to lack clearly identifiable homologues of several enzymes in the Met regeneration pathway. The most glaring omission is the lack of an S-adenosylmethionine decarboxylase (SAMdc) homologue  were cultured in liquid Middlebrook 7H9 complete medium or on Middlebrook 7H10 plates at 37°C for M. tuberculosis or 30°C for M. marinum. All substrates and inhibitors were obtained from Sigma-Aldrich (Oakville, ON, Canada).M. tuberculosis by vortexing packed cells in a minimal volume of 50 mM Tris-HCl pH 8.0/10 mM EDTA/100 mM NaCl containing 500 μm acid washed glass beads (Sigma). After allowing the glass beads to settle, the supernatant was added to an equal volume of 10 mM Tris-HCl pH 8.0/100 mM NaCl/25 mM EDTA/0.5% w/v sodium dodecyl sulfate/0.1 mg/ml proteinase K and incubated for 1 hr at 37°C with occasional gentle mixing. The mixture was then subjected to extraction with phenol:chloroform:isoamyl alcohol (25:24:1), and the DNA ethanol precipitated.Genomic DNA was isolated from M. tuberculosis BCAT gene was discovered by a BLAST search of the complete M. tuberculosis H37Rv genome using the B. subtilis YbgE, YwaA, or YheM gene products as the query proteins [Escherichia coli XL10 cells  and was subsequently recovered using the Qiaspin miniprep kit (Qiagen). Positive clones were determined by digesting the plasmid with NotI and XhoI to confirm the presence of the insert on a 1% agarose gel. The sequence of the insert was confirmed by using the Big-Dye cycle sequencing kit  and an ABI Prism 310 genetic analyser.The sequence of the putative proteins ,23,43. TE. coli BL21(DE3) CodonPlus RIL cells (Stratagene) for functional expression. Cells were grown in liquid LB medium containing 50 μg/ml ampicillin and 50 μg/ml chloramphenicol at 37°C and 250 rpm until the culture reached an A600 nm of 0.6–0.8. The culture was then cooled to 20°C for 30 min at 250 rpm before the addition of 0.1 mM isopropylthiogalactopyranoside (IPTG) and an additional 20 hr of incubation at 20°C and 250 rpm.The plasmid from positive clones was transformed into 4. The column was washed with 50 mM HEPES (pH 7.4)/750 mM NaCl and 50 mM HEPES (pH 7.4)/750 mM NaCl/80 mM imidazole, before elution with 50 mM HEPES (pH 7.4)/750 mM NaCl/800 mM imidazole. Fractions containing the recombinant protein were pooled and concentrated to less than 3.0 ml using a 30 kDa molecular mass cut-off filter . The concentrated enzyme was then dialysed against 50 mM HEPES (pH 7.4)/1 mM dithiothreitol/1 mM EDTA/trace pyridoxal-5-phosphate (PLP) overnight at 4°C. The concentrated enzymes were stored at 4°C for several days, or with 20% v/v glycerol at -20°C for several weeks, without appreciable loss of activity. Recombinant protein samples were examined by electrophoresis on 10% SDS polyacrylamide gels followed by Coomassie Brilliant Blue R250 staining. Protein concentration was measured using the Bio-Rad reagent .The culture was centrifuged at 3500 × g for 20 min at 4°C, and the cell pellet resuspended in 50 mM HEPES (pH 7.4)/750 mM NaCl and frozen at -20°C. The resuspended cells were then thawed, sonicated on ice, centrifuged at 3000 × g for 20 min at 4°C, and the supernatant loaded onto a 1.6 × 9.5 Chelating-Sepharose-FF column  charged with NiSO4 (pH 7.4)/50 μM PLP/various concentrations of amino acid/various concentrations of keto acid) and incubated for 30 min at 37°C. The samples were then stored at -20°C until analysis by HPLC. All samples were analysed by pre-column derivatisation and reverse-phase HPLC. 10 μl of sample was mixed with 50 μl of 400 mM borate pH 10.5 and then with 10 μl of 10 mg/ml o-phthalaldehyde/12 μl/ml mercaptopropionate/400 mM borate pH 10.5 prior to the injection of 7.0 μl onto a 2.1 × 200 mm ODS-AA column . The column was eluted using 2.72 mg/ml sodium acetate pH 7.2/0.018% v/v triethylamine/0.3% v/v tetrahydrofuran as Buffer A and 2.72 mg/ml sodium acetate pH 7.2/40% v/v methanol/40% v/v acetonitrile as Buffer B with a linear gradient of 0 – 17% B over 16 min followed by a linear gradient of 17–100% B over 1 min and 6.0 min at 100% B. The flow rate was 0.45 ml/min from 0 – 16 min and 0.80 ml/min from 17–30 min. The elution of derivatised amino acids was monitored at 338 nm and fluorometrically with an excitation of 338 nm and an emission of 450 nm. All separations were performed on an Agilent 1100 HPLC equipped with an autosampler, variable wavelength ultraviolet/visible spectrophotometric detector, fluorescence detector, and Chemstation operating system.Aminotransferase activities were assayed by an HPLC method . 5 or 10The amino donor range for Met regeneration was determined by incubating 2 mM of each individual amino acid and 1 mM KMTB, followed by HPLC for Met quantification. Amino acids which were effective amino donors were further studied at 0.1 – 10 mM amino acid and 10 mM KMTB to determine the kinetic constants. Similar assays were performed with 0.1 – 10 mM KMTB and 10 mM Leu. Replacement of KMTB with ketoglutarate (KG) in these experiments and subsequent HPLC analysis of Glu formation allowed for the determination of BCAT activity. The apparent Km and Vmax values for each substrate were assessed by non-linear curve fitting using the Scientist software programmed with the Michaelis-Menton equation .M. tuberculosis BCAT using 2.0 mM Leu/1.0 mM KMTB/0.1 or 1.0 mM inhibitor in the enzyme incubation. Inhibitors which demonstrated better than 50% reduction of activity at the 0.1 mM concentration were further studied for the determination of Ki values. These reactions involved 0.5, 1.0, 2.0, or 3.0 mM Leu and 1.0 mM KMTB in the reaction mixture together with 0, 25, 50, 75, 100, 150, 200 μM of inhibitor. The Ki values were determined by non-linear curve fitting with the Scientist software programmed with competitive, uncompetitive, and mixed inhibition equations [Initial inhibition studies screened 13 aminooxy compounds against M. tuberculosis and M. marinum using the most effective enzyme inhibitors. Cultures at mid-log growth in Middlebrook 7H9 complete medium was diluted to 2 × 105 cfu/ml and 100 μl added to 96 well microtitre plates containing 100 μl of doubling dilutions of each inhibitor. The final inhibitor concentration ranged from 10 mM – 298 pM. Positive and negative controls consisted of 100 μl Middlebrook 7H9 medium replacing the inhibitor or cells respectively. The plates were incubated at 30°C for 8 days (M. marinum) or 37°C for 14 days (M. tuberculosis) with no agitation before measurement of growth at A650 nm using a Molecular Devices 96-well spectrophotometer . The MIC was determined as the lowest dilution that completely prevented microbial growth and the IC50 was determined by non-linear curve fitting with the Scientist software programmed with the Chou equation [In vitro growth inhibition studies were performed on equation .[Mycobacterium spp. BCAT sequences from preliminary genome projects were made available from The Institute for Genomic Research  for M. smegmatis and M. avium, from The Sanger Centre  for M. marinum, and from The Institut Pasteur  for M. ulcerans. These sequences were aligned using the Clustal algorithm and the BLOSUM sequence substitution table in the ClustalX program [Additional BCAT and DAAT sequences were obtained from GenBank . Mycobac program . Aligned program  and were program  to const program . All tre program .M. marinum experiments. BJB conceived the study, performed the M. tuberculosis experiments, and wrote the manuscript.ESV performed the cloning, expression, and characterisation of the enzyme, and assisted in writing the manuscript. CLR assisted in the cloning and expression experiments. MHK performed the


As the presumed “seat of consciousness,” the cerebral cortex mediates the higher-level cognitive processing—such as abstract thought—that humans like to think distinguishes them from other animals. The cerebral cortex is, in fact, significantly larger in the human brain and has far more “columns” than it does in other mammals, particularly compared to the rat, a traditional model for brain study. Neurons in these cortical columns have similar response properties and form fundamental units of brain processing. It is the larger brain surface area, accommodating a greater number of cortical columns, that gives humans the computational edge.By studying the genetic and molecular agents of cortex development, scientists hope to understand the nature and extent of cortical cognitive function and identify effective therapies to repair brain injury and disease. Analyses of mutant mice have provided insights into the mechanisms controlling cerebral cortex development, but many details of cortical development remain to be revealed. Identifying individual molecules and genes involved in discrete brain processes is particularly difficult given the complexity of brain structure and function. Geneticists have traditionally linked genes to specific biological pathways by first screening large numbers of individuals of a species for unusual physical traits (phenotypes) and then determining the genetic makeup of these mutants to home in on the faulty gene. This approach, called forward genetics, typically requires large numbers of individuals to find unusual phenotypes and so has traditionally focused on fast-breeding organisms like zebrafish and fruitflies. But zebrafish and fruitflies are unlikely to reveal the secrets of higher consciousness.Now Andrew Peterson and colleagues have updated the forward genetic screen and added a new resource to the neuroscientist's “brain dissection” toolkit. Their approach, which labels specific populations of neurons with protein “reporters,” offers a novel way to find mutations in developing neurons and to identify mutations that interfere with cerebral cortex development. The reporter used here highlights mutations that disrupt interneuron migration into the cortex as well as those that affect cortex growth and morphology. Like most forward genetic approaches, the researchers started with a genetically well-characterized breed, then used a chemical mutagen to damage the organism's DNA. After two or three rounds of breeding, the researchers looked for cortical-related defects in the developing embryos. In this case, however, the region of the mouse brain that gives rise to developing interneurons was labeled in the original mice. Thus, when the researchers screened for mutants with defects associated with forebrain development and interneuron migration, they could easily find the cells and genes involved.The screen identified thirteen mutations affecting cortical development and interneuron migration.  Three mutations are variants of genes known to play a role in cortical development; nine mutations were in genes that had not been linked to cortical development before.The screen described here takes advantage of a chemical mutagen  that induces mutations at a single base pair, or nucleotide, in DNA. These mutations tend to have quite selective effects on protein function—by changing the composition of a single domain—which can provide information on how the protein should normally function and highlight its role in a particular process. The selective nature of ENU, the authors argue, offers new information about how the mutated genes identified here function in cortical development and how these putative roles might be tested.Altogether, the results suggest that this type of focused screening, so long a resource in fly genetics, can be a powerful tool in mammalian biology as well. That the strategy outlined here could identify novel mutations in a process as complicated as cerebral cortex development suggests that it could do the same for a broad range of biological processes. If the success of other model systems moving in this direction is any indication, this new strategy in the mouse offers researchers a powerful resource for identifying the genetic underpinnings of living systems.


Behavior results from the integration of ongoing sensory signals and contextual information in various forms, such as past experience, expectations, current goals, etc. Thus, the response to a specific stimulus, say the ringing of a doorbell, varies depending on whether you are at home or in someone else's house. What is the neural basis of this flexibility? What mechanism is capable of selecting, in a context-dependent way an adequate response to a given stimulus? One possibility is based on a nonlinear neural representation in which context information regulates the gain of stimulus-evoked responses. Here I explore the properties of this mechanism.By means of three hypothetical visuomotor tasks, I study a class of neural network models in which any one of several possible stimulus-response maps or rules can be selected according to context. The underlying mechanism based on gain modulation has three key features: (1) modulating the sensory responses is equivalent to switching on or off different subpopulations of neurons, (2) context does not need to be represented continuously, although this is advantageous for generalization, and (3) context-dependent selection is independent of the discriminability of the stimuli. In all cases, the contextual cues can quickly turn on or off a sensory-motor map, effectively changing the functional connectivity between inputs and outputs in the networks.The modulation of sensory-triggered activity by proprioceptive signals such as eye or head position is regarded as a general mechanism for performing coordinate transformations in vision. The present results generalize this mechanism to situations where the modulatory quantity and the input-output relationships that it selects are arbitrary. The model predicts that sensory responses that are nonlinearly modulated by arbitrary context signals should be found in behavioral situations that involve choosing or switching between multiple sensory-motor maps. Because any relevant circumstancial information can be part of the context, this mechanism may partly explain the complex and rich behavioral repertoire of higher organisms. The concept of a direct, one-to-one association between a sensory stimulus and a motor response has been strongly influential in neuroscience . Such asGain control is a common mechanism by which neurons integrate information from multiple modalities or sources ,9. Gain-Interestingly, all of these examples deal with the same problem – spatial localization – but the computations that can be effectively carried out through gain-modulated responses are much more general ,16,26. IUsing theoretical and computer-simulation methods, I show that this type of functional switching can be achieved through contextual modulation regardless of how the context is encoded – whether continuously or discontinuously – and independently of the discriminability of the stimuli. The results are presented using neural network models of hypothetical behavioral tasks similar to those used in experiments with awake monkeys. A report with a different example was published previously .All model networks discussed below have the same general, two-layer architecture -16. A fix along the horizontal and the subject responds by making an eye movement  may serve to indicate which condition applies in each trial [In the antisaccade task, a stimulus appears briefly at position ent Fig. . There ach trial .x, with one curve for each of the conditions (red and green traces). These tuning curves are bell-shaped because Gaussian functions were used to define them (see Methods). Because context affects the gain of the responses, for any given cell, the two curves differ only in their amplitudes. The context that produces the highest gain is the preferred one. The maximum and minimum gains for each neuron are model parameters that can be between 0 and 1. The four GM units in Fig. Examples of model GM responses chosen for this task are shown in Fig. x for saccades (context 1) and to -x for antisaccades (context 2). It is easy to see why such a network can implement two entirely independent sensory-motor maps: each population has its own set of synaptic connections driving the downstream motor neurons, and the maps are kept separate because the two populations are never active at the same time.First consider what happens if the first layer of a model network is composed of two populations of such switching neurons. One population is active in context 1 and the other in context 2. This is illustrated in Fig. Figure The model correctly produces saccades in context 1 and antisaccades in context 2. Furthermore, this scheme for switching sensory-motor maps as a function of context would also work for any two maps driven by the two populations. This model switches maps successfully because the GM neurons are themselves switched on and off by context, so this case is trivial. However, the main result in this section is that a network of partially modulated GM neurons has exactly the same functionality. The more rigorous statement is this: for a discrete number of contexts and everything else being equal, a network of partially modulated neurons can generate the same mean downstream responses as a network of switching neurons. Figure The key point is that, under relatively mild conditions, partial and full modulation lead to the exact same repertoire of switchable sensory-motor maps (the difference lies in their accuracy, as discussed below). The formal proof is presented in Appendix A. This result is interesting because it provides an intuitive interpretation of gain modulated activity: modulations that may seem small at the single-unit level may produce drastically different output responses due to their collective effects, the result being as if different sensory populations had been turned on and off.The equivalence between networks of neurons that switch across contexts and networks with partial modulation is subject to an important condition and a qualification.x = 5, the condition is fulfilled for that pair if one neuron prefers context 1 and the other context 2. As long as this independence constraint is satisfied, there is great flexibility in the actual amount of modulation; it does not need to be 100%, as with a full switch.The key condition is that the modulation factors that determine the gain of all the neurons with similar stimulus selectivities must be linearly independent across contexts (Appendix A). In practice, one way to achieve this is to include all relevant combinations of sensory and contextual preferences. For instance, if there are two neurons that respond maximally when The qualification, however, is also critical, because a network of partially modulated GM neurons is not exactly the same as one composed of switching neurons: in most functionally relevant cases, partially modulated neurons are effectively noisier. In general, variability plays an important role in the performance of these networks. No fluctuations were included in the simulations of Fig. σR, quantifies the variability of single neurons. Figure σR as a function of γ, which is the minimum gain of the GM neurons (the maximum is 1). When γ = 0, the GM neurons are fully suppressed in their non-preferred context; when γ = 1, the GM responses are identical in both contexts. The three curves are for three levels of noise. Their order shows that, as expected, higher noise in the input layer always produces higher variability in the output. For each data point, the synaptic weights were set so that the average firing rates of the output neurons, as functions of stimulus location and context, were always the same (Appendix B). Thus, for all γ values, the average profile of motor responses for x = -15 and x = 10 looked exactly like those in Fig. γ, as predicted theoretically (Appendix C).Two measures of noise sensitivity are plotted in Fig. σCM, which estimates the error between the desired movement location and the center of mass of the output population, which is considered the encoded movement location (Equations 19, 20). Thus, σCM quantifies the variability of the network. Figure σCM also increases with γ, reaching a saturation level. This error saturates because, in contrast to the individual neuron responses, the encoded movement location is restricted to a limited range of values, so its variance cannot grow above a certain limit.The second measure of noise sensitivity is γ, the optimal algorithm considers both the mean and the variance of the output responses [x = -15 and x = 10 there are small differences compared to the curves in Fig. σCM  results in higher output variability.Note that, as explained in Appendix C, γ is above 0.6 or so, at which point σCM is about twice that observed with full modulation. These results were obtained using the same γ for all GM neurons, but almost identical curves were produced when γ varied randomly across cells and the results were plotted against its average value.In conclusion, as the modulation becomes weaker, the performance of the network typically becomes less accurate, even though the average output responses may be close or identical to those obtained with maximum modulation. In Fig. The possible contexts encountered by an organism could be numerous and diverse, so it is not clear how the brain might encode them. There are at least two distinct ways: as separate, discrete states, or as points along a smooth, continuous space. What would be the difference in terms of the functionality of the remapping networks studied here? This is investigated next, using a generalization of the antisaccade task referred to as the scaling task.x, and a scale factor, y; the movement should be toward the point xy. When y = 1, the movement is simply a saccade toward x; when y = -1, the movement is an antisaccade toward -x; when y = 0.5, the movement should be to a point halfway between fixation and the location of the stimulus, and so on. To begin with, five possible conditions are considered, corresponding to scales of -1, -0.5, 0, 0.5 and 1.The scaling task is very much like the antisaccade task, except with more contexts. The subject's response should be an eye movement toward a location determined by the position of the stimulus, y differs from each cell's preferred scale. All units in the figure have approximately the same minimum gain, γ ≈ 0.5.Figure σCM ≈ 0.6.Differences between these two coding strategies can be observed in Fig. σCM as a function of the number of GM neurons for three levels of noise. In all cases, the error decreases approximately as  – a sign that noise is what limits the accuracy of the system. This is consistent with the virtually perfect performance obtained with zero noise. With the five selected contexts, results are almost identical for the continuous and discontinous cases.Figures σCM as a function of the number of GM neurons in networks in which the connections were corrupted. For these plots, the connections were first set to their optimal values, as in the standard simulations, but then 25% of them, chosen randomly, were set to zero. To generate the same range of output firing rates, all remaining connections were divided by 0.75, but no further adjustments were made. Performance was then tested. Compared to the results with unaltered weights  and 5 scales. Accuracy is practically the same as in the original simulations . Results are shown in Fig. In summary, this task requires somewhat more complex GM neurons than the antisaccade task, because there are more contexts. In the discontinuous case, the basic intuition for why the model works is the same as in the previous task: with the proviso that they are effectively noisier, partially modulated neurons are equivalent to switching neurons, which can trivially establish independent sensory-motor maps. However, the key advantage of a continuous neural representation of context over a discontinuous one is that it allows a network to perform accurately on combinations of stimulus and context that have not been explicitly encountered before. By its very definition, such continuous encoding requires partial modulation. Therefore, although partial modulation is typically detrimental for switching between discrete contexts (relative to full switching), it is highly advantageous when context is parameterized by a continuous variable, because it serves to generalize.In the scaling task, all stimuli and contexts are unambiguous, but in many real-life situations and experimental paradigms, motor actions are preceded by perceptual processes that involve the interpretation or analysis of sensory information. That is, specific actions (e.g., pressing a left or right button) are often based on ambiguous information (e.g., whether on average a group of flickering dots moved to the left or to the right). In theory, switching between maps should be independent of the perceptual component of a task (Appendix A).x. The task is most difficult when x is near 0° and the bar is nearly vertical, but it becomes easier as x approaches ± 45°. This is also a remapping task because the association between bar orientation and correct target is not unique: the color of the fixation spot determines whether left and right targets correspond to bars tilted to the left (x < 0) and to the right (x > 0), respectively, or viceversa. There is also a no-go condition, which gives a total of three.To investigate this, consider the orientation discrimination task illustrated in Fig. y (see Methods). The order in which each GM cell prefers the three contexts is set randomly, so context is encoded discontinuously.The GM cells in this case are tuned to stimulus orientation. The response curves are not shown, but have a single peak, as in Figs. The responses of the model output units are shown in Figs. To quantify the performance of the network in this case, the generated movement was set equal to the location of the tallest hill of activity. This always corresponded to one or the other target location, +10 or -10, so each trial could be scored as either correct or incorrect. The assumption here is that a profile of activity with two peaks, as in Figs. x. These are essentially neurometric curves – psychometric curves computed from neuronal responses – and indeed have the sigmoidal shape that is characteristic of many psychophysical measurements. Figure x > 0 correspond to movements to the right; Fig. x > 0 correspond to movements to the left. The gray curves are best fits to the simulation data points. The fits have two parameters, the center point, or bias (indicated by dashed lines), and a second parameter that determines the steepness at the center point and is inversely proportional to the discrimination threshold (see Methods). Without noise, performance is virtually perfect (not shown), in which case the bias and threshold are zero and the neurometric curve becomes a step function. However, both quantities increase in magnitude as noise is increased, producing the observed sigmoidal curves.Based on this criterion, the performance of the network is shown in Figs. The presence of a bias might be surprising, given the symmetry of the network. However, the bias depends on the number of trials used to estimate the probabilities. If each orientation were tested an infinite number of times, the data points in Figs. Figures In summary, although the ambiguity of the sensory information is reflected in the motor responses, it does not interfere with the context-dependent selection mechanism.The above results demonstrate that contextual modulation could serve to select one of many associations or maps between sensory stimuli and motor responses. Indeed, a key insight is that modulating the gain of a neural population is, in a sense, equivalent to flipping a switch that turns on or off specific subpopulations of neurons. This explains why networks of GM neurons can generate large changes in downstream responses – even all-or-none changes, as in go vs no-go conditions Figs.  – althoux form a basis set when any arbitrary function of x can be computed as a linear superposition of those functions in the group; sines and cosines of are a well known example. The function of x typically associated with a neuron's response is its tuning curve – its firing rate measured as a function of x. Pouget and Sejnowski showed that, starting with two networks that form separate basis sets for x and y, a network of GM neurons comprising all possible combinations (i.e., pairwise products) of those two sets would form a basis set for functions that depend simultaneously on x and y. This means that any function of x and y can be computed from the resulting GM responses. This was a crucial result, because it provided a rationale for generating such a combined representation. However, it assumed that both the sensory and modulatory variables are continuous and that, taken independently, the sets of x- and y-dependent tuning curves both form true basis sets.In a landmark paper, Pouget and Sejnowski  studied y varies discretely, each of its values corresponds to computing a different function of the stimulus x. Furthermore, the x- dependent tuning curves determine what functions of x or sensory-motor maps can be computed downstream, but there is no requirement for them to form a strict basis set. As mentioned, the discontinuous case fits better with the idea of switching between various possible maps, as if separate populations of neurons were turned on and off. This approach also highlights two important characteristics of these networks, that the modulation factors need to be nonlinear functions of context (Appendix A), and that the sensitivity to noise depends on the magnitude of the modulation (Appendix C).The present results relax some of these assumptions and provide a complementary point of view. When the modulatory quantity x and y are the inputs and the high-dimensional space is composed of the GM responses. Interestingly, such expansion into an appropriate set of basis functions [An important property of networks of GM neurons is that the output units read out the correct maps using a simple procedure, a weighted sum -15. As aunctions ,37 is thunctions . It alsounctions . This stA crucial requirement for the above scheme of projecting the data onto a suitable set of basis responses is to cover all relevant combinations of sensory stimuli and modulatory signals in the GM array -15. It iAnother possibility is to have a relatively small number of available gain modulation patterns controlled by an additional preprocessing mechanism that would link them to the current relevant cue (the color of the fixation spot, its shape, the background illumination, etc.), a sort of intermediate switchboard between possible contexts and possible gain changes. Attention has some features that fit this description – it can select or favor one stimulus over another, it can act across modalities, and it can produce changes in gain -23,39. Nf(x) and g(y), nonlinearly [f and g were added (Equation 15) instead of multiplied, all transformations failed completely, as expected [f and g is not crucial for these models.A key ingredient of the general, two-layer model is that the GM neurons must combine sensory and modulatory dependencies, linearly -15. Resuexpected . Second,However, in some cases a multiplication allows the synaptic connections to be learned through simple Hebbian mechanisms ,15, so iIn the model for the orientation discrimination task, the level of activity of the output neurons reflects not only the evoked movement but also the difficulty of the sensory process. This is consistent with the observation that, during sensory discrimination tasks, neuronal responses in many motor areas carry information about the stimulus -42. ThisIn the present framework, if sensory responses were modulated by multiple environmental cues, the responses of downstream neurons could be made conditional on highly specific contextual situations (see ref. 16). Therefore, this mechanism may also explain the capability of some neurons to drastically change their response properties in a context-dependent way. Two prominent examples are hippocampal place cells, whose place fields can be fully reconfigured depending on multiple cues ,44, and X and Y then Z'. The switching property of GM networks is useful in these situations as well. The study of abstract rule representation by Wallis and Miller [In many tasks, two or more inputs are combined into conditional statements – 'if d Miller  is a good Miller  are geneOther experimental studies also include results that are consistent with gain interactions between multiple sensory cues ,27,28,30Ideally, the nonlinear mixture will show up through multiplicative changes in gain, as in Figs. The framework discussed here demonstrates how to make a neural network adaptable to various environmental contingencies, labeled here simply as context. To achieve this flexibility, context must influence the ongoing sensory activity in a nonlinear way. This strategy was illustrated with tasks akin to those used in neurophysiological experiments with awake monkeys, but is generally applicable to the problem of executing a sensory-evoked action only when a specific set of conditions are satisfied. The mechanism works because changing the gain of multiple neurons is, in a sense, equivalent to flipping a switch that turns on and off different groups of neurons. Its main disadvantage is that all relevant combinations of stimulus and context must be covered, which may require a large number of units. On the upside, however, the switching functionality is insensitive to the quality or content of the sensory signals, is robust to changes in connectivity, and places minimal restrictions on how context is encoded. Future experiments should better characterize how cortical neurons integrate sensory and contextual information.x, which may represent stimulus location or stimulus orientation, and on a context signal y. For each model GM cell, these quantities are combined through a product of two factors, f and g. The former determines the sensory tuning curve of the neuron and the latter its gain or amplitude as a function of context y. The mean firing rate rj of GM unit j is thus written asThe GM responses depend on a sensory feature rj = rmax fj(x) gj(y) + B,     (1)fj and gj vary between 0 and 1, B is a baseline firing rate equal to 4 spikes/s and rmax = 35 spikes/s. The specific functions used for fj and gj depend on the task and are described below. However, note that because these two terms are combined through a multiplication, changes in context alter the overall responsiveness of a cell, but not its selectivity, which is the defining feature of gain modulation [j is equal to αrj, where α is a constant. Qualitatively similar results are obtained with additive instead of multiplicative noise.where dulation ,9. To ini isThe output neurons form a population code that represents the location of an impending eye movement. Each firing rate is determined by a weighted sum of the GM rates, where the weights correspond to synaptic connections. The mean rate of output neuron wij is the synaptic weight from GM neuron j to output neuron i. The output rates should encode the location of the movement to be made in each trial, so their profile of activity should have a single peak indicating the location that should be reached. The synaptic connections that achieve the correct sensory-motor alignment are found through an optimal algorithm, which is described further below.where Fi, and is a function of the stimulus and the context. Thus, Ri and Fi refer to the same postsynaptic neuron, but one is the actual driven response and the other is the intended response. The functions f, g and F vary across tasks, as described next.Equation 2 is used when the GM neurons drive the output neurons. But for each task, there is also an intended or desired response for each output unit. This is denoted as x in two possible contexts, labeled y = 1 and y = -1. The response should be a movement toward the location equal to xy. The firing rates depend on the following functions. The tuning curves are Gaussians,In this task Fig. , stimuliaj between -25 and +25 and σf = 4 . Because there are two conditions, the modulatory functions gj take only 2 values, 1 and γ, where γ is the minimum gain. Crucially, one half of the GM neurons have gj(y = 1) = 1 and gj(y = -1) = γ, whereas the other half have the opposite context preference, gj(y = 1) = γ and gj(y = -1) = 1. The only exceptions are Figs. gj for each neuron were chosen randomly from uniform distributions. In this task, the desired response of output neuron i iswith the preferred stimulus location in Figs.  or σf = ci is the preferred movement location of unit i and σF = 4. Therefore, the output profile of activity (obtained by plotting Fi vs ci) should be a Gaussian centered at the intended target location, x or -x, depending on the context.where y can take many values. The tuning functions fj, are the same (Equation 3) and the output responses again encode the location given by xy (Equation 4). The gain factors depend on which of two possible representations is used. When context is encoded discontinuously, five scales are used, y = - 1, -0.5, 0, 0.5 or 1, so the modulatory functions gj must take five values; these areThe scaling task is identical to the antisaccade task, except that the context gj(y) = {1, 0.9, 0.75, 0.65, 0.5}.     (5)gj values are jittered by small, random amounts , or viceversa (y = 2). In addition, there is a no-go condition (y = 3), for a total of three contexts. The orientation tuning curves are given by cosine functions,In this task Fig. , x is thaj is now a preferred orientation between -90° and +90°. The modulation functions gj are generated as in the discontinuous version of the scaling task, except with three values,where gj(y) = {1, 0.75, 0.5}.     (8)The order in which each GM neuron prefers the three contexts is random. The responses of the motor neurons are given byy = 3 being the no-go condition and σF = 4. Here, M(x, y) is the correct movement location, either -10 or +10, when orientation x is presented in condition y. Specifically, for y = 1, M = -10 if x < 0 and M = +10 if x > 0; and for y = 2, M = -10 if x > 0 and M = +10 if x < 0. In no-go trials, all output responses should stay at the baseline level, B.with PR(x), which is a function of orientation. Those results are fitted to the curveSimulation results in the orientation discrimination task are presented in terms of the probability of generating a movement toward the target on the right, ae, which is the center point, or bias, and be, which is inversely proportional to the maximum slope. The discrimination threshold is defined as one half of the difference between the values of x that give PR = 0.75 and PR = 0.25; for Equation 10 it is equal to be erfinv(1/2), where erfinv is the inverse of the error function.where erf is the error function. This expression has two parameters: The synaptic weights are chosen so that, on average, the driven and desired responses of the output neurons are as close as possible. This means thatwij is the connection from GM neuron j to output neuron i. The angle brackets indicate an average over all values of x and y and over multiple trials. The optimal connections are found by taking the derivative of the above expression with respect to wpq, setting the result equal to zero, and solving for the connections. The result ismust be minimized. As in Equation 2, whereCkj ≡ <rkrj>     (13)Lkj ≡ <Fk(x, y)rj>.     (14)C-1 is the inverse of the correlation matrix C defined above. This inverse (or the pseudo-inverse) is found numerically. To calculate the averages defined above, the GM rates for all values of x and y are needed. These are found by evaluating Equation 1 plus the noise term for each GM neuron. When the noise is uncorrelated across neurons, as in the simulations, it only contributes to the diagonal of C. Because the variance of the noise is equal to α times the mean response, noise adds an amount α <ri> to element Cii of the correlation matrix. Except for this, all averages Ckj and Lkj are obtained from the mean input responses given by Equation 1 and the corresponding Fi functions of the output neurons.Equations 12–14 are the recipe for setting the connections. Here Having specified the parameters of the network (number of GM and output units, tuning and gain functions, stimulus-movement association), the procedure for setting the synaptic weights is run only once. Afterward, the connections are not adjusted any further.fj(x) and gj(y). The effects of other possible interactions between stimulus and context are investigated using four alternative expressions in place of Equation 1. First, a linear combination of sensory and context signals,Equation 1 is based on an exact multiplication between Then, three nonlinear interactions. The first one is based on rectification,rj = rmax [fj(x) + gj(y) - 1]+ + B,     (16)x]+ = max{0, x}. The second one uses a sigmoid function,where [as and bs. The third nonlinear interaction is based on a power law,The sigmoid is widely used in artificial neural networks  and has and has two parameters too. This type of expression approximates some of the gain effects observed experimentally . The frex and y, (2) generating all GM responses (Equation 1), (3) calculating the driven, output responses (Equation 2), and (4) determining the encoded movement Mout by using the center of mass of the motor activity profile (Equation 19). Finally, the encoded movement is compared to the movement Mdesired that should have been performed given x and y – their difference is the error in that particular trial.Having specified a task, the tuning and gain curves of the GM neurons, and the network connections, the model is tested in a series of trials of the task. Each trial consists of the following steps: (1) specifying the stimulus and context, Mout is equated with the center of mass of the output population,The encoded movement ci is the preferred target location of output unit i. The root-mean-square average of the motor error is used to quantify performance over multiple trials,where Mout - Mdesired> ≈ 0. Thus, σCM is the standard deviation of the motor error, and measures the accuracy of the output population as a whole.where only go trials are included in the calculation. On average, the encoded movement is very near the desired one, <ci uniformly spaced between -25 and 25. Preferred stimulus values aj and preferred context values bj are first distributed uniformly and then jittered by small, random amounts.In all tasks, 25 output units are used, with All simulations were performed using Matlab (The Mathworks, Natick, MA). The source code is available on request.This section shows that, with a finite number of contexts, gain modulation is functionally equivalent to a switch. More precisely, for a discrete number of contexts and everything else being equal, a network of partially modulated neurons can generate the same mean downstream responses as a network of switching neurons.M populations or groups of sensory neurons with identical sets of tuning functions fj(x). There are N neurons in each population, so index j runs from 1 to N. These populations project to a postsynaptic neuron through synaptic connections , where the superscript indicates the presynaptic population of origin. Thus,  is the synaptic weight from neuron j in group p to the postsynaptic unit. The sensory neurons are gain modulated, so the mean response of unit j in population p is given byConsider x and y label the stimulus and the context, as before. Next, assume that there are M possible contexts, so y can take integer values from 1 to M. Therefore, the gain factors can be expressed as three-dimensional arrays, and the presynaptic firing rates can be rewritten aswhere  corresponds to the gain of unit j in population p during context k. With this notation, the response of the downstream neuron during context k becomesHere, j, the coefficient in front of the tuning function is given by the product of an M-dimensional vector of weights times an M × M matrix of gain factors.where the sums are over all populations and all units in each population. Note that, for each index The essential idea is to compare the response of the postsynaptic unit under two conditions: when only one input population is active in any particular context , and when the populations are only partially suppressed, with different combinations of gain factors for each context. For this, the hat symbol ^ is used to label all quantities obtained in the former case, with switching neurons; that is, the hat means 'obtained with full modulation'.j is equal to the identity matrix,Full modulation occurs when the matrix of gain factors for all the units with index y = 1 and is 0 otherwise; for population 2, the gain is 1 when y = 2 and is 0 otherwise, and so forth. Substituting this expression in equation 23 gives the firing rate of the downstream unit when driven by switching neurons,According to this expression, for population 1, the gain is 1 when (x, y = k) implements a different function of x for each context value, such that the function expressed in context 1 depends only on the weights from the first population of switching neurons, , the function expressed in context 2 depends only on the weights from the second population, , and so on. This is the situation depicted in Figs. Again, the hat simply indicates that the quantity was obtained with maximally modulated input neurons. In this case, R(x, y = k), equal to the response obtained with full modulation, (x, y = k)? Compare the right hand sides of Equations 23 and 25; for them to be the same, the coefficients in front of the tuning functions must be equal; that is,On the other hand, the postsynaptic response driven by partially modulated units is simply as in Equation 23, where the absence of a hat means 'obtained with partial modulation'. Under what conditions is the output response driven by partially modulated neurons, This condition is satisfied if the weights with partial modulation are set equal tohj is the inverse of the matrix of gain factors gj; that is, . Therefore, the key constraint here is that the gain factors in the partial modulation case must have linearly independent values across contexts, so that the inverses exist; in other words, the matrices gj must have full rank. An important consequence of this is that for M > 2, the gain of each neuron as a function of context ( in Equation 21) must be nonlinear.where fj(x)). For the recipe to apply, the gain factors in the latter must have the appropriate inverses, but otherwise they are arbitrary. Because each possible function that a network can generate corresponds to a different matrix of synaptic connections, this implies that all the possible functions of x that the output can implement with fully switching neurons can be replicated with partial gain modulation.Equation 27 is the key result. It provides a recipe for going from a network of switching neurons to a network of partially modulated neurons . These determine the possible functions of x that can be generated downstream – that is, the available sensory-motor maps – but have no effect on how these are switched or selected. Finally, the result is also valid if the postsynaptic response is equal, not simply to the weighted sum of GM responses, but to an arbitrary function of that sum.This statement is exact when there is no noise; with noise it applies to the average downstream responses. Notice that this result is independent of the tuning functions To illustrate the result in Appendix A, consider a simple case with two populations and two contexts, as in Figs. in context 1, andM = 2. Context turns one sensory population on and another off. Now, how can we obtain the same downstream responses, as functions of x, when the GM neurons are partially modulated? First, suppose that the modulation matrices arein context 2. This is simply Equation 25 for γ ≥ 0 for the non-preferred one; the full-modulation case is recovered when γ = 0. For simplicity, these factors are the same for all units in each population, so there is no variation across index j. This matrix was used in Fig. γ = 0 and γ = 0.5 for the left and middle columns, respectively, and in Figs. The gain factors can only take two values, 1 for the preferred context, and 1 >Next, substitute into the transformation rule found earlier, Equation 27; the result isp = 1, 2 and the gain factors in Equation 30) become identical to the rates driven by the switching neurons (Equations 28,29). This is the linear transformation used in Figs. With these synaptic weights, the downstream responses driven by the partially modulated neurons , but now it has a variance, which is equal towhere .Here, the angle brackets indicate an average over trials, which affects the noise terms only. To go from the second to the third line above, the key is to assume that the fluctuations are independent across neurons, such that γ. The variance  of the postsynaptic response driven by switching neurons is exactly as in Equation 35, but with  and p = 1,2. This must be compared to the variance obtained with partial modulation for the same mean postsynaptic responses. The synaptic weights that achieve this are given by Equations 32 and 33; substituting those into Equation 35 givesThe next step is to compare the variance of the postsynaptic unit when driven by the switching neurons and by the regular, partially modulated GM neurons. For simplicity, consider the same 2 × 2 case as in Appendix B, where the modulation is parameterized by  obtained when the response is driven by fully modulated, switching neurons. Here, a depends on the weights , but is not a function of γ,This is the variance of the postsynaptic response driven by an array of partially modulated GM neurons as a function of the variance b is a constant. Note that a is a measure of the overlap between the sets of connections from the two populations. The dependence of  on the weights is such that a ≤ 2.where x and y for all γ, its variability changes with γ. A similar result is obtained when the variance of the input firing rates is proportional to their mean. In that case,Equation 36 shows that, although the average postsynaptic response is the same function of  = 1. A calculation analogous to the one just described leads towith a is the same as in Equation 37, except with a different proportionality constant. In this case, it is still true that a ≤ 2. This expression was used to generate the continuous lines in Fig.  was simply the variance in the postsynaptic firing rate found from the simulations with γ = 0, and b was chosen to generate the best fit to the rest of the simulation data points.where γ. This depends on a, which is a measure of the similarity between the sensory-motor maps established in the two contexts. For instance, when the two maps are the same,  for all j, and a attains its maximum value, 2. In that case, the variance with partially modulated neurons is always smaller than with switching neurons. This makes sense: if the maps in the two contexts are the same, it is always better to have the two populations active at the same time, as this reduces the noise. According to the analysis, when a = 2 and γ = 1, Equation 36 gives . The variance is divided by 2 because noise is additive and there are two active populations doing the very same thing. In contrast, when the two maps are different, their respective synaptic weights are also different, and a is either positive but much smaller than 2, or negative. Then,  might have a minimum for some intermediate value of γ, or may increase monotonically, which is what happens in Figs. Equations 36 and 39 do not always increase monotonically with GM, gain-modulated.


There is evidence of a contribution of early life socioeconomic exposures to the risk of chronic diseases in adulthood. However, extant studies investigating the impact of the neighborhood social environment on health tend to characterize only the current social environment. This in part may be due to complexities involved in obtaining and geocoding historical addresses. The Life Course Socioeconomic Status, Social Context, and Cardiovascular Disease Study collected information on childhood (1930–1950) and early adulthood (1960–1980) place of residence from 12,681 black and white middle-aged and older men and women from four U.S. communities to link participants with census-based socioeconomic indicators over the life course.Most (99%) participants were linked to 1930–50 county level socioeconomic census data  corresponding to childhood place of residence. Linkage did not vary by race, gender, birth cohort, or level of educational attainment. A commercial geocoding vendor processed participants' self-reported street addresses for ages 30, 40, and 50. For 1970 and 1980 censuses, spatial coordinates were overlaid onto shape files containing census tract boundaries; for 1960 no shape files existed and comparability files were used. Several methods were tested for accuracy and to increase linkage. Successful linkage to historical census tracts varied by census (66% for 1960, 76% for 1970, 85% for 1980). This compares to linkage rates of 94% for current addresses provided by participants over the course of the ARIC examinations.There are complexities and limitations in characterizing the past social context. However, our results suggest that it is feasible to characterize the earlier social environment with known levels of measurement error and that such an approach should be considered in future studies. Notwithstanding concerns about the accuracy in the assignment of statistical tabulation areas by commercial geocoders [Consideration of the impact of neighborhood social environment on health is now common in social epidemiologic studies -7. Whileeocoders -14, effoeocoders .Obtaining and geocoding historical addresses is more complex and has rarely been undertaken despite the potential advantages derived from its inclusion in life course studies. The completeness and accuracy of historical addresses may not be as high as that of current addresses, unless added care is taken during data collection. Further, widespread use of geocoding in research applications is relatively new and commercial geocoding databases are typically optimized to current street atlases and most recent census tract boundaries. Accurate past addresses, even when assigned correct spatial coordinates, would not be linked with correct historical social contextual data if census tracts had not been defined or summary census data was not available for the area when an individual resided at the address or if the census boundaries had changed over time.The Life Course SES, Social Context, and Cardiovascular Disease (LC-SES) Study retrospectively collected place of residence during childhood and earlier adulthood on a cohort of middle-aged and older persons. We report on the methods used and our success rate in placing participants into historical census areas and linking them with measures of the social context over time based on self-reported place of residence during childhood and at ages 30, 40, and 50 years.The procedures used to obtain the results described in this section are explained in detail in the methods section of this paper as well as in the LC-SES Study manual of procedures, available on the study website .Of 12,681 participants, we excluded 304 who reported living outside of the United States during most of their childhood. Of the remaining 12,377 participants 86% provided apparently correct information on county and state and 10% provided a county which was misspelled. Spelling errors were corrected using a listing of counties in the U.S. available on a publicly accessible website . The remParticipants ranged in age from 45–64 years at the baseline ARIC examination 1987–89). Given their 20 year age span, the years at which they were aged 30 (and 40 and 50) years ranged over several decades, requiring that those from different birth cohorts be linked to data from different census years . Success rates associated with efforts to manually assign historic tracts to addresses varied considerably across study areas and also according to the reasons for the failure of the automated geocoding procedure. Of the addresses which we attempted to manually assign a census tract, we were successful for 54% of Forsyth, NC addresses, 45% of Jackson, MS addresses, 30% of Minneapolis, MN address and 29% of Washington County, MD addresses (data not shown). Rates were particularly low in MD because many roads were located in areas not classified into tracts in the 1960 census and because the conversion to a grid address system – during which time some streets renumbered and renamed – did not occur until the early 1990s. In contrast, the low success rate in MN occurred primarily because the tracts were physically small and streets tended to cross multiple tracts.Figure Table There were variations, albeit inconsistent, in early life sociodemographic characteristics and assignment to census tract of residence at ages 30, 40, and 50. Those living outside of their current (at baseline) state of residence during childhood were markedly less likely to be linked with a census tract at ages 30–50, while those whose parents were homeowners were more likely to be assigned tracts. Those who had fathers with twelve or more years of education or who were in managerial and professional or in farming occupations were modestly but more heavily represented in the groups not assigned tracts. In contrast, those with fathers who were in blue collar occupations  were consistently less likely to be in the group not assigned tracts. These differences by fathers' occupations, while consistent, were generally modest.Assessment of social circumstances in childhood and early adulthood in life course studies is typically limited to individual level measures of parental / own occupation or education -10. The Match rates of participants' addresses to the 1960–1980 U.S. census tracts were lower largely for three reasons: limited ability to recall complete historical addresses, obsolete or unusable addresses , and the previously incomplete coverage of census tracts in the U.S. Linkage rates were considerably higher in 1970 when census tracts were in place at all of our study sites. Now the coverage of census tracts is complete for the U.S. and grid address systems are common even in rural areas. Thus, studies of more recent birth cohorts though still faced with limitations of recall would be expected to have higher linkage rates.® files represent streets as a series of segments. When streets consisted of more than one segment, even when all were located within a single tract, it appears that commercial geocoding software was not able to assign a census tract. We were able to assign census tracts to a sizable portion of these addresses by using detailed street maps overlaid with historical census boundaries. However, this process involves multiple steps and is labor intensive and thus can be practically implemented only in areas where a sizable number of addresses are located.The yield from attempts to commercially geocode incomplete street addresses (e.g., street name but not number) were quite low, even when street fell completely within the boundaries of one census tract. TIGER/LineRecall of county, city, and state of residence during childhood was virtually complete, while recall of street address of former places of residence was more limited. The potential limitations of retrospectively recalled data are known ,21, suggLinkage to county-level place of childhood residence did not vary by participant sociodemographic characteristics. In contrast, successful linkage of the later but more detailed address information to 1960–1980 census tract data varied by sociodemographic characteristics (gender, family income, father's and own education). More striking was the substantial difference seen between those born in vs. outside the study state. Those born outside of the study states were between 1.5 and 1.9 times more likely to not be assigned a tract than those born in one of the study states. To some extent, this occurred because a higher proportion of the participants born in other states originated from areas lacking census tracts at the time of the pertinent historical census.The optimal geographical unit of analysis for contextual measures is discussed in the literature -24. StudOur purpose in assigning current and former adulthood places of residence to census tracts was to link participants with census-based neighborhood profiles to provide area-based measures of the social context (s) across epochs spanning early to later adulthood. Although the approach presented here had not been previously attempted and has logistical complexities, its feasibility, success and error rates are now documented. The opportunity to acquire area-based measures of SES in a historical context does not obviate the methodologic challenges associated with life course research, however. Among the latter it is worth mentioning that many census variables differ across censuses (see table entitled "SES var by census in the Census Tract SES section of the LC-SES Study website ). For exThe importance of the social and economic environments in influencing health is increasingly recognized, yet most research to date is limited to the current social context -6. We beThe Atherosclerosis Risk in Communities (ARIC) study is an investigation of the etiology and natural history of atherosclerosis and its sequelae. At baseline (1987–89), 15,792 African American and white middle-aged men and women from four U.S. communities  were included. An account of the design and procedures is published . Since bAn ancillary study to ARIC, the LC-SES Study was initiated in Spring 2001 to examine the association between SES across life and adult CVD-related conditions, and to determine the extent to which the current and historical context [neighborhood estimated at the county (early childhood) and census tract (early adulthood) level] modify the association of individual-level SES exposures and CVD. Trained interviewers administered a telephone questionnaire including 44 questions about parental and early adulthood occupational and educational exposures, current sociodemographic characteristics and childhood and earlier adulthood places of residence. Participants responding to the questionnaire (N = 12681), represent 81% of the ARIC baseline cohort and approximately 94% of cohort survivors. Additional details about the LC-SES Study can be found in the manual of procedures  and otheParticipants were asked "Where did you mostly live when you were a child? If possible, give me the city/town, county, and state of residence." Participants were also asked to provide their address (street number and name, city, county, state, and zip code) at various points during adulthood. Everyone was asked to provide addresses for age 30 (n = 12,681), and those who had first participated in the ARIC study after age 49 (n = 8,971) or 59 (n = 2,496) were also asked to provide addresses for ages 40/50 and 50, respectively. Those unable to provide an exact address were asked to provide the street name and the closest cross-street.The year at which participants were aged ten years, which represented the approximate midpoint of childhood, was determined in order to link with the county-level socioeconomic data from the closest census year (1930, 1940, 1950). When a city, but not a county was provided, we used a publicly available website to attempt to identify the correct county . County Prior to geocoding, all state data was standardized to conform to the two-digit U.S. Postal Services state coding system. Within each state the accuracy of the spellings of cities were verified. Street addresses were reviewed and computer programs written to correct obvious misspellings and to standardize formats. We did not submit zip codes because those accompanying the historical address could have changed over time. Because our goal was to classify the social environment where participants lived, we excluded post office box addresses as they do not necessarily correspond to actual residences. These along with other incomplete and unusable addresses (e.g., institutions, military APO, c/o, etc.) were not sent for geocoding. After editing, addresses and an encrypted study ID number were sent to a commercial vendor under contractual terms of confidentiality negotiated by university counsel and approved by the IRB.The vender assigned to each address: spatial coordinates, Federal Information Processing Standards (FIPS) codes for statistical tabulation areas corresponding to 1990 census boundaries, and a match code describing the degree of accuracy of the geocoding. The accuracy rating assigned by the vendor ranged from" house range address matches" (best) to the "centroid of county" (worst). As we were interested in accurately classifying each participant's place of residence at the level of the census tract , we accepted only house range address matches (e.g., accuracy at level of exact address, intersection, or street segment) or matches to centroids of zip code areas where everyone lived within a census block group, census tract or where more than 80% of addresses in area were located in the same tract. Rural routes were sent to the vendor but these addresses were not successfully geocoded.Two methods were considered to link the spatial coordinates obtained from the vendor with the appropriate historical census tract. The overlay method uses the spatial coordinates assigned to exact address matches in conjunction with historical boundary maps to place addresses into historical tracts. The comparability file method uses current US Bureau of the Census tract assignments that are traced back in time stepwise to 1980 tracts, then from 1980 to 1970 tracts using files that describe tract changes from decade to decade. As a test, we compared the 1970 tract assignments by the two methods for 13,044 addresses that were successfully geocoded to the 1990 census by the geocoding vendor. While all addresses were assigned tracts using the overlay method, 36% could not be assigned a 1970 tract using the comparability files due to census tract merges (a tract contains parts of more then one tract from the previous decade). Of the remaining addresses (n = 8348), 97% were assigned the same tract by both methods. Because there are known errors in the assignment of spatial coordinates by commercial geocoding vendors ,12,14, wWe determined the census year (1960, 1970, 1980) that corresponded most closely to when the participant resided at each address. Arcview GIS Version 3.3 software was used to overlay the spatial coordinates assigned to addresses by the vendor onto Geolytics, Inc. shape files of census tract and block numbering area boundaries of the appropriate census year [Census CD 1970, Census CD 1980]. The spatial coordinates falling within the historical tracts were assigned the corresponding tract number. Figure Electronic shape files were not available for the 1960 census. Thus, 1960 addresses were placed into 1970 tracts using the overlay method and then mapped to the appropriate 1960 tracts using files providing data on the correspondence between 1970 and 1960 tracts. These were obtained from print volumes of comparability files published by the US Bureau of the Census  and keye® tools and the street map legends, we attempted to locate each address. If a street was contained within the boundary of a census tract, we assigned it the corresponding tract number. If a street crossed a census tract boundary or was the boundary for two or more tracts, we did not assign a census tract.When a 1960 address fell into a 1970 tract made up of merged 1960 tracts or when addresses were not geocoded by the commercial vendor [street name but not number, cross streets, obsolete addresses (road renamed or renumbered)], we attempted to manually place addresses into historical census tracts. Because this process is labor intensive, we undertook this effort only for addresses which were located within the four ARIC study communities (as a large number of addresses were not clustered in other areas). First, we obtained detailed street maps for the four study areas and overlaid them with census tract boundaries and numbers from the three historical censuses. Then, using web-based MapquestA large number of historical addresses in Washington County, MD were obsolete, because in the early 1990s the state changed to a grid address system to improve emergency response systems. Thus, we obtained historical street maps from the Hagerstown, MD Public Library and tried to locate the original street names in an attempt to manually assign a census tract using the procedure described above.For addresses placed within a 1960–1980 census tract, we linked with tract level socioeconomic data. For 1970 and 1980 we used data from Geolytics, Inc. (Census CD 1970, Census CD 1980). The ODUM Institute at the University of North Carolina, USA provided electronic 1960 census tract data. Jackson MS and Washington County MD had not been assigned census tracts in 1960. For Jackson, MS and the portion of Washington County, MD falling within the Hagerstown city limits, we obtained 1960 census housing data at the level of city blocks from print volumes , and aggKMR conceived of and led the writing of the manuscript. JLW analyzed the data on early adulthood and developed the methods for assigning census tracts to historic addresses. GH, the principal investigator of the LC-SES Study, contributed to the conceptualization and writing of this manuscript. EAW assessed the accuracy of the commercial geocoding and developed standardized procedures for manual geocoding. SK developed standardized procedures for editing the recalled address data. RP analyzed data pertaining to childhood place of residence. DY was instrumental in reviewing methods for placing participants into their historical tracts. AVDR provided expert input on methods of geocoding. All authors helped to frame the ideas, interpret findings, and review drafts of the manuscript.