Robust outlier detection by de-biasing VAE likelihoods

Deep networks often make confident, yet, incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep generative models (DGMs) are a candidate metric for outlier detection with unlabeled data. Yet, previous studies have shown that DGM likelihoods are unreliable and can be easily biased by simple transformations to input data. Here, we examine outlier detection with variational autoencoders (VAEs), among the simplest of DGMs. We propose novel analytical and algorithmic approaches to ameliorate key biases with VAE likelihoods. Our bias corrections are sample-specific, computationally inexpensive, and readily computed for various decoder visible distributions. Next, we show that a well-known image pre-processing technique -- contrast stretching -- extends the effectiveness of bias correction to further improve outlier detection. Our approach achieves state-of-the-art accuracies with nine grayscale and natural image datasets, and demonstrates significant advantages -- both with speed and performance -- over four recent, competing approaches. In summary, lightweight remedies suffice to achieve robust outlier detection with VAEs.Comment: To appear at CVPR 2022. 20 pages and 19 figure

A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks.

Cognitively demanding tasks that evoke activation in the brain's central-executive network (CEN) have been consistently shown to evoke decreased activation (deactivation) in the default-mode network (DMN). The neural mechanisms underlying this switch between activation and deactivation of large-scale brain networks remain completely unknown. Here, we use functional magnetic resonance imaging (fMRI) to investigate the mechanisms underlying switching of brain networks in three different experiments. We first examined this switching process in an auditory event segmentation task. We observed significant activation of the CEN and deactivation of the DMN, along with activation of a third network comprising the right fronto-insular cortex (rFIC) and anterior cingulate cortex (ACC), when participants perceived salient auditory event boundaries. Using chronometric techniques and Granger causality analysis, we show that the rFIC-ACC network, and the rFIC, in particular, plays a critical and causal role in switching between the CEN and the DMN. We replicated this causal connectivity pattern in two additional experiments: (i) a visual attention ''oddball'' task and (ii) a task-free resting state. These results indicate that the rFIC is likely to play a major role in switching between distinct brain networks across task paradigms and stimulus modalities. Our findings have important implications for a unified view of network mechanisms underlying both exogenous and endogenous cognitive control. brain networks ͉ cognitive control ͉ insula ͉ attention ͉ prefrontal cortex O ne distinguishing feature of the human brain, compared with brains lower on the phylogenetic ladder, is the amount of cognitive control available for selecting, switching, and attending to salient events in the environment. Recent research suggests that the human brain is intrinsically organized into distinct functional networks that support these processes (1-4). Analysis of resting-state functional connectivity, using both model-based and model-free approaches, has suggested the existence of at least three canonical networks: (i) a centralexecutive network (CEN), whose key nodes include the dorsolateral prefrontal cortex (DLPFC), and posterior parietal cortex (PPC); (ii) the default-mode network (DMN), which includes the ventromedial prefrontal cortex (VMPFC) and posterior cingulate cortex (PCC); and (iii) a salience network (SN), which includes the ventrolateral prefrontal cortex (VLPFC) and anterior insula (jointly referred to as the fronto-insular cortex; FIC) and the anterior cingulate cortex (ACC) In a recent meta-analysis, Dosenbach and colleagues hypothesized that several brain regions that overlap with the CEN and SN are important for multiple cognitive control functions, including initiation, maintenance, and adjustment of attention (7). However, no studies to date have directly assessed the temporal dynamics and causal interactions of specific nodes within the CEN, SN, and DMN. Converging evidence from a number of brain imaging studies across several task domains suggests that the FIC and ACC nodes of the SN, in particular, respond to the degree of subjective salience, whether cognitive, homeostatic, or emotional We used three functional magnetic resonance imaging (fMRI) experiments to examine the interaction between the SN, CEN, and DMN, with particular interest in the role of the FIC/ACC in regulating these networks. In the first experiment, we scanned 18 participants as they listened with focused attention to classical music symphonies inside the scanner. We analyzed brain responses during the occurrence of ''movement transitions:'' salient, orienting events arising from transitions between adjacent ''movements'' in the music (19). To specifically elucidate the role of the FIC in driving network changes, we used chronometry and Granger Causality Analysis (GCA), to provide information about the dynamics and directionality of signaling in cortical circuits In the second experiment, we investigated the generality of network switching mechanisms involving the FIC by examining brain responses elicited during a visual "oddball" attention task (23). A third experiment examined whether the network switching mechanism could be observed during task-free resting state where there was no overt task and no behavioral response (4). Our motivation for examining the resting-state fMRI data was the recent finding, based on computer simulation of large-scale brain networks, that even in the absence of external stimuli, certain nodes can regulate other nodes and function as hubs (24). NEUROSCIENCE Our aim was to test the hypothesis that common network switching mechanisms apply across tasks with varying cognitive demands and differing stimulus modalities. If confirmed, our findings would provide insights into fundamental control mechanisms in the human brain. Results We describe findings from Experiment 1 in the first three sections. Convergent findings from Experiments 2 and 3 are described subsequently. Activation of CEN and SN, and Deactivation of DMN During Auditory Event Segmentation. As reported previously (19), we found robust right-lateralized activation in the DLPFC, PPC, and FIC during ''movement transitions'' in the auditory event segmentation task. Here, we extend these findings to characterize network-specific responses in the CEN, DMN, and SN. Activations in the CEN and SN were found to be accompanied by robust deactivation in the DMN at the movement transition [ Latency Analysis Reveals Early Activation of the rFIC Relative to the CEN and DMN. First, we identified differences in the latency of the event-related fMRI responses across the entire brain using the method developed by Henson and colleagues (26). Briefly, this method provides a way to estimate the peak latency of the BOLD response at each voxel using the ratio of the derivative to canonical parameter estimates (see SI Materials and Methods for details). This analysis revealed that the event-related fMRI signal in the right FIC (rFIC) and ACC peaks earlier compared to the signal in the nodes of the CEN and DMN, indicating that the neural responses in the rFIC and ACC precede the CEN and DMN (see GCA Reveals that the rFIC Is a Causal Outflow Hub at the Junction of the CEN and DMN. Finally, to elucidate the dynamic interactions between the three networks we applied GCA. Briefly, GCA detects causal interactions between brain regions by assessing the Activations height and extent thresholded at the P Ͻ 0.001 level (uncorrected). The ICA prunes out extraneous activation and deactivation clusters visible in the GLM analysis to reveal brain regions that constitute independent and tightly coupled networks. Fig. 3. Granger causality analysis (GCA) of the six key nodes of the Salience (blue nodes), Central-Executive (green nodes) and Default-Mode (yellow nodes) networks during (A) auditory event segmentation, (B) visual oddball attention task, and (C) task-free resting state. GCA revealed significant causal outflow from the rFIC across tasks and stimulus modalities. In each subfigure, the thickness of the connecting arrows between two regions corresponds to the strength of directed connection (F-value) normalized by the maximum F-value between any pair of regions for that task (''raw'' F-values reported in NEUROSCIENCE shorter path length than all of the other regions except the VMPFC (t test, P Ͻ 0.05); however, these differences did not remain significant after multiple comparison correction (data not shown). These results suggest that the rFIC is an outflow hub at the junction of the CEN and DMN. Converging Evidence from Two Additional fMRI Experiments. To provide converging evidence for the rFIC as a causal outflow hub, we analyzed fMRI data from two other experiments using the same GCA and network analyses methods described above: (i) a visual ''oddball'' attention experiment, and (ii) a task-free resting state experiment (see also SI Materials and Methods). We found a pattern of significant causal outflow from the rFIC that was strikingly similar to the auditory event segmentation experiment ( Discussion ICA revealed the existence of statistically independent CEN, DMN, and SN during task performance, extending our recent discovery of similar networks in task-free, resting-state, conditions (4). Our analysis indicates that the rFIC, a key node of the SN, plays a critical and causal role in switching between the CEN and the DMN (we use the term ''causal'' here, and in the following sections in the sense implied by, and consistent with, latency analysis, GCA and network analysis). The striking similarity of significant causal outflow from the rFIC across tasks, involving different stimulus modalities, indicates a general role for the rFIC in switching between two key brain networks. Furthermore, our replication of this effect in the task-free resting state suggests that the rFIC is a network hub that can also initiate spontaneous switching between the CEN and DMN (24). Our findings help to provide a more unified perspective on exogenous and endogenous mechanisms underlying cognitive control. In the SI Discussion, we suggest that these interactions are the result of neural, rather than vascular processes. Here, we focus on the neurobiological implications of our findings in the context of the three networks that we set out to examine; analyses of several other control regions (including the sensory and association cortices) that further clarify the crucial role of the FIC in the switching process are discussed in the SI Text. FIC-ACC Network Is Neuroanatomically Uniquely Positioned to Gen- erate Control Signals. In primates, anatomical studies have revealed that the insular cortex is reciprocally connected to multiple sensory, motor, limbic, and association areas of the brain (30, 31). The FIC and ACC themselves share significant topographic reciprocal connectivity and form an anatomically tightly coupled network ideally placed to integrate information from several brain regions (9, 10, 32). Indeed, analysis of the auditory and visual experiments in our study found coactivation of these regions during task performance, as in many other studies involving cognitively demanding tasks (7). Previous neurophysiological and brain imaging studies have shown that the FIC-ACC complex moderates arousal during cognitively demanding tasks and that the rFIC, in particular, plays a critical role in the interoceptive awareness of both stimulus-induced and stimulus-independent changes in homeostatic states (9, 10). Furthermore, the FIC and ACC share a unique feature at the neuronal level: The human FIC-ACC network has a specialized class of neurons with distinctive anatomical and functional features that might facilitate the network switching process that we report here. The von Economo neurons (VENs) are specialized neurons exclusively localized to the FIC and ACC (33). Based on the dendritic architecture of the VENs, Allman and colleagues have proposed that ''the function of the VENs may be to provide a rapid relay to other parts of the brain of a simple signal derived from information processed within FI and ACC.'' (34). We propose that the VENs may, therefore, constitute the neuronal basis of control signals generated by the FIC and ACC in our study. Taken together, these findings suggest that the FIC and ACC, anchored within the SN, are uniquely positioned to initiate control signals that activate the CEN and deactivate the DMN. Differential Roles of the rFIC, ACC, and Lateral Prefrontal Cortex in Initiating Control Signals. Many previous studies of attentional and cognitive control have reported coactivation of the FIC and Comparison of the net causal outflow (out-in degree) for the six key nodes of the Salience, Central-Executive, and Default-Mode networks as assessed by Granger causality analysis revealed that the rFIC has a significantly higher net causal outflow than the CEN and DMN regions across tasks (conventions as in 12572 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0800005105 Sridharan et al. ACC (7, Our findings help to synthesize these and other extant findings in the literature into a common network dynamical framework and they suggest a causal, and potentially critical, role for the rFIC in cognitive control. We propose that one fundamental mechanism underlying such control is a transient signal from the rFIC, which engages the brain's attentional, working memory and higher-order control processes while disengaging other systems that are not task-relevant. We predict that disruptions to these processes may constitute a key aspect of psychopathology in several neurological and psychiatric disorders, including frontotemporal dementia, autism, and anxiety disorders (34, 50, 51). More generally, our study illustrates the power of a unified network approach-wherein we first specify intrinsic brain networks and then analyze interactions among anatomically discrete regions within these networks during cognitive information processing-for understanding fundamental aspects of human brain function and dysfunction

Spectrin-based skeleton as an actor in cell signaling

This review focuses on the recent advances in functions of spectrins in non-erythroid cells. We discuss new data concerning the commonly known role of the spectrin-based skeleton in control of membrane organization, stability and shape, and tethering protein mosaics to the cellular motors and to all major filament systems. Particular effort has been undertaken to highlight recent advances linking spectrin to cell signaling phenomena and its participation in signal transduction pathways in many cell types

Subcortical connectivity correlates selectively with attention's effects on spatial choice bias

Neural mechanisms of attention are extensively studied in the neocortex; comparatively little is known about how subcortical regions contribute to attention. The superior colliculus (SC) is an evolutionarily conserved, subcortical (midbrain) structure that has been implicated in controlling visuospatial attention. Yet how the SC contributes mechanistically to attention remains unknown. We investigated the role of the SC in attention, combining model-based psychophysics, diffusion imaging, and tractography in human participants. Specifically, we asked whether the SC contributes to enhancing sensitivity (d') to attended information, or whether it contributes to biasing choices (criteria) in favor of attended information. We tested human participants on a multialternative change detection task, with endogenous spatial cueing, and quantified sensitivity and bias with a recently developed multidimensional signal detection model (m-ADC model). At baseline, sensitivity and bias exhibited complementary patterns of asymmetries across the visual hemifields: While sensitivity was consistently higher for detecting changes in the left hemifield, bias was higher for reporting changes in the right hemifield. Remarkably, white matter connectivity of the SC with the neocortex mirrored this pattern of asymmetries. Specifically, the asymmetry in SC-cortex connectivity correlated with the asymmetry in choice bias, but not in sensitivity. In addition, SC-cortex connectivity strength could predict cueing-induced modulation of bias, but not of sensitivity, across individuals. In summary, the SC may be a key node in an evolutionarily conserved network for controlling choice bias during visuospatial attention

Control analyses for temporal order reversal effects.

(A) Same as in Fig 3C (main text), but showing recency bias at the Saccade Toward and Saccade Away locations, following exclusion of trials in which the saccade onset occurred (B) Same as in panel A, but showing primacy bias at the Saccade Toward and Saccade Away locations. (A, B) Other conventions are the same as in Fig 3C. Data are available at https://dx.doi.org/10.6084/m9.figshare.21792002. (TIF)</p

Model predictions of behavioral responses in the change detection task.

(A) Same as in Fig 5E (main text), but showing response proportions predicted with the baseline model (y-axis) versus observed (true) response proportions (x-axis) in the change detection task. (B) Same as in Fig 5E (main text), but showing response proportions predicted with the perceptual bias model (y-axis) versus observed (true) response proportions (x-axis) in the change detection task. (A, B) Other conventions are the same as in Fig 5E. Data are available at https://dx.doi.org/10.6084/m9.figshare.21792002. (TIF)</p

Perceptual and choice biases contribute to presaccadic selection’s effects on orientation change detection.

(A) Schematic of the variable precision model for a change detection and localization task. (Left box) Internal measurements of the initial (θi) and final (θf) Gabor stimuli are represented by latent vectors (x and y, respectively) that follow von Mises distributions, with concentration parameters, κ. The encoding precision (J, directly related to κ) is variable across items and trials. (Middle box) F(l), the decision variable at each location, l, is proportion to the posterior odds ratio of probability of change (C) at that location to the probability of no change (N). The Bayesian ideal observer localizes the change to the location at which the decision variable F(l) exceeds the decision threshold ηl by the highest margin. If the decision variable does not exceed the decision threshold at any location, the observer reports “no change.” Perceptual bias model: The “baseline” VP model was modified to incorporate a “perceptual bias,” i.e., an attractive recency bias parameter, B, at the ST location (red text). Choice bias model: The “baseline” VP model was modified to incorporate unequal decision threshold parameters, ηl, at the ST and SA locations (green text). Both biases model: Both perceptual bias and unequal decision threshold parameters were incorporated into the VP model (blue text). (B) (Left) Model comparison with the AICc, for the 4 VP models. Gray/black: baseline model, red: perceptual bias model, green: choice bias model, blue: both biases model. Box-and-whisker plot conventions are the same as in Fig 1E. (Right) Same as in the left panel, but showing the cross-validated data likelihoods for the 4 models. Other conventions are the same as in the left panel. (C) Same as in Fig 1E, but showing the decision threshold, η, estimated for the ST and SA locations with the “both biases” model. Other conventions are the same as in Fig 1E. (D) Response proportions predicted with the both biases model (y-axis) versus observed (true) response proportions (x-axis) in the change detection task. Circles: hits, upward triangles: FAs, downward triangles: misses, squares: correct rejections (CRs). Other conventions are the same as in panel C. Data are available at https://dx.doi.org/10.6084/m9.figshare.21792002. AICc, corrected Akaike information criterion; CR, correct rejection; CV, cross-validated; FA, false alarm; SA, Saccade Away; ST, Saccade Toward; VP, variable precision.</p

Data and Analysis Scripts

Contains data and analysis scripts to produce figures presented in paper