Measurement of Functional Brain Network Connectivity in People with Orthostatic Tremor.

Orthostatic tremor is a rare movement disorder characterized by a sensation of unsteadiness and leg tremor while standing. It has been hypothesized that the disorder is attributable to dysregulation of a central oscillatory network in the brain. This putative network includes primary motor cortex, supplementary motor area, cerebellum, thalamus, and pontine tegmentum. We studied this brain network by recording resting-state functional MRI data from individuals with orthostatic tremor. For each participant, we measured resting-state functional connectivity using a seed-based approach. Regions of interest included were components of the putative central oscillatory network and a primary motor thumb region (identified via transcranial magnetic stimulation). A non-central oscillatory network region of interest-posterior cingulate cortex-was included for comparative analysis of a well-characterized intrinsic network, the default mode network. Demographic information, medical history, and tremor characteristics were collected to test associations with functional connectivity. For normative context, data from the 1000 Functional Connectomes Project were analyzed using an identical approach. We observed that tremor and demographic variables were correlated with functional connectivity of central oscillatory network components. Furthermore, relative to healthy comparison participants, patients with orthostatic tremor exhibited qualitatively different patterns of cerebellar resting state functional connectivity. Our study enhances the current understanding of brain network differences related to orthostatic tremor and is consistent with a hypothesized selective decoupling of cerebellum. Additionally, associations observed between functional connectivity and factors including medical history and tremor features may suggest targets for treatment of orthostatic tremor.

The functional role of spatial anisotropies in ensemble perception.

BACKGROUND: The human brain can rapidly represent sets of similar stimuli by their ensemble summary statistics, like the average orientation or size. Classic models assume that ensemble statistics are computed by integrating all elements with equal weight. Challenging this view, here, we show that ensemble statistics are estimated by combining parafoveal and foveal statistics in proportion to their reliability. In a series of experiments, observers reproduced the average orientation of an ensemble of stimuli under varying levels of visual uncertainty. RESULTS: Ensemble statistics were affected by multiple spatial biases, in particular, a strong and persistent bias towards the center of the visual field. This bias, evident in the majority of subjects and in all experiments, scaled with uncertainty: the higher the uncertainty in the ensemble statistics, the larger the bias towards the element shown at the fovea. CONCLUSION: Our findings indicate that ensemble perception cannot be explained by simple uniform pooling. The visual system weights information anisotropically from both the parafovea and the fovea, taking the intrinsic spatial anisotropies of vision into account to compensate for visual uncertainty.

Hypertension prevalence, coding and control in an urban primary care setting in the UK between 2014 and 2021.

OBJECTIVE: Hypertension is a leading preventable cause of mortality, yet high rates of undiagnosed and uncontrolled hypertension continue. The burden falls most heavily on some ethnic minorities and the socially deprived, with the COVID-19 pandemic having further widened inequalities. We sought to determine the prevalence and predictors of unmeasured blood pressure (BP), uncoded elevated BP and uncontrolled hypertension in primary care across 2014-2021. METHODS: A population-based cohort study using data from all 41 general practices in a socioeconomically diverse inner-city borough. BP measurements, sociodemographic, lifestyle and clinical factors were extracted from anonymized primary care data. Hypertension and BP control were defined using NICE guidelines. Associations between patient characteristics and hypertension outcomes were identified using logistical regression modelling. RESULTS: Of 549 082 patients, 39.5% had unmeasured BP; predictors included male sex [AOR 2.40, 95% confidence interval (95% CI) 2.26-2.43] and registration in the pandemic years. Of 71 970 adults with elevated BP, 36.0% were uncoded; predictors included obesity (AOR 2.51, 95% CI 2.42-2.60) and increasing age. Of 44 648 adults on the hypertension register, 46.8% had uncontrolled hypertension; predictors included black ethnicity compared to white (AOR 1.54, 95% CI 1.41-1.68) and cardiovascular co-morbidities (AOR 1.23, 95% CI 1.21-1.25). Social deprivation was only weakly or not significantly associated with hypertension outcomes. CONCLUSION: The burden of uncoded elevated BP and uncontrolled hypertension is high. Obesity and male sex were associated with uncoded elevated BP and uncontrolled hypertension. Black ethnicity was associated with uncontrolled hypertension. Initiatives are needed to optimize hypertension coding and control, with an emphasis on specific population subgroups.

Serial dependence in visual perception: A meta-analysis and review.

Positive sequential dependencies are phenomena in which actions, perception, decisions, and memory of features or objects are systematically biased toward visual experiences from the recent past. Among many labels, serial dependencies have been referred to as priming, sequential dependencies, sequential effects, or serial effects. Despite extensive research on the topic, the field still lacks an operational definition of what counts as serial dependence. In this meta-analysis, we review the vast literature on serial dependence and quantitatively assess its key diagnostic characteristics across several different domains of visual perception. The meta-analyses fully characterize serial dependence in orientation, face, and numerosity perception. They show that serial dependence is defined by four main kinds of tuning: serial dependence decays with time (temporal-tuning), it depends on relative spatial location (spatial-tuning), it occurs only between similar features and objects (feature-tuning), and it is modulated by attention (attentional-tuning). We also review studies of serial dependence that report single observer data, highlighting the importance of individual differences in serial dependence. Finally, we discuss a range of outstanding questions and novel research avenues that are prompted by the meta-analyses. Together, the meta-analyses provide a full characterization of serial dependence as an operationally defined family of visual phenomena, and they outline several of the key diagnostic criteria for serial dependence that should serve as guideposts for future research.

Serial dependence in perception across naturalistic generative adversarial network-generated mammogram.

Purpose: Human perception and decisions are biased toward previously seen stimuli. This phenomenon is known as serial dependence and has been extensively studied for the last decade. Recent evidence suggests that clinicians' judgments of mammograms might also be impacted by serial dependence. However, the stimuli used in previous psychophysical experiments on this question, consisting of artificial geometric shapes and healthy tissue backgrounds, were unrealistic. We utilized realistic and controlled generative adversarial network (GAN)-generated radiographs to mimic images that clinicians typically encounter. Approach: Mammograms from the digital database for screening mammography (DDSM) were utilized to train a GAN. This pretrained GAN was then adopted to generate a large set of authentic-looking simulated mammograms: 20 circular morph continuums, each with 147 images, for a total of 2940 images. Using these stimuli in a standard serial dependence experiment, participants viewed a random GAN-generated mammogram on each trial and subsequently matched the GAN-generated mammogram encountered using a continuous report. The characteristics of serial dependence from each continuum were analyzed. Results: We found that serial dependence affected the perception of all naturalistic GAN-generated mammogram morph continuums. In all cases, the perceptual judgments of GAN-generated mammograms were biased toward previously encountered GAN-generated mammograms. On average, perceptual decisions had 7% categorization errors that were pulled in the direction of serial dependence. Conclusions: Serial dependence was found even in the perception of naturalistic GAN-generated mammograms created by a GAN. This supports the idea that serial dependence could, in principle, contribute to decision errors in medical image perception tasks.

Inferential Emotion Tracking reveals impaired context-based emotion processing in individuals with high Autism Quotient scores.

Emotion perception is essential for successful social interactions and maintaining long-term relationships with friends and family. Individuals with autism spectrum disorder (ASD) experience social communication deficits and have reported difficulties in facial expression recognition. However, emotion recognition depends on more than just processing face expression; context is critically important to correctly infer the emotions of others. Whether context-based emotion processing is impacted in those with Autism remains unclear. Here, we used a recently developed context-based emotion perception task, called Inferential Emotion Tracking (IET), and investigated whether individuals who scored high on the Autism Spectrum Quotient (AQ) had deficits in context-based emotion perception. Using 34 videos (including Hollywood movies, home videos, and documentaries), we tested 102 participants as they continuously tracked the affect (valence and arousal) of a blurred-out, invisible character. We found that individual differences in Autism Quotient scores were more strongly correlated with IET task accuracy than they are with traditional face emotion perception tasks. This correlation remained significant even when controlling for potential covarying factors, general intelligence, and performance on traditional face perception tasks. These findings suggest that individuals with ASD may have impaired perception of contextual information, it reveals the importance of developing ecologically relevant emotion perception tasks in order to better assess and treat ASD, and it provides a new direction for further research on context-based emotion perception deficits in ASD.

Serial Dependence in Dermatological Judgments.

Serial Dependence is a ubiquitous visual phenomenon in which sequentially viewed images appear more similar than they actually are, thus facilitating an efficient and stable perceptual experience in human observers. Although serial dependence is adaptive and beneficial in the naturally autocorrelated visual world, a smoothing perceptual experience, it might turn maladaptive in artificial circumstances, such as medical image perception tasks, where visual stimuli are randomly sequenced. Here, we analyzed 758,139 skin cancer diagnostic records from an online app, and we quantified the semantic similarity between sequential dermatology images using a computer vision model as well as human raters. We then tested whether serial dependence in perception occurs in dermatological judgments as a function of image similarity. We found significant serial dependence in perceptual discrimination judgments of lesion malignancy. Moreover, the serial dependence was tuned to the similarity in the images, and it decayed over time. The results indicate that relatively realistic store-and-forward dermatology judgments may be biased by serial dependence. These findings help in understanding one potential source of systematic bias and errors in medical image perception tasks and hint at useful approaches that could alleviate the errors due to serial dependence.

Serial dependence in emotion perception mirrors the autocorrelations in natural emotion statistics.

A critical function of the human visual system is to track emotion accurately and continuously. However, visual information about emotion fluctuates over time. Ideally, the visual system should track these temporal fluctuations-these "natural emotion statistics" of the world-over time. This would balance the need to detect changes in emotion with the need to maintain the stability of visual scene representations. The visual system could promote this goal through serial dependence, which biases our perception of facial expressions toward those seen in the recent past and thus smooths our perception of the world. Here, we quantified the natural emotion statistics in videos by measuring the autocorrelations in emotional content present in films and movies. The results showed that observers' perception of emotion was smoothed over ∼12 seconds or more, and this time-course closely followed the temporal fluctuations in visual information about emotion found in natural scenes. Moreover, the temporal and feature tuning of the perceptual smoothing was consistent with known properties of serial dependence. Our findings suggest that serial dependence is introduced in the perception of emotion to match the natural autocorrelations that are observed in the real world, an operation that could improve the efficiency, sensitivity, and stability of emotion perception.

Individual differences in classification images of Mooney faces.

Human face recognition is robust even under conditions of extreme lighting and in situations where there is high noise and uncertainty. Mooney faces are a canonical example of this: Mooney faces are two-tone shadow-defined images that are readily and holistically recognized despite lacking easily segmented face features. Face perception in such impoverished situations-and Mooney face perception in particular-is often thought to be supported by comparing encountered faces to stored templates. Here, we used a classification image approach to measure the templates that observers use to recognize Mooney faces. Visualizing these templates reveals the regions and structures of the image that best predict individual observer recognition, and they reflect the underlying internal representation of faces. Using this approach, we tested whether there are classification images that are consistent from session to session, whether the classification images are observer-specific, and whether they allow for pattern completion of holistic representations even in the absence of an underlying signal. We found that classification images of Mooney faces were indeed non-random (i.e., consistent session from session) within each observer, but they were different between observers. This result is in line with previously proposed existence of face templates that support face recognition, and further suggests that these templates may be unique to each observer and could drive idiosyncratic individual differences in holistic face recognition. Moreover, we found classification images that reflected information within the blank regions of the original Mooney faces, suggesting that observers may fill in missing information using idiosyncratic internal information about faces.

Ensemble perception: Stacking the hay to find the needle.

The visual clutter we constantly encounter in the world limits object recognition, a phenomenon known as visual crowding. A new study shows that ensemble perception counters this by condensing redundant information into summary statistical representations, which thus releases visual crowding's effect on individual objects.

Searching for serial dependencies in the brain.

Identifying the neural correlates of visual serial dependence has lagged behind the behavioral understanding. A new study in PLOS Biology provides a model of interpreting the complex relationship between physiology and behavior in studies of serial dependence.

Advancing Research on Medical Image Perception by Strengthening Multidisciplinary Collaboration.

Medical image interpretation is central to detecting, diagnosing, and staging cancer and many other disorders. At a time when medical imaging is being transformed by digital technologies and artificial intelligence, understanding the basic perceptual and cognitive processes underlying medical image interpretation is vital for increasing diagnosticians' accuracy and performance, improving patient outcomes, and reducing diagnostician burnout. Medical image perception remains substantially understudied. In September 2019, the National Cancer Institute convened a multidisciplinary panel of radiologists and pathologists together with researchers working in medical image perception and adjacent fields of cognition and perception for the "Cognition and Medical Image Perception Think Tank." The Think Tank's key objectives were to identify critical unsolved problems related to visual perception in pathology and radiology from the perspective of diagnosticians, discuss how these clinically relevant questions could be addressed through cognitive and perception research, identify barriers and solutions for transdisciplinary collaborations, define ways to elevate the profile of cognition and perception research within the medical image community, determine the greatest needs to advance medical image perception, and outline future goals and strategies to evaluate progress. The Think Tank emphasized diagnosticians' perspectives as the crucial starting point for medical image perception research, with diagnosticians describing their interpretation process and identifying perceptual and cognitive problems that arise. This article reports the deliberations of the Think Tank participants to address these objectives and highlight opportunities to expand research on medical image perception.

The Power of Access in Parkinson's Disease Care: A Retrospective Review of Telehealth Uptake During the COVID-19 Pandemic.

Objective: The onset of the COVID-19 pandemic in March of 2020 forced a rapid pivot to telehealth and compelled a use-case experiment in specialty telehealth neurology movement disorders care. The aims of this study were to quantify the potential benefit of telehealth as an option to the Parkinson's disease community as shown by the first 9 months of the COVID-19 pandemic, and to quantify the potential impact of the absence of a deep brain stimulation (DBS) telehealth option on DBS patient follow-up. Methods: New patient visits to the Inova Parkinson's and Movement Disorder's Center from April to December 2020 (9 months) were retrospectively reviewed for telehealth vs. in-person, demographics (age, gender, race, primary insurance), chief complaint, prior movement disorders specialist (MDS) consultation, imaging tests ordered, and distance/travel time from primary zip code to clinic. Additionally, DBS programming visit volume from April to December 2020 was compared to DBS programming visit volume from April to December 2019. Results: Of the 1,097 new patients seen, 85% were via telehealth (N = 932) and 15% in person (N = 165). In the telehealth cohort, 97.75% had not consulted with an MDS before (N = 911), vs. 87.9% of in-person (N = 145). Age range was 61.8 +/- 17.9 years (telehealth), 68.8 +/- 16.0 years (in-person). Racial breakdown for telehealth was 60.7% White (N = 566), 10.4% Black (N = 97), 7.4% Asian (N = 69) and 4.5% Hispanic (N = 42); in-person was 70.9% White (N = 117), 5.5% Black (N = 9), 7.9% Asian (N = 13) and 5.5% Hispanic (N = 9). Top 5 consultation reasons, top 10 primary insurance providers and imaging studies ordered between the two cohorts were similar. Distance/travel time between primary zip code and clinic were 33.8 +/- 104.8 miles and 42.2 +/- 93.4 min (telehealth) vs. 38.1 +/- 114.7 miles and 44.1 +/- 97.6 min (in-person). DBS programming visits dropped 24.8% compared to the same period the year before (254 visits to 191 visits). Conclusion: Telehealth-based new patient visits to a Movement Disorders Center appeared successful at increasing access to specialty care. The minimal difference in supporting data highlights the potential parity to in-person visits. With no telehealth option for DBS visits, a significant drop-off was seen in routine DBS management.

Relative tuning of holistic face processing towards the fovea.

Humans quickly detect and gaze at faces in the world, which reflects their importance in cognition and may lead to tuning of face recognition toward the central visual field. Although sometimes reported, foveal selectivity in face processing is debated: brain imaging studies have found evidence for a central field bias specific to faces, but behavioral studies have found little foveal selectivity in face recognition. These conflicting results are difficult to reconcile, but they could arise from stimulus-specific differences. Recent studies, for example, suggest that individual faces vary in the degree to which they require holistic processing. Holistic processing is the perception of faces as a whole rather than as a set of separate features. We hypothesized that the dissociation between behavioral and neuroimaging studies arises because of this stimulus-specific dependence on holistic processing. Specifically, the central bias found in neuroimaging studies may be specific to holistic processing. Here, we tested whether the eccentricity-dependence of face perception is determined by the degree to which faces require holistic processing. We first measured the holistic-ness of individual Mooney faces (two-tone shadow images readily perceived as faces). In a group of independent observers, we then used a gender discrimination task to measured recognition of these Mooney faces as a function of their eccentricity. Face gender was recognized across the visual field, even at substantial eccentricities, replicating prior work. Importantly, however, holistic face gender recognition was relatively tuned-slightly, but reliably stronger in the central visual field. Our results may reconcile the debate on the eccentricity-dependance of face perception and reveal a spatial inhomogeneity specifically in the holistic representations of faces.

The test-retest reliability and spatial tuning of serial dependence in orientation perception.

Humans perceive objects and scenes consistently, even in situations where visual input is noisy and unstable. One of the mechanisms that underlies this perceptual stability is serial dependence, whereby the perception of objects or features at any given moment is pulled toward what was previously seen. Although recent findings from several studies have reported large individual differences in serial dependence, it is not clear how stable the serial dependence is within an individual. Here, we investigated the stability of serial dependence in orientation perception over two different days within the same observers. In addition, we also examined the visual field location specificity of perceptual serial dependence. On each trial, observers viewed a Gabor patch and then reported its apparent orientation by adjusting the orientation of a bar. For each observer, the Gabor was located in the foveal or peripheral (10° right or left eccentricity) visual field on both days or changed location from day to day. The results showed a very high degree of test-retest reliability in serial dependence measured across days within individual observers. Interestingly, this high within-subject consistency was only found when serial dependence was measured at the same visual field location. These results suggest that individual differences in serial dependence are stable across days, and that the spatiotemporal range in which the previous stimulus assimilates the perception of the current stimulus (the continuity field) may vary across different visual field locations in an observer-specific manner.

Illusion of visual stability through active perceptual serial dependence.

Despite a noisy and ever-changing visual world, our perceptual experience seems remarkably stable over time. How does our visual system achieve this apparent stability? Here, we introduce a previously unknown visual illusion that shows direct evidence for an online mechanism continuously smoothing our percepts over time. As a result, a continuously seen physically changing object can be misperceived as unchanging. We find that online object appearance is captured by past visual experience up to 15 seconds ago. We propose that, because of an underlying active mechanism of serial dependence, the representation of the object is continuously merged over time, and the consequence is an illusory stability in which object appearance is biased toward the past. Our results provide a direct demonstration of the link between serial dependence in visual representations and perceived visual stability in everyday life.

Controllable Medical Image Generation via GAN.

Medical image data is critically important for a range of disciplines, including medical image perception research, clinician training programs, and computer vision algorithms, among many other applications. Authentic medical image data, unfortunately, is relatively scarce for many of these uses. Because of this, researchers often collect their own data in nearby hospitals, which limits the generalizabilty of the data and findings. Moreover, even when larger datasets become available, they are of limited use because of the necessary data processing procedures such as de-identification, labeling, and categorizing, which requires significant time and effort. Thus, in some applications, including behavioral experiments on medical image perception, researchers have used naive artificial medical images (e.g., shapes or textures that are not realistic). These artificial medical images are easy to generate and manipulate, but the lack of authenticity inevitably raises questions about the applicability of the research to clinical practice. Recently, with the great progress in Generative Adversarial Networks (GAN), authentic images can be generated with high quality. In this paper, we propose to use GAN to generate authentic medical images for medical imaging studies. We also adopt a controllable method to manipulate the generated image attributes such that these images can satisfy any arbitrary experimenter goals, tasks, or stimulus settings. We have tested the proposed method on various medical image modalities, including mammogram, MRI, CT, and skin cancer images. The generated authentic medical images verify the success of the proposed method. The model and generated images could be employed in any medical image perception research.

Idiosyncratic biases in the perception of medical images.

Introduction: Radiologists routinely make life-altering decisions. Optimizing these decisions has been an important goal for many years and has prompted a great deal of research on the basic perceptual mechanisms that underlie radiologists' decisions. Previous studies have found that there are substantial individual differences in radiologists' diagnostic performance (e.g., sensitivity) due to experience, training, or search strategies. In addition to variations in sensitivity, however, another possibility is that radiologists might have perceptual biases-systematic misperceptions of visual stimuli. Although a great deal of research has investigated radiologist sensitivity, very little has explored the presence of perceptual biases or the individual differences in these. Methods: Here, we test whether radiologists' have perceptual biases using controlled artificial and Generative Adversarial Networks-generated realistic medical images. In Experiment 1, observers adjusted the appearance of simulated tumors to match the previously shown targets. In Experiment 2, observers were shown with a mix of real and GAN-generated CT lesion images and they rated the realness of each image. Results: We show that every tested individual radiologist was characterized by unique and systematic perceptual biases; these perceptual biases cannot be simply explained by attentional differences, and they can be observed in different imaging modalities and task settings, suggesting that idiosyncratic biases in medical image perception may widely exist. Discussion: Characterizing and understanding these biases could be important for many practical settings such as training, pairing readers, and career selection for radiologists. These results may have consequential implications for many other fields as well, where individual observers are the linchpins for life-altering perceptual decisions.

Inferential emotion tracking (IET) reveals the critical role of context in emotion recognition.

The ability to recognize others' emotions is critical for social interactions. It is widely assumed that recognizing facial expressions predominantly determines perceived categorical emotion, and contextual information only coarsely modulates or disambiguates interpreted faces. Using a novel method, inferential emotion tracking, we isolated and quantified the contribution of visual context versus face and body information in dynamic emotion recognition. Even when faces and bodies were blurred out in muted videos, observers inferred the emotion of invisible characters accurately and in high agreement based solely on visual context. Our results further show that the presence of visual context can override interpreted emotion categories from face and body information. Strikingly, we find that visual context determines perceived emotion nearly as much and as often as face and body information does. Visual context is an essential and indispensable element of emotion recognition: Without context, observers can misperceive a person's emotion over time. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Prevalence of Pseudobulbar Affect (PBA) in Parkinson's Disease: An Underrecognized Patient Burden.

Objective Pseudobulbar affect (PBA) is a neurological condition characterized by emotional lability and a discrepancy between the patient's emotional expression and emotional experience. These uncontrollable episodes cause distress in social situations resulting in embarrassment and social withdrawal. The most comprehensive study to date estimated that 26% of Parkinson's disease (PD) patients screened positive for PBA symptoms via the validated Center for Neurologic Study-Lability Scale (CNS-LS) screening tool. We hypothesize that the prevalence of this disabling syndrome is higher than reported, often being labeled as depression. Methods One hundred patients were enrolled in the study and screened with a CNS-LS tool, all of whom were diagnosed with PD by a fellowship-trained movement disorder specialist. Patients were also asked about previous diagnosis of depression, current antidepressant medication use, and history of PBA diagnosis and treatment. Results The percentage of PD patients (n = 100) with PBA symptoms as defined by a CNS-LS score ≥13 was 41% (n = 41) and by a CNS-LS score ≥17 was 21.0% (n = 21). In our sample, 38.0% of patients (n = 38) had a previous clinical diagnosis of depression and 25.0% (n = 25) were currently undergoing treatment for their depression. There was a significant association between previous depression diagnosis, current antidepressant use, and higher CNS-LS scores (p < 0.001). Conclusion Using either of the CNS-LS score cutoffs, a significant proportion of the PD population in our sample displayed symptoms of PBA. We also found an association between previous diagnosis of depression and higher CNS-LS scores as well as between antidepressant use and higher CNS-LS scores. This suggests both a higher prevalence than prior studies showed as well as frequent misdiagnosis or co-diagnosis with depression.