Estimating Cortical Feature Maps with Dependent Gaussian Processes.

A striking example of brain organisation is the stereotyped arrangement of cell preferences in the visual cortex for edges of particular orientations in the visual image. These "orientation preference maps" appear to have remarkably consistent statistical properties across many species. However fine scale analysis of these properties requires the accurate reconstruction of maps from imaging data which is highly noisy. A new approach for solving this reconstruction problem is to use Bayesian Gaussian process methods, which produce more accurate results than classical techniques. However, so far this work has not considered the fact that maps for several other features of visual input coexist with the orientation preference map and that these maps have mutually dependent spatial arrangements. Here we extend the Gaussian process framework to the multiple output case, so that we can consider multiple maps simultaneously. We demonstrate that this improves reconstruction of multiple maps compared to both classical techniques and the single output approach, can encode the empirically observed relationships, and is easily extendible. This provides the first principled approach for studying the spatial relationships between feature maps in visual cortex.

Sensory experience modifies feature map relationships in visual cortex.

The extent to which brain structure is influenced by sensory input during development is a critical but controversial question. A paradigmatic system for studying this is the mammalian visual cortex. Maps of orientation preference (OP) and ocular dominance (OD) in the primary visual cortex of ferrets, cats and monkeys can be individually changed by altered visual input. However, the spatial relationship between OP and OD maps has appeared immutable. Using a computational model we predicted that biasing the visual input to orthogonal orientation in the two eyes should cause a shift of OP pinwheels towards the border of OD columns. We then confirmed this prediction by rearing cats wearing orthogonally oriented cylindrical lenses over each eye. Thus, the spatial relationship between OP and OD maps can be modified by visual experience, revealing a previously unknown degree of brain plasticity in response to sensory input.

Limitations of Neural Map Topography for Decoding Spatial Information.

UNLABELLED: Topographic maps are common throughout the nervous system, yet their functional role is still unclear. In particular, whether they are necessary for decoding sensory stimuli is unknown. Here we examined this question by recording population activity at the cellular level from the larval zebrafish tectum in response to visual stimuli at three closely spaced locations in the visual field. Due to map imprecision, nearby stimulus locations produced intermingled tectal responses, and decoding based on map topography yielded an accuracy of only 64%. In contrast, maximum likelihood decoding of stimulus location based on the statistics of the evoked activity, while ignoring any information about the locations of neurons in the map, yielded an accuracy close to 100%. A simple computational model of the zebrafish visual system reproduced these results. Although topography is a useful initial decoding strategy, we suggest it may be replaced by better methods following visual experience. SIGNIFICANCE STATEMENT: A very common feature of brain wiring is that neighboring points on a sensory surface (eg, the retina) are connected to neighboring points in the brain. It is often assumed that this "topography" of wiring is essential for decoding sensory stimuli. However, here we show in the developing zebrafish that topographic decoding performs very poorly compared with methods that do not rely on topography. This suggests that, although wiring topography could provide a starting point for decoding at a very early stage in development, it may be replaced by more accurate methods as the animal gains experience of the world.

Optimizing the representation of orientation preference maps in visual cortex.

The colorful representation of orientation preference maps in primary visual cortex has become iconic. However, the standard representation is misleading because it uses a color mapping to indicate orientations based on the HSV (hue, saturation, value) color space, for which important perceptual features such as brightness, and not just hue, vary among orientations. This means that some orientations stand out more than others, conveying a distorted visual impression. This is particularly problematic for visualizing subtle biases caused by slight overrepresentation of some orientations due to, for example, stripe rearing. We show that displaying orientation maps with a color mapping based on a slightly modified version of the HCL (hue, chroma, lightness) color space, so that primarily only hue varies between orientations, leads to a more balanced visual impression. This makes it easier to perceive the true structure of this seminal example of functional brain architecture.

Stripe-rearing changes multiple aspects of the structure of primary visual cortex.

An important example of brain plasticity is the change in the structure of the orientation map in mammalian primary visual cortex in response to a visual environment consisting of stripes of one orientation. In principle there are many different ways in which the structure of a normal map could change to accommodate increased preference for one orientation. However, until now these changes have been characterised only by the relative sizes of the areas of primary visual cortex representing different orientations. Here we extend to the stripe-reared case a recently proposed Bayesian method for reconstructing orientation maps from intrinsic signal optical imaging data. We first formulated a suitable prior for the stripe-reared case, and developed an efficient method for maximising the marginal likelihood of the model in order to determine the optimal parameters. We then applied this to a set of orientation maps from normal and stripe-reared cats. This analysis revealed that several parameters of overall map structure, specifically the difference between wavelength, scaling and mean of the two vector components of maps, changed in response to stripe-rearing, which together give a more nuanced assessment of the effect of rearing condition on map structure than previous measures. Overall this work expands our understanding of the effects of the environment on brain structure.

A scoring system predicting the risk for intensive care unit admission for complications after major lung resection: a multicenter analysis.

BACKGROUND: We aimed to develop and validate a scoring system to predict intensive care unit (ICU) admission for complications after major lung resection for purposes of optimizing planning of resources for patient care. METHODS: Patients undergoing major lung resections performed between 2000 and 2006 at three thoracic surgery units were analyzed for unplanned admission to the ICU for complications. Variables were initially screened by univariate analysis. Selected variables were used in a stepwise logistic regression analysis that was validated by bootstrap analysis. The scoring system was developed by proportional weighting of the significant and reliable predictors estimates and validated on patients operated on in a different center. RESULTS: In the derivation set of 1297 patients, 82 (6.3%) had ICU admission for complications, and 30 died (associated mortality rate, 36.5%). Predictive variables and their scores were pneumonectomy, 2 points; and 1 point each for age older than 65, predicted postoperative forced expiratory volume in 1 second below 65%, predicted postoperative carbon monoxide lung diffusion capacity below 50%, and cardiac comorbidity. Patients were grouped into three risk classes by their scores, which were significantly associated with incremental risk of ICU admission in the validation set of 349 patients. CONCLUSIONS: This scoring system predicts incremental risk of ICU admission for complications after major lung resection. This system may help in assessing the need for additional postoperative resources and in modifying indicators used to determine the appropriateness of initial transfer of postoperative patients from ICU or stepdown status and in developing criteria for future cost-effectiveness trials.

Hosomi-Sakurai reactions of silacyclohexenes.

Silacyclic allyl silanes, derived from silene-diene Diels-Alder reactions, combine with acetals in the presence of Lewis acids to afford, following oxidation of the intermediate fluorosilane, either butane-1,4-diols or tetrahydronaphthalenes containing four contiguous chiral centres with moderate to good diastereoselectivity.

Risk-adjusted morbidity and mortality models to compare the performance of two units after major lung resections.

OBJECTIVE: We sought to develop risk-adjusted morbidity and mortality models to compare the performance of 2 different thoracic surgery units in patients submitted to major lung resections. METHODS: Seven hundred forty-three patients (551 male and 192 female patients) who underwent lobectomy (n = 611) or pneumonectomy (n = 132) from January 2000 through August 2004 at 2 European thoracic units (519 patients in unit A and 224 patients in unit B) were analyzed. Risk-adjusted models of 30-day or in-hospital cardiopulmonary morbidity and mortality were developed by using stepwise logistic regression analyses and validated by means of bootstrap analysis. Preoperative and operative variables were initially screened by using univariate analysis. Those with a P value of less than .10 were used as independent variables in the regression analyses. The regression equations were then used to estimate the risk of outcome, and the observed and predicted outcome rates of the 2 units were compared by using the z test for comparison of proportions. RESULTS: The following regression models were developed. Predicted morbidity: lnR/1-R=-2.4+0.03Xage-0.02XppoFEV1+0.6Xcardiaccomorbidity (Hosmer-Lemeshow statistic = 6.1 [P = .6], c index = 0.65). Predicted mortality: lnR/1-R=-6.97+0.095Xage-0.042XppoFEV1 (Hosmer-Lemeshow statistic = 2.99 [P = .9], c index = 0.77). The models proved to be stable at bootstrap analyses. No differences were noted between observed and predicted outcome rates within each unit, despite an apparent unadjusted better performance of unit B. CONCLUSIONS: The use of risk-adjusted outcome models avoided misleading information derived from the unadjusted analysis of performance. Risk modeling is essential for the evaluation of the quality of care.

Carbon monoxide lung diffusion capacity improves risk stratification in patients without airflow limitation: evidence for systematic measurement before lung resection.

OBJECTIVE: In many centers, carbon monoxide lung diffusion capacity (DLCO) is still not routinely measured in all patients but only in patients with airflow limitation. The objective of the study was to assess the degree of correlation between forced expiratory volume in 1s (FEV1) and DLCO, and verify whether a low predicted postoperative DLCO (ppoDLCO) could have a role in predicting complications in patients without airflow limitation. METHODS: We analyzed 872 patients submitted to lung resection between January 2000 and December 2004 in two units measuring systematically DLCO before operation. Correlation between FEV1 and DLCO was assessed in the entire dataset and in different subsets of patients. A number of variables were then tested for a possible association with postoperative cardiopulmonary complications in patients with FEV1>80% by univariate analysis. Variables with p<0.10 at univariate analysis were used as independent variables in a stepwise logistic regression analysis (dependent variable: presence of cardiopulmonary morbidity), which was in turn validated by bootstrap analysis. RESULTS: The correlation coefficients between FEV1 and DLCO in the entire dataset and in different subsets of lung resection candidates (stratified by age, gender, cause of operation, airflow limitation) were all below 0.5, showing a modest degree of correlation. Two hundred and nineteen of the 508 patients (43%) with FEV1>80% had DLCO<80%. Moreover, in patients with FEV1>80%, logistic regression analysis showed that ppoDLCO<40% was a significant and reliable predictor of postoperative complications (p=0.004). CONCLUSION: The modest correlation between FEV1 and DLCO and the capacity of ppoDLCO to discriminate between patients with and without complications in subjects with a normal FEV1, warrants the routine measurement of DLCO in all candidates for lung resection, irrespective of their FEV1 value, in order to improve surgical risk stratification.