Multivariate Classification Analysis Research Articles

Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods

Polygoni Multiflori Radix (PMR) is increasingly being used not just as a traditional herbal medicine but also as a popular functional food. In this study, multivariate chemometric methods and mass spectrometry were combined to analyze the ultra-high-performance liquid chromatograph (UPLC) fingerprints of PMR from six different geographical origins. A chemometric strategy based on multivariate curve resolution–alternating least squares (MCR–ALS) and three classification methods is proposed to analyze the UPLC fingerprints obtained. Common chromatographic problems, including the background contribution, baseline contribution, and peak overlap, were handled by the established MCR–ALS model. A total of 22 components were resolved. Moreover, relative species concentrations were obtained from the MCR–ALS model, which was used for multivariate classification analysis. Principal component analysis (PCA) and Ward's method have been applied to classify 72 PMR samples from six different geographical regions. The PCA score plot showed that the PMR samples fell into four clusters, which related to the geographical location and climate of the source areas. The results were then corroborated by Ward's method. In addition, according to the variance-weighted distance between cluster centers obtained from Ward's method, five components were identified as the most significant variables (chemical markers) for cluster discrimination. A counter-propagation artificial neural network has been applied to confirm and predict the effects of chemical markers on different samples. Finally, the five chemical markers were identified by UPLC–quadrupole time-of-flight mass spectrometer. Components 3, 12, 16, 18, and 19 were identified as 2,3,5,4′-tetrahydroxy-stilbene-2-O-β-d-glucoside, emodin-8-O-β-d-glucopyranoside, emodin-8-O-(6′-O-acetyl)-β-d-glucopyranoside, emodin, and physcion, respectively. In conclusion, the proposed method can be applied for the comprehensive analysis of natural samples.

DcR3, TFF3, and Midkine Are Novel Serum Biomarkers in Small Intestinal Neuroendocrine Tumors

Small intestinal neuroendocrine tumors (SI-NETs) are amine- and peptide-producing neoplasms. Most patients display metastases at the time of diagnosis; they have an unpredictable individual disease course and the tumors are often therapy resistant. Chromogranin A and 5-hydroxyindoleacetic acid are the biomarkers clinically used most often today, but there is a great need for novel diagnostic and prognostic biomarkers and new therapeutic targets. Sixty-nine biomarkers were screened in serum from 23 SI-NET patients and 23 healthy controls using the multiplex proximity ligation assay (PLA). A refined method, the proximity extension assay (PEA), was used to analyze 76 additional biomarkers. Statistical testing and multivariate classification were performed. Immunohistochemistry and ELISA were performed in an extended cohort. Using PLA, 19 biomarkers showed a significant difference in serum concentrations between patients and controls, and PEA revealed a difference in the concentrations of 17 proteins. Multivariate classification analysis revealed decoy receptor 3 (DcR3), trefoil factor 3 (TFF3), and midkine to be good biomarkers for the disease, which was confirmed by ELISA analysis. All 3 biomarkers were expressed in tumor tissue. DcR3 concentrations were elevated in patients with stage IV disease. High concentrations of DcR3 and TFF3 were correlated to poor survival. DcR3, TFF3, and midkine exhibited elevated serum concentrations in SI-NET patients compared to healthy controls, and DcR3 and TFF3 were associated with poor survival. DcR3 seems to be a marker for liver metastases, while TFF3 and midkine may be new diagnostic biomarkers for SI-NETs.

Large-Scale Meta-Analysis of Human Medial Frontal Cortex Reveals Tripartite Functional Organization.

The functional organization of human medial frontal cortex (MFC) is a subject of intense study. Using fMRI, the MFC has been associated with diverse psychological processes, including motor function, cognitive control, affect, and social cognition. However, there have been few large-scale efforts to comprehensively map specific psychological functions to subregions of medial frontal anatomy. Here we applied a meta-analytic data-driven approach to nearly 10,000 fMRI studies to identify putatively separable regions of MFC and determine which psychological states preferentially recruit their activation. We identified regions at several spatial scales on the basis of meta-analytic coactivation, revealing three broad functional zones along a rostrocaudal axis composed of 2-4 smaller subregions each. Multivariate classification analyses aimed at identifying the psychological functions most strongly predictive of activity in each region revealed a tripartite division within MFC, with each zone displaying a relatively distinct functional signature. The posterior zone was associated preferentially with motor function, the middle zone with cognitive control, pain, and affect, and the anterior with reward, social processing, and episodic memory. Within each zone, the more fine-grained subregions showed distinct, but subtler, variations in psychological function. These results provide hypotheses about the functional organization of medial prefrontal cortex that can be tested explicitly in future studies. Activation of medial frontal cortex in fMRI studies is associated with a wide range of psychological states ranging from cognitive control to pain. However, this high rate of activation makes it challenging to determine how these various processes are topologically organized across medial frontal anatomy. We conducted a meta-analysis across nearly 10,000 studies to comprehensively map psychological states to discrete subregions in medial frontal cortex using relatively unbiased data-driven methods. This approach revealed three distinct zones that differed substantially in function, each of which were further subdivided into 2-4 smaller subregions that showed additional functional variation. Each individual region was recruited by multiple psychological states, suggesting subregions of medial frontal cortex are functionally heterogeneous.

Multimodal Feature Integration in the Angular Gyrus during Episodic and Semantic Retrieval

Much evidence from distinct lines of investigation indicates the involvement of angular gyrus (AnG) in the retrieval of both episodic and semantic information, but the region's precise function and whether that function differs across episodic and semantic retrieval have yet to be determined. We used univariate and multivariate fMRI analysis methods to examine the role of AnG in multimodal feature integration during episodic and semantic retrieval. Human participants completed episodic and semantic memory tasks involving unimodal (auditory or visual) and multimodal (audio-visual) stimuli. Univariate analyses revealed the recruitment of functionally distinct AnG subregions during the retrieval of episodic and semantic information. Consistent with a role in multimodal feature integration during episodic retrieval, significantly greater AnG activity was observed during retrieval of integrated multimodal episodic memories compared with unimodal episodic memories. Multivariate classification analyses revealed that individual multimodal episodic memories could be differentiated in AnG, with classification accuracy tracking the vividness of participants' reported recollections, whereas distinct unimodal memories were represented in sensory association areas only. In contrast to episodic retrieval, AnG was engaged to a statistically equivalent degree during retrieval of unimodal and multimodal semantic memories, suggesting a distinct role for AnG during semantic retrieval. Modality-specific sensory association areas exhibited corresponding activity during both episodic and semantic retrieval, which mirrored the functional specialization of these regions during perception. The results offer new insights into the integrative processes subserved by AnG and its contribution to our subjective experience of remembering. Using univariate and multivariate fMRI analyses, we provide evidence that functionally distinct subregions of angular gyrus (AnG) contribute to the retrieval of episodic and semantic memories. Our multivariate pattern classifier could distinguish episodic memory representations in AnG according to whether they were multimodal (audio-visual) or unimodal (auditory or visual) in nature, whereas statistically equivalent AnG activity was observed during retrieval of unimodal and multimodal semantic memories. Classification accuracy during episodic retrieval scaled with the trial-by-trial vividness with which participants experienced their recollections. Therefore, the findings offer new insights into the integrative processes subserved by AnG and how its function may contribute to our subjective experience of remembering.

Memory Reactivation Predicts Resistance to Retroactive Interference: Evidence from Multivariate Classification and Pattern Similarity Analyses.

Reactivating a previously encoded memory is believed to provide an opportunity to strengthen the memory, but also to return the memory to a labile state, making it susceptible to interference. However, there is debate as to how memory reactivation elicited by a potentially interfering event influences subsequent retrieval of the memory. The findings of the current study indicate that reactivating features idiosyncratic to a particular experience during interference only influences subsequent memory when interference is relatively weak. Critically, reactivation of generic contextual information predicts subsequent source memory when retroactive interference is either strong and weak. The results indicate that reactivation of generic information about a prior episode mitigates forgetting due to retroactive interference.

Serum biomarker panels for diagnosis of gastric cancer.

PurposeCurrently, serum biomarkers that are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinomas are not known. In this study, ten serum markers were assessed using the Luminex system and enzyme-linked immunosorbent assay for the diagnosis of gastric cancer and analysis of the relation between prognosis and metastases.Patients and methodsA training set consisting of 228 gastric adenocarcinoma and 190 control samples was examined. A Luminex multiplex panel with nine biomarkers, consisting of three proteins discovered through our previous studies and six proteins previously reported to be cancer-associated, was constructed. One additional biomarker was detected using a commercial kit containing EDTA. Logistic regression, random forest (RF), and support vector machine (SVM) were used to identify the panel of discriminatory biomarkers in the training set. After selecting five proteins as candidate biomarkers, multivariate classification analyses were used to identify algorithms for diagnostic biomarker combinations. These algorithms were independently validated using a set of 57 gastric adenocarcinoma and 48 control samples.ResultsSerum pepsinogen I, serum pepsinogen II, A Disintegrin And Metalloproteinase domain-containing protein 8 (ADAM8), vascular endothelial growth factor (VEGF), and serum IgG to Helicobacter pylori were selected as classifiers in the three algorithms. These algorithms differentiated between the majority of gastric adenocarcinoma and control serum samples in the training/test set with high accuracy (RF 79.0%, SVM 83.8%, logistic regression 76.2%). These algorithms also differentiated the samples in the validation set (accuracy: RF 82.5%, SVM 86.1%, logistic regression 78.7%).ConclusionA panel of combinatorial biomarkers comprising VEGF, ADAM8, IgG to H. pylori, serum pepsinogen I, and pepsinogen II were developed. The use of biomarkers is a less invasive method for the diagnosis of gastric adenocarcinoma. They may supplement clinical gastroscopic evaluation of symptomatic gastric cancer patients and enhance the diagnostic accuracy.

Gastrointestinal Acuity Score (GAS): A Comprehensive Risk Score Incorporating Histologic Grade and Clinical Factors to Predict Need for Second Line Therapy, Non-Relapse Mortality, and Overall Survival in Lower Gastrointestinal Graft Vs. Host Disease

Lower Gastrointestinal Graft versus Host Disease (GI GVHD) is a major cause for GVHD related non-relapsed mortality (NRM). High dose corticosteroid is the initial therapy for suspected lower GI GVHD, and additional therapy is often required for steroid refractory cases. The identification of high-risk patients is critical as timely intensification of treatment through early institution of second line therapy improves outcomes. To develop a reliable lower GI GVHD risk scoring system, Gastrointestinal Acuity Score (GAS), we evaluated 210 consecutive patients who underwent endoscopic biopsy for suspected lower GI GVHD within 6 months from the transplant from 2009 to 2012 at M.D. Anderson Cancer Center.We first identified 5 significant prognostic factors that accounted for the increased NRM in lower GI GVHD using univariate analysis: histologic grade, clinical stage, age, multi-organ GVHD, and donor type (Table 1). Gender, donor cell type, conditioning regimen (myeloablative vs non-myeloablative), time of diagnosis, disease status did not significantly influence NRM. Next, we performed multivariate Classification and Regression Tree (CART) analysis that utilizes sequential binary decision nodes to categorize 197 patients into subgroups according to their risk profile and outcome (Figure 1). The decision tree identified 7 subgroups starting with the presence of multi-organ GVHD as the first major decision node. This was followed by clinical stage 3/4, Age > 40 years, histology grade 3/4, and histology grade 1. We then consolidated subgroups with comparable risk profiles into low, intermediate, and high NRM risk groups (Figure 1).The 3 NRM risk groups correlated with day 28 response, the need for second line therapy and overall survival (Figure 2). The complete response rates at day 28 from the initiation of steroid therapy were significantly lower in the high (15%, p<0.001) and intermediate (43%, p=0.003) risk group compared to the low-risk group (70%). Accordingly, the high-risk patients were 4.3 (p < 0.001) and 1.6 (p=0.02) folds more likely to need second line therapy compared to low and intermediate risk patients, respectively. The intermediate risk patients were 2.7 (p=0.02) folds more likely to need second line therapy compared to the low risk group. In addition, the high-risk group was associated with the highest NRM (HR 5.4, p<0.001), followed by the intermediate risk (HR 3.8, p<0.001) compared to the low risk group. This translated into worse overall survival at 1 year for the high-risk patients. Lastly, there was a trend towards a lower chronic GVHD rate in the low risk group (25%, HR=0.6, p 0.06) compared to the high and intermediate risk groups (37%).In conclusion, we developed a novel risk scoring system, Gastrointestinal Acuity Score (GAS), incorporating histologic grades and clinical factors through multivariate CART analysis, and demonstrated that GAS predicts both early outcomes (day 28 CR rate and need for second line therapy) and late outcomes (NRM and overall survival) in patients with lower GI GVHD. Once prospectively validated in a larger cohort of lower GI GVHD patients, it will be of great use for clinicians with limited access to GVHD biomarker analysis in making treatment decisions as our new risk scoring system utilizes readily available variables. [Display omitted] [Display omitted] [Display omitted] DisclosuresAlousi:Therakos, Inc: Research Funding.

Synchronous fluorescence and multivariate classification analysis as a screening tool for determining Sudan I dye in culinary spices

Spices are a globally traded commodity which has been found to be adulterated with forbidden Sudan dyes. This work proposes a screening method for determining the adulteration of paprika varieties (mild, hot and smoked) with Sudan I dye, based on constant-wavelength synchronous fluorescence spectroscopy with multivariate classification. Different wavelength-intervals (Δλ) were evaluated. Classification models were built with Partial Least Squares-Discriminant Analysis (PLS-DA) at two Sudan I dye concentration levels (1 and 5 mg L−1) and they were tested with samples at a lower level (0.5 mg L−1). Classification results were quite satisfactory when a strategy based on first-derivative spectra was used for improving classification results. Δλ = 60 nm was chosen as the optimum wavelength interval giving a 100% of sensitivity and specificity. These results are promising because the risk of assigning adulterated samples as safe to be consumed is highly minimized. The proposed method is feasible, rapid and simple taking advantage of Sudan I fluorescence phenomena in a direct way.

Characterizing the associative content of brain structures involved in habitual and goal-directed actions in humans: a multivariate FMRI study.

While there is accumulating evidence for the existence of distinct neural systems supporting goal-directed and habitual action selection in the mammalian brain, much less is known about the nature of the information being processed in these different brain regions. Associative learning theory predicts that brain systems involved in habitual control, such as the dorsolateral striatum, should contain stimulus and response information only, but not outcome information, while regions involved in goal-directed action, such as ventromedial and dorsolateral prefrontal cortex and dorsomedial striatum, should be involved in processing information about outcomes as well as stimuli and responses. To test this prediction, human participants underwent fMRI while engaging in a binary choice task designed to enable the separate identification of these different representations with a multivariate classification analysis approach. Consistent with our predictions, the dorsolateral striatum contained information about responses but not outcomes at the time of an initial stimulus, while the regions implicated in goal-directed action selection contained information about both responses and outcomes. These findings suggest that differential contributions of these regions to habitual and goal-directed behavioral control may depend in part on basic differences in the type of information that these regions have access to at the time of decision making.

Application of MOOSY32 eNose to Assess the Effects of Some Post Harvest Treatments on the Quality of ‘Salustiana’ Orange Juice

A new prototype of Electronic Nose instrument, Multisensory Odor Olfactory System MOOSY32, with a processing method based on a multivariate classification analysis was used to assess different postharvest and storage treatments effects to Salustiana oranges. The analysis method is based on the measurement of the volatile compounds produced under different environmental and operational conditions. The Electronic Nose system revealed that orange juice flavor changes even when juices are analyzed right after each treatment and fruits are stored under refrigerated conditions. The instrument was able to detect even small changes in the aromatic pattern of the juices, confirming that the packing line itself is able to cause perceptible changes in the flavor. This can be a new and important finding in the Salustiana orange treatment that can lead to a significant improvement of fruits quality on the markets.

Anatomical Abnormalities in Autism?

Substantial controversy exists regarding the presence and significance of anatomical abnormalities in autism spectrum disorders (ASD). The release of the Autism Brain Imaging Data Exchange (∼1000 participants, age 6-65 years) offers an unprecedented opportunity to conduct large-scale comparisons of anatomical MRI scans across groups and to resolve many of the outstanding questions. Comprehensive univariate analyses using volumetric, thickness, and surface area measures of over 180 anatomically defined brain areas, revealed significantly larger ventricular volumes, smaller corpus callosum volume (central segment only), and several cortical areas with increased thickness in the ASD group. Previously reported anatomical abnormalities in ASD including larger intracranial volumes, smaller cerebellar volumes, and larger amygdala volumes were not substantiated by the current study. In addition, multivariate classification analyses yielded modest decoding accuracies of individuals' group identity (<60%), suggesting that the examined anatomical measures are of limited diagnostic utility for ASD. While anatomical abnormalities may be present in distinct subgroups of ASD individuals, the current findings show that many previously reported anatomical measures are likely to be of low clinical and scientific significance for understanding ASD neuropathology as a whole in individuals 6-35 years old.

Understanding biophysical and socio-economic determinants of maize (Zea mays L.) yield variability in eastern India

The aim of this paper was to investigate the key factors limiting maize (Zea mays L.) productivity in eastern India to develop effective crop and nutrient management strategies to reduce yield gap. A series of farm surveys was conducted in two distinct agro-ecological zones of eastern India to evaluate the importance of crop management and structural constraints for maize productivity in a range of socio-economic settings prevalent in smallholder farms. Surveys revealed yield gap and yield variations among farms across growing seasons. Lower yields of farmers were mainly associated with farmer's ethnic origin, availability of family labor, land ownership, legumes in cropping sequence, irrigation constraints, seed type, optimal plant population, labor and capital investment, and use of organic manure. These constraints varied strongly between sites as well as growing seasons. Stochastic Frontier Analysis suggested intensification of farm input use and removal of socio-economic and structural constraints for increasing efficiency in maize production. The use of multivariate classification and regression tree analysis revealed that maize yield was affected by multiple and interacting production constraints, differentiating the surveyed farms in six distinct resource groups. These farm types lend scope for introducing typology-specific crop management practices through appropriate participatory on-farm evaluation/trials. Summarily, this research indicated that interacting production constraints should be addressed simultaneously, considering the need of different farm types, if significant productivity improvements are to be achieved. This will be, however, more challenging for less endowed farms due to lack of social and financial capital to improve management intensity.A typology-specific farm support strategy may be formulated to offset this lack of entitlement among resource-poor farmers.

Selection of yeasts with multifunctional features for application as starters in natural black table olive processing

Yeasts are unicellular eukaryotic microorganisms with a great importance in the elaboration on many foods and beverages. In the last years, researches have focused their attention to determine the favourable effects that these microorganisms could provide to table olive processing. In this context, the present study assesses, at laboratory scale, the potential technological (resistance to salt, lipase, esterase and β-glucosidase activities) and probiotic (phytase activity, survival to gastric and pancreatic digestions) features of 12 yeast strains originally isolated from Greek natural black table olive fermentations. The multivariate classification analysis carried out with all information obtained (a total of 336 quantitative input data), revealed that the most promising strains (clearly discriminated from the rest of isolates) were Pichia guilliermondii Y16 (which showed overall the highest resistance to salt and simulated digestions) and Wickerhamomyces anomalus Y18 (with the overall highest technological enzymatic activities), while the rest of strains were grouped together in two clearly differentiated clusters. Thus, this work opens the possibility for the evaluation of these two selected yeasts as multifunctional starters, alone or in combination with lactic acid bacteria, in real table olive fermentations.

Assessment of multivariate classification analysis methods on 3‐dimensional anatomical distribution of functionally identified cat pre‐Bötzinger complex microinjection sites (712.15)

Previous work from our laboratory has demonstrated that multiple patterns of phrenic motor output can be elicited by chemical stimulation of anatomically distinct sub‐regions of the in vivo adult cat pre‐Bötzinger complex. To gain further insight into the anatomical distribution of the microinjection sites yielding these different discharge patterns, we implemented two multivariate classification analysis methods: Fisher Linear Discriminant (FLD) and probabilistic neural network (PNN). For FLD analysis, the multivariate data were transformed into univariate observations by computing the inverse of a pooled variance‐covariance matrix, which was then translated back to the histological coordinates to predict physical sub‐region boundaries. For PNN analysis, a 3‐layered radial basis neural network was created with (1) the input vector for each discharge pattern yielding an output value according to how close the input vector was to each pattern weight vector, (2) the contributions for each class of inputs was summed to produce a net output vector of probabilities; and (3) a transfer function was applied to the outputs to identify the maximum probabilities and produce a classification decision. While each analysis method has its advantages (and disadvantages), both were able to predicte all of the observed phrenic discharge patterns based solely on the 3‐D coordinates of the microinjection sites. We suggest, however, that FLD analysis may be better suited for these data.Grant Funding Source: Supported by HL63175

Combination of multivariate curve resolution and multivariate classification techniques for comprehensive high-performance liquid chromatography-diode array absorbance detection fingerprints analysis of Salvia reuterana extracts

In this study, multivariate curve resolution (MCR) and multivariate classification methods are proposed to develop a new chemometric strategy for comprehensive analysis of high-performance liquid chromatography-diode array absorbance detection (HPLC-DAD) fingerprints of sixty Salvia reuterana samples from five different geographical regions. Different chromatographic problems occurred during HPLC-DAD analysis of S. reuterana samples, such as baseline/background contribution and noise, low signal-to-noise ratio (S/N), asymmetric peaks, elution time shifts, and peak overlap are handled using the proposed strategy. In this way, chromatographic fingerprints of sixty samples are properly segmented to ten common chromatographic regions using local rank analysis and then, the corresponding segments are column-wise augmented for subsequent MCR analysis. Extended multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure component profiles in each segment. In general, thirty-one chemical components were resolved using MCR-ALS in sixty S. reuterana samples and the lack of fit (LOF) values of MCR-ALS models were below 10.0% in all cases. Pure spectral profiles are considered for identification of chemical components by comparing their resolved spectra with the standard ones and twenty-four components out of thirty-one components were identified. Additionally, pure elution profiles are used to obtain relative concentrations of chemical components in different samples for multivariate classification analysis by principal component analysis (PCA) and k-nearest neighbors (kNN). Inspection of the PCA score plot (explaining 76.1% of variance accounted for three PCs) showed that S. reuterana samples belong to four clusters. The degree of class separation (DCS) which quantifies the distance separating clusters in relation to the scatter within each cluster is calculated for four clusters and it was in the range of 1.6–5.8. These results are then confirmed by kNN. In addition, according to the PCA loading plot and kNN dendrogram of thirty-one variables, five chemical constituents of luteolin-7-o-glucoside, salvianolic acid D, rosmarinic acid, lithospermic acid and trijuganone A are identified as the most important variables (i.e., chemical markers) for clusters discrimination. Finally, the effect of different chemical markers on samples differentiation is investigated using counter-propagation artificial neural network (CP-ANN) method. It is concluded that the proposed strategy can be successfully applied for comprehensive analysis of chromatographic fingerprints of complex natural samples.

Neuroanatomical correlates of Klinefelter syndrome studied in relation to the neuropsychological profile

Brain imaging in Klinefelter syndrome (47, XXY) (KS), a genetic disorder characterized by the presence of an extra X chromosome, may contribute to understanding the relationship between gene expression, brain structure, and subsequent cognitive disabilities and psychiatric disorders.We conducted the largest to date voxel-based morphometry study of 65 KS subjects and 65 controls matched for age and education and correlated these data to neuropsychological test scores. The KS patients had significantly smaller total brain volume (TBV), total gray matter volume (GMV) and total white matter volume (WMV) compared to controls, whereas no volumetric difference in cerebral spinal fluid (CSF) was found. There were no differences in TBV, GMV, WMV or CSF between testosterone treated KS (T-KS) and untreated KS (U-KS) patients. Compared to controls, KS patients had significantly decreased GMV bilaterally in insula, putamen, caudate, hippocampus, amygdala, temporal pole and frontal inferior orbita. Additionally, the right parahippocampal region and cerebellar volumes were reduced in KS patients. KS patients had significantly larger volumes in right postcentral gyrus, precuneus and parietal regions. Multivariate classification analysis discriminated KS patients from controls with 96.9% (p < 0.001) accuracy. Regression analyses, however, revealed no significant association between GMV differences and cognitive and psychological factors within the KS patients and controls or the groups combined. These results show that although gene dosage effect of having and extra X-chromosome may lead to large scale alterations of brain morphometry and extended cognitive disabilities no simple correspondence links these measures.

He Who Is Well Prepared Has Half Won The Battle: An fMRI Study of Task Preparation

The neural mechanism underlying preparation for tasks that vary in difficulty has not been explored. This functional magnetic resonance imaging study manipulated task difficulty by varying the working memory (WM) load of the n-back task. Each n-back task block was preceded by a preparation period involving a screen that indicated the level of difficulty of the upcoming task. Consistent with previous work, activation in some brain regions depended on WM load in the task. These regions were used as regions of interest for the univariate and multivariate (classification) analyses of preparation periods. The findings were that the patterns of brain activation during task preparation contain information about the upcoming task difficulty. (1) A support vector machine classifier was able to decode the n-back task difficulty from the patterns of brain activation during task preparation. Those individuals whose activation patterns for anticipated 1- versus 2- versus 3-back conditions were classified with higher accuracy showed better behavioral performance on the task, suggesting that task performance depends on task preparation. (2) Left inferior frontal gyrus, intraparietal sulcus, and anterior cingulate cortex parametrically decreased activation as anticipated task difficulty increased. Taken together, these results suggest dynamic involvement of the WM network not only during WM task performance, but also during task preparation.

Brain Organization Underlying Superior Mathematical Abilities in Children with Autism

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication deficits. While such deficits have been the focus of most research, recent evidence suggests that individuals with ASD may exhibit cognitive strengths in domains such as mathematics. Cognitive assessments and functional brain imaging were used to investigate mathematical abilities in 18 children with ASD and 18 age-, gender-, and IQ-matched typically developing (TD) children. Multivariate classification and regression analyses were used to investigate whether brain activity patterns during numerical problem solving were significantly different between the groups and predictive of individual mathematical abilities. Children with ASD showed better numerical problem solving abilities and relied on sophisticated decomposition strategies for single-digit addition problems more frequently than TD peers. Although children with ASD engaged similar brain areas as TD children, they showed different multivariate activation patterns related to arithmetic problem complexity in ventral temporal-occipital cortex, posterior parietal cortex, and medial temporal lobe. Furthermore, multivariate activation patterns in ventral temporal-occipital cortical areas typically associated with face processing predicted individual numerical problem solving abilities in children with ASD but not in TD children. Our study suggests that superior mathematical information processing in children with ASD is characterized by a unique pattern of brain organization and that cortical regions typically involved in perceptual expertise may be utilized in novel ways in ASD. Our findings of enhanced cognitive and neural resources for mathematics have critical implications for educational, professional, and social outcomes for individuals with this lifelong disorder.

Variations in floristic and palatability composition, yield of veld and of soil characteristics on a paddock scale

Five parallel 900-m line-transects were laid out 100 m apart in relatively homogeneous veld. Floristic composition, yields of dry matter, soil forms and series, effective depth, clay content of the top and subsoil, and the pH of the topsoil were determined along the transects. The floristic data were subjected to multivariate classification and ordination analyses, but no meaningful discontinuities were found. Patterns of spatial variation in palatability composition, yield and in the relevant soil characteristics were established. Taking yield and palatability composition as the primary determinants of grazing capacity, it seems from the present and previous studies that spatial variations in one or both of these parameters in local veld are commonly so great that accurate determination of patterns of distribution of grazing capacity within farms is an unrealistic objective. It is suggested that in farm planning only broad assessment of grazing capacity within relatively homogeneous veld should be aimed ...

Flipping a coin in your head without monitoring outcomes? Comments on predicting free choices and a demo program

Flipping a coin in your head without monitoring outcomes? Comments on predicting free choices and a demo program