Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain

Functional magnetic resonance data acquired in a task-absent condition (“resting state”) require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google''s PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular “betweenness centrality” - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level.     

A Sparse Representation-Based Algorithm for Pattern Localization in Brain Imaging Data Analysis

Considering the two-class classification problem in brain imaging data analysis, we propose a sparse representation-based multi-variate pattern analysis (MVPA) algorithm to localize brain activation patterns corresponding to different stimulus classes/brain states respectively. Feature selection can be modeled as a sparse representation (or sparse regression) problem. Such technique has been successfully applied to voxel selection in fMRI data analysis. However, single selection based on sparse representation or other methods is prone to obtain a subset of the most informative features rather than all. Herein, our proposed algorithm recursively eliminates informative features selected by a sparse regression method until the decoding accuracy based on the remaining features drops to a threshold close to chance level. In this way, the resultant feature set including all the identified features is expected to involve all the informative features for discrimination. According to the signs of the sparse regression weights, these selected features are separated into two sets corresponding to two stimulus classes/brain states. Next, in order to remove irrelevant/noisy features in the two selected feature sets, we perform a nonparametric permutation test at the individual subject level or the group level. In data analysis, we verified our algorithm with a toy data set and an intrinsic signal optical imaging data set. The results show that our algorithm has accurately localized two class-related patterns. As an application example, we used our algorithm on a functional magnetic resonance imaging (fMRI) data set. Two sets of informative voxels, corresponding to two semantic categories (i.e., “old people” and “young people”), respectively, are obtained in the human brain.     

Will the clinical development of 4th-generation “double mutant active” ALK TKIs (TPX-0131 and NVL-655) change the future treatment paradigm of ALK+ NSCLC?

Our current treatment paradigm of advanced anaplastic lymphoma kinase fusion (ALK+) non-small cell lung cancer (NSCLC) classifies the six currently approved ALK tyrosine kinase inhibitors (TKIs) into three generations. The 2nd-generation (2G) and 3rd-generation (3G) ALK TKIs are all “single mutant active” with varying potencies across a wide spectrum of acquired single ALK resistance mutations. There is a vigorous debate among clinicians which is the best upfront ALK TKI is for the first-line (1L) treatment of ALK+ NSCLC and the subsequent sequencing strategies whether it should be based on the presence of specific on-target ALK resistance mutations or not. Regardless, sequential use of “single mutant active” ALK TKIs will eventually lead to double ALK resistance mutations in cis. This has led to the creation of fourth generation (4G) “double mutant active” ALK TKIs such as TPX-0131 and NVL-655. We discuss the critical properties 4G ALK TKIs must possess to be clinically successful. We proposed conceptual first-line, second-line, and molecularly-based third-line registrational randomized clinical trials designed for these 4G ALK TKIs. How these 4G ALK TKIs would be used in the future will depend on which line of treatment the clinical trial design(s) is adopted provided the trial is positive. If approved, 4G ALK TKIs may usher in a new treatment paradigm for advanced ALK+ NSCLC that is based on classifying ALK TKIs based on the intrinsic functional capabilities (“singe mutant active” versus “double mutant active”) rather than the loosely-defined “generational” (first-, second-,third-,fourth-) classification and avoid the current clinical approaches of seemingly random sequential use of 2G and 3G ALK TKIs.     

The Role of Physical Activity in Harm Reduction among Betel Quid Chewers from a Prospective Cohort of 419,378 Individuals

ObjectiveTo assess the benefits of regular exercise in reducing harms associated with betel quid (BQ) chewing.MethodsThe study cohort, 419,378 individuals, participated in a medical screening program between 1994 and 2008, with 38,324 male and 1,495 female chewers, who consumed 5–15 quids of BQ a day. Physical activity of each individual, based on “MET-hour/week”, was classified as “inactive” or “active”, where activity started from a daily 15 minutes/day or more of brisk walking (≥3.75 MET-hour/week). Hazard ratios for mortality and remaining years in life expectancy were calculated.ResultsNearly one fifth (18.7%) of men, but only 0.7% of women were chewers. Chewers had a 10-fold increase in oral cancer risk; and a 2-3-fold increase in mortality from lung, esophagus and liver cancer, cardiovascular disease, and diabetes, with doubling of all-cause mortality. More than half of chewers were physically inactive (59%). Physical activity was beneficial for chewers, with a reduction of all-cause mortality by 19%. Inactive chewers had their lifespan shortened by 6.3 years, compared to non-chewers, but being active, chewers improved their health by gaining 2.5 years. The improvement, however, fell short of offsetting the harms from chewing.ConclusionsChewers had serious health consequences, but being physically active, chewers could mitigate some of these adverse effects, and extend life expectancy by 2.5 years and reduce mortality by one fifth. Encouraging exercise, in addition to quitting chewing, remains the best advice for 1.5 million chewers in Taiwan.     

The Association between the Use of Proton Pump Inhibitors and the Risk of Hypomagnesemia: A Systematic Review and Meta-Analysis

Background

Although many case reports have described patients with proton pump inhibitor (PPI)-induced hypomagnesemia, the impact of PPI use on hypomagnesemia has not been fully clarified through comparative studies. We aimed to evaluate the association between the use of PPI and the risk of developing hypomagnesemia by conducting a systematic review with meta-analysis.

Methods

We conducted a systematic search of MEDLINE, EMBASE, and the Cochrane Library using the primary keywords “proton pump,” “dexlansoprazole,” “esomeprazole,” “ilaprazole,” “lansoprazole,” “omeprazole,” “pantoprazole,” “rabeprazole,” “hypomagnesemia,” “hypomagnesaemia,” and “magnesium.” Studies were included if they evaluated the association between PPI use and hypomagnesemia and reported relative risks or odds ratios or provided data for their estimation. Pooled odds ratios with 95% confidence intervals were calculated using the random effects model. Statistical heterogeneity was assessed with Cochran’s Q test and I ² statistics.

Results

Nine studies including 115,455 patients were analyzed. The median Newcastle-Ottawa quality score for the included studies was seven (range, 6–9). Among patients taking PPIs, the median proportion of patients with hypomagnesemia was 27.1% (range, 11.3–55.2%) across all included studies. Among patients not taking PPIs, the median proportion of patients with hypomagnesemia was 18.4% (range, 4.3–52.7%). On meta-analysis, pooled odds ratio for PPI use was found to be 1.775 (95% confidence interval 1.077–2.924). Significant heterogeneity was identified using Cochran’s Q test (df = 7, P<0.001, I ² = 98.0%).

Conclusions

PPI use may increase the risk of hypomagnesemia. However, significant heterogeneity among the included studies prevented us from reaching a definitive conclusion.     

Structural Basis for Conformational Dynamics of GTP-bound Ras Protein

Ras family small GTPases assume two interconverting conformations, “inactive” state 1 and “active” state 2, in their GTP-bound forms. Here, to clarify the mechanism of state transition, we have carried out x-ray crystal structure analyses of a series of mutant H-Ras and M-Ras in complex with guanosine 5′-(β,γ-imido)triphosphate (GppNHp), representing various intermediate states of the transition. Crystallization of H-RasT35S-GppNHp enables us to solve the first complete tertiary structure of H-Ras state 1 possessing two surface pockets unseen in the state 2 or H-Ras-GDP structure. Moreover, determination of the two distinct crystal structures of H-RasT35S-GppNHp, showing prominent polysterism in the switch I and switch II regions, reveals a pivotal role of the guanine nucleotide-mediated interaction between the two switch regions and its rearrangement by a nucleotide positional change in the state 2 to state 1 transition. Furthermore, the ³¹P NMR spectra and crystal structures of the GppNHp-bound forms of M-Ras mutants, carrying various H-Ras-type amino acid substitutions, also reveal the existence of a surface pocket in state 1 and support a similar mechanism based on the nucleotide-mediated interaction and its rearrangement in the state 1 to state 2 transition. Intriguingly, the conformational changes accompanying the state transition mimic those that occurred upon GDP/GTP exchange, indicating a common mechanistic basis inherent in the high flexibility of the switch regions. Collectively, these results clarify the structural features distinguishing the two states and provide new insights into the molecular basis for the state transition of Ras protein.     

Never Resting Brain: Simultaneous Representation of Two Alpha Related Processes in Humans

Brain activity is continuously modulated, even at “rest”. The alpha rhythm (8–12 Hz) has been known as the hallmark of the brain''s idle-state. However, it is still debated if the alpha rhythm reflects synchronization in a distributed network or focal generator and whether it occurs spontaneously or is driven by a stimulus. This EEG/fMRI study aimed to explore the source of alpha modulations and their distribution in the resting brain. By serendipity, while computing the individually defined power modulations of the alpha-band, two simultaneously occurring components of these modulations were found. An ‘induced alpha’ that was correlated with the paradigm (eyes open/ eyes closed), and a ‘spontaneous alpha’ that was on-going and unrelated to the paradigm. These alpha components when used as regressors for BOLD activation revealed two segregated activation maps: the ‘induced map’ included left lateral temporal cortical regions and the hippocampus; the ‘spontaneous map’ included prefrontal cortical regions and the thalamus. Our combined fMRI/EEG approach allowed to computationally untangle two parallel patterns of alpha modulations and underpin their anatomical basis in the human brain. These findings suggest that the human alpha rhythm represents at least two simultaneously occurring processes which characterize the ‘resting brain’; one is related to expected change in sensory information, while the other is endogenous and independent of stimulus change.     

Neural Processes Underlying the“Same”-“Different” Judgment of Two Simultaneously Presented Objects- An EEG Study

The present study investigated the neural processes underlying “same” and -“different” judgments for two simultaneously presented objects, that varied on one or both, of two dimensions: color and shape. Participants judged whether or not the two objects were “same” or “different” on either the color dimension (color task) or the shape dimension (shape task). The unattended irrelevant dimension of the objects was either congruent (same-same; different-different) or incongruent (same-different). ERP data showed a main effect of color congruency in the time window 190–260 ms post-stimulus presentation and a main effect of shape congruency in the time window 220–280 ms post-stimulus presentation in both color and shape tasks. The interaction between color and shape congruency in the ERP data occurred in a later time window than the two main effects, indicating that mismatches in task-relevant and task-irrelevant dimensions were processed automatically and independently before a response was selected. The fact that the interference of the task-irrelevant dimension occurred after mismatch detection, supports a confluence model of processing.     

An Antidote to the Imager's Fallacy,or How to Identify Brain Areas That Are in Limbo

Traditionally, fMRI data are analyzed using statistical parametric mapping approaches. Regardless of the precise thresholding procedure, these approaches ultimately divide the brain in regions that do or do not differ significantly across experimental conditions. This binary classification scheme fosters the so-called imager''s fallacy, where researchers prematurely conclude that region A is selectively involved in a certain cognitive task because activity in that region reaches statistical significance and activity in region B does not. For such a conclusion to be statistically valid, however, a test on the differences in activation across these two regions is required. Here we propose a simple GLM-based method that defines an “in-between” category of brain regions that are neither significantly active nor inactive, but rather “in limbo”. For regions that are in limbo, the activation pattern is inconclusive: it does not differ significantly from baseline, but neither does it differ significantly from regions that do show significant changes from baseline. This pattern indicates that measurement was insufficiently precise. By directly testing differences in activation, our procedure helps reduce the impact of the imager''s fallacy. The method is illustrated using concrete examples.     

Social Distance Evaluation in Human Parietal Cortex

Across cultures, social relationships are often thought of, described, and acted out in terms of physical space (e.g. “close friends” “high lord”). Does this cognitive mapping of social concepts arise from shared brain resources for processing social and physical relationships? Using fMRI, we found that the tasks of evaluating social compatibility and of evaluating physical distances engage a common brain substrate in the parietal cortex. The present study shows the possibility of an analytic brain mechanism to process and represent complex networks of social relationships. Given parietal cortex''s known role in constructing egocentric maps of physical space, our present findings may help to explain the linguistic, psychological and behavioural links between social and physical space.     

Plant intelligence: Why,why not or where?

The concept of plant intelligence, as proposed by Anthony Trewavas, has raised considerable discussion. However, plant intelligence remains loosely defined; often it is either perceived as practically synonymous to Darwinian fitness, or reduced to a mere decorative metaphor. A more strict view can be taken, emphasizing necessary prerequisites such as memory and learning, which requires clarifying the definition of memory itself. To qualify as memories, traces of past events have to be not only stored, but also actively accessed. We propose a criterion for eliminating false candidates of possible plant intelligence phenomena in this stricter sense: an “intelligent” behavior must involve a component that can be approximated by a plausible algorithmic model involving recourse to stored information about past states of the individual or its environment. Re-evaluation of previously presented examples of plant intelligence shows that only some of them pass our test.

“You were hurt?” Kumiko said, looking at the scar.Sally looked down. “Yeah.”“Why didn''t you have it removed?”“Sometimes it''s good to remember.”“Being hurt?”“Being stupid.”—(W. Gibson: Mona Lisa Overdrive)

Key words: intelligence, memory, learning, plant development, mathematical models, plant neurobiology, definition of terms     

Intrinsic connectivity reveals functionally distinct cortico-hippocampal networks in the human brain

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. Here, using data-driven analyses of resting-state functional magnetic resonance imaging (fMRI) data, we identified the networks that interact with the hippocampus—the default mode network (DMN) and a “medial temporal network” (MTN) that included regions in the medial temporal lobe (MTL) and precuneus. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into 3 subnetworks: a “posterior medial” (PM) subnetwork comprised of posterior cingulate and lateral parietal cortices; an “anterior temporal” (AT) subnetwork comprised of regions in the temporopolar and dorsomedial prefrontal cortex; and a “medial prefrontal” (MP) subnetwork comprised of regions primarily in the medial prefrontal cortex (mPFC). These networks vary in their functional connectivity (FC) along the hippocampal long axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory.

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. This study uses network analyses of intrinsic brain networks at rest to identify and characterize brain networks that interact with the hippocampus and have distinct functions during memory-guided decision making.     

Extracting Multiscale Pattern Information of fMRI Based Functional Brain Connectivity with Application on Classification of Autism Spectrum Disorders

We employed a multi-scale clustering methodology known as “data cloud geometry” to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI) protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs) in 29 individuals with autism spectrum disorders (ASD), and 29 individuals with typical development (TD) while they completed a cognitive control task. Connectivity clustering geometry was examined at both “fine” and “coarse” scales. At the coarse scale, the connectivity clustering geometry produced 10 valid clusters with a coherent relationship to neural anatomy. A supervised learning algorithm employed fine scale information about clustering motif configurations and prevalence, and coarse scale information about intra- and inter-regional connectivity; the algorithm correctly classified ASD and TD participants with sensitivity of and specificity of . Most of the predictive power of the logistic regression model resided at the level of the fine-scale clustering geometry, suggesting that cellular versus systems level disturbances are more prominent in individuals with ASD. This article provides validation for this multi-scale geometric approach to extracting brain functional connectivity pattern information and for its use in classification of ASD.     

Color Discrimination in the Tufted Capuchin Monkey,Sapajus spp

The present study evaluated the efficacy of an adapted version of the Mollon-Reffin test for the behavioral investigation of color vision in capuchin monkeys. Ten tufted capuchin monkeys (Sapajus spp., formerly referred to as Cebus apella) had their DNA analyzed and were characterized as the following: one trichromat female, seven deuteranope dichromats (six males and one female), and two protanope males, one of which was identified as an “ML protanope.” For their behavioral characterization, all of the subjects were tested at three regions of the Commission International de l''Eclairage (CIE) 1976 u′v′ diagram, with each test consisting of 20 chromatic variation vectors that were radially distributed around the chromaticity point set as the test background. The phenotypes inferred from the behavioral data were in complete agreement with those predicted from the genetic analysis, with the threshold distribution clearly differentiating between trichromats and dichromats and the estimated confusion lines characteristically converging for deuteranopes and the “classic” protanope. The discrimination pattern of the ML protanope was intermediate between protan and deutan, with confusion lines horizontally oriented and parallel to each other. The observed phenotypic differentiation confirmed the efficacy of the Mollon-Reffin test paradigm as a useful tool for evaluating color discrimination in nonhuman primates. Especially noteworthy was the demonstration of behavioral segregation between the “classic” and “ML” protanopes, suggesting identifiable behavioral consequences of even slight variations in the spectral sensitivity of M/L photopigments in dichromats.     

Learning brain dynamics for decoding and predicting individual differences

Insights from functional Magnetic Resonance Imaging (fMRI), as well as recordings of large numbers of neurons, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of brain signals. To decode brain dynamics, we propose an architecture based on recurrent neural networks to uncover distributed spatiotemporal signatures. We demonstrate the potential of the approach using human fMRI data during movie-watching data and a continuous experimental paradigm. The model was able to learn spatiotemporal patterns that supported 15-way movie-clip classification (∼90%) at the level of brain regions, and binary classification of experimental conditions (∼60%) at the level of voxels. The model was also able to learn individual differences in measures of fluid intelligence and verbal IQ at levels comparable to that of existing techniques. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational (i.e., classification related) properties of brain dynamics. Finally, saliency maps and lesion analysis were employed to characterize brain-region/voxel importance, and uncovered how dynamic but consistent changes in fMRI activation influenced decoding performance. When applied at the level of voxels, our framework implements a dynamic version of multivariate pattern analysis. Our approach provides a framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.     

Hippocampus Is Place of Interaction between Unconscious and Conscious Memories

Recent evidence suggests that humans can form and later retrieve new semantic relations unconsciously by way of hippocampus—the key structure also recruited for conscious relational (episodic) memory. If the hippocampus subserves both conscious and unconscious relational encoding/retrieval, one would expect the hippocampus to be place of unconscious-conscious interactions during memory retrieval. We tested this hypothesis in an fMRI experiment probing the interaction between the unconscious and conscious retrieval of face-associated information. For the establishment of unconscious relational memories, we presented subliminal (masked) combinations of unfamiliar faces and written occupations (“actor” or “politician”). At test, we presented the former subliminal faces, but now supraliminally, as cues for the reactivation of the unconsciously associated occupations. We hypothesized that unconscious reactivation of the associated occupation—actor or politician—would facilitate or inhibit the subsequent conscious retrieval of a celebrity’s occupation, which was also actor or politician. Depending on whether the reactivated unconscious occupation was congruent or incongruent to the celebrity’s occupation, we expected either quicker or delayed conscious retrieval process. Conscious retrieval was quicker in the congruent relative to a neutral baseline condition but not delayed in the incongruent condition. fMRI data collected during subliminal face-occupation encoding confirmed previous evidence that the hippocampus was interacting with neocortical storage sites of semantic knowledge to support relational encoding. fMRI data collected at test revealed that the facilitated conscious retrieval was paralleled by deactivations in the hippocampus and neocortical storage sites of semantic knowledge. We assume that the unconscious reactivation has pre-activated overlapping relational representations in the hippocampus reducing the neural effort for conscious retrieval. This finding supports the notion of synergistic interactions between conscious and unconscious relational memories in a common, cohesive hippocampal-neocortical memory space.     

Genome Wide Association Study Identifies New Loci Associated with Undesired Coat Color Phenotypes in Saanen Goats

This paper reports a quantitative genetics and genomic analysis of undesirable coat color patterns in goats. Two undesirable coat colors have routinely been recorded for the past 15 years in French Saanen goats. One fifth of Saanen females have been phenotyped “pink” (8.0%) or “pink neck” (11.5%) and consequently have not been included in the breeding program as elite animals. Heritability of the binary “pink” and “pink neck” phenotype, estimated from 103,443 females was 0.26 for “pink” and 0.21 for “pink neck”. Genome wide association studies (using haplotypes or single SNPs) were implemented using a daughter design of 810 Saanen goats sired by 9 Artificial Insemination bucks genotyped with the goatSNP50 chip. A highly significant signal (-log10pvalue = 10.2) was associated with the “pink neck” phenotype on chromosome 11, suggesting the presence of a major gene. Highly significant signals for the “pink” phenotype were found on chromosomes 5 and 13 (-log10p values of 7.2 and, 7.7 respectively). The most significant SNP on chromosome 13 was in the ASIP gene region, well known for its association with coat color phenotypes. Nine significant signals were also found for both traits. The highest signal for each trait was detected by both single SNP and haplotype approaches, whereas the smaller signals were not consistently detected by the two methods. Altogether these results demonstrated a strong genetic control of the “pink” and “pink neck” phenotypes in French Saanen goats suggesting that SNP information could be used to identify and remove undesired colored animals from the breeding program.     

Higher Landing Accuracy in Expert Pilots is Associated with Lower Activity in the Caudate Nucleus

The most common lethal accidents in General Aviation are caused by improperly executed landing approaches in which a pilot descends below the minimum safe altitude without proper visual references. To understand how expertise might reduce such erroneous decision-making, we examined relevant neural processes in pilots performing a simulated landing approach inside a functional MRI scanner. Pilots (aged 20–66) were asked to “fly” a series of simulated “cockpit view” instrument landing scenarios in an MRI scanner. The scenarios were either high risk (heavy fog–legally unsafe to land) or low risk (medium fog–legally safe to land). Pilots with one of two levels of expertise participated: Moderate Expertise (Instrument Flight Rules pilots, n = 8) or High Expertise (Certified Instrument Flight Instructors or Air-Transport Pilots, n = 12). High Expertise pilots were more accurate than Moderate Expertise pilots in making a “land” versus “do not land” decision (CFII: d′ = 3.62±2.52; IFR: d′ = 0.98±1.04; p<.01). Brain activity in bilateral caudate nucleus was examined for main effects of expertise during a “land” versus “do not land” decision with the no-decision control condition modeled as baseline. In making landing decisions, High Expertise pilots showed lower activation in the bilateral caudate nucleus (0.97±0.80) compared to Moderate Expertise pilots (1.91±1.16) (p<.05). These findings provide evidence for increased “neural efficiency” in High Expertise pilots relative to Moderate Expertise pilots. During an instrument approach the pilot is engaged in detailed examination of flight instruments while monitoring certain visual references for making landing decisions. The caudate nucleus regulates saccade eye control of gaze, the brain area where the “expertise” effect was observed. These data provide evidence that performing “real world” aviation tasks in an fMRI provide objective data regarding the relative expertise of pilots and brain regions involved in it.     

Smoothness without Smoothing: Why Gaussian Naive Bayes Is Not Naive for Multi-Subject Searchlight Studies

Spatial smoothness is helpful when averaging fMRI signals across multiple subjects, as it allows different subjects'' corresponding brain areas to be pooled together even if they are slightly misaligned. However, smoothing is usually not applied when performing multivoxel pattern-based analyses (MVPA), as it runs the risk of blurring away the information that fine-grained spatial patterns contain. It would therefore be desirable, if possible, to carry out pattern-based analyses which take unsmoothed data as their input but which produce smooth images as output. We show here that the Gaussian Naive Bayes (GNB) classifier does precisely this, when it is used in “searchlight” pattern-based analyses. We explain why this occurs, and illustrate the effect in real fMRI data. Moreover, we show that analyses using GNBs produce results at the multi-subject level which are statistically robust, neurally plausible, and which replicate across two independent data sets. By contrast, SVM classifiers applied to the same data do not generate a replication, even if the SVM-derived searchlight maps have smoothing applied to them. An additional advantage of GNB classifiers for searchlight analyses is that they are orders of magnitude faster to compute than more complex alternatives such as SVMs. Collectively, these results suggest that Gaussian Naive Bayes classifiers may be a highly non-naive choice for multi-subject pattern-based fMRI studies.     

Niche theory‐based modeling of assembly processes of viral communities in bats

Understanding the assembly processes of symbiont communities, including viromes and microbiomes, is important for improving predictions on symbionts’ biogeography and disease ecology. Here, we use phylogenetic, functional, and geographic filters to predict the similarity between symbiont communities, using as a test case the assembly process in viral communities of Mexican bats. We construct generalized linear models to predict viral community similarity, as measured by the Jaccard index, as a function of differences in host phylogeny, host functionality, and spatial co‐occurrence, evaluating the models using the Akaike information criterion. Two model classes are constructed: a “known” model, where virus–host relationships are based only on data reported in Mexico, and a “potential” model, where viral reports of all the Americas are used, but then applied only to bat species that are distributed in Mexico. Although the “known” model shows only weak dependence on any of the filters, the “potential” model highlights the importance of all three filter types—phylogeny, functional traits, and co‐occurrence—in the assemblage of viral communities. The differences between the “known” and “potential” models highlight the utility of modeling at different “scales” so as to compare and contrast known information at one scale to another one, where, for example, virus information associated with bats is much scarcer.