Learning Task Research Articles

EXPRESS: Implicit learning in children and adolescents with and without autism spectrum disorders: Exploring the effects of individual differences.

Implicit learning plays an important role in the acquisition of various skills that are often deficient in individuals with autism spectrum disorder (ASD). The present study examines the implicit learning ability of children and adolescents with ASD, by comparing it to that of a typical group of peers, using the Artificial Grammar Learning (AGL) task. Additionally, this study investigates whether the above ability is associated with individual characteristics, namely, intelligence quotient (IQ), ASD symptoms severity, and individual perception style (global/holistic or local/focused). The sample consisted of 20 individuals with mild to relatively severe ASD symptoms and 20 age- and gender-matched typically developing individuals (TD). The unconscious (implicit) nature of learning was assessed via a subjective measure, the "no loss gambling" method, which allows an unbiased evaluation of the confidence accompanying each judgement. The results provided evidence of implicit learning, which was preserved in the ASD group, although reduced relative to the typical group. Multiple linear regressions with interaction terms between Group and participants' scores on the Wechsler Abbreviated Scale of Intelligence (WASI), the Childhood Autism Rating Scale (CARS), and performance on a Navon-type task examined whether the possible relationships between each of the above scores and AGL and implicit learning differ in the two groups. Implicit learning was not significantly associated with IQ, ASD symptoms severity or individual perception style (except for perception style in terms of RTs for the TD group). These results confirm and extend earlier findings supporting the resilience of implicit learning to individual differences.

Simultaneously predicting SPAD and water content in rice leaves using hyperspectral imaging with deep multi-task regression and transfer component analysis.

Water content and chlorophyll content are important indicators for monitoring rice growth status. Simultaneous detection of water content and chlorophyll content is of significance. Different varieties of rice show differences in phenotype, resulting in the difficulties of establishing a universal model. In this study, hyperspectral imaging was used to detect the Soil and Plant Analyzer Development (SPAD) values and water content of fresh rice leaves of three rice varieties (Jiahua 1, Xiushui 121 and Xiushui 134). Both partial least squares regression and convolutional neural networks were used to establish single-task and multi-task models. Transfer component analysis (TCA) was used as transfer learning to learn the common features to achieve an approximate identical distribution between any two varieties. Single-task and multi-task models were also built using the features of the source domain, and these models were applied to the target domain. These results indicated that for models of each rice variety the prediction accuracy of most multi-task models was close to that of single-task models. As for TCA, the results showed that the single-task model achieved good performance for all transfer learning tasks. Compared with the original model, good and differentiated results were obtained for the models using features learned by TCA for both the source domain and target domain. The multi-task models could be constructed to predict SPAD values and water content simultaneously and then transferred to another rice variety, which could improve the efficiency of model construction and realize rapid detection of rice growth indicators. © 2024 Society of Chemical Industry.

Designing Universally-Approximating Deep Neural Networks: A First-Order Optimization Approach.

Universal approximation capability, also referred to as universality, is an important property of deep neural networks, endowing them with the potency to accurately represent the underlying target function in learning tasks. In practice, the architecture of deep neural networks largely influences the performance of the models. However, most existing methodologies for designing neural architectures, such as the heuristic manual design or neural architecture search, ignore the universal approximation property, thus losing a potential safeguard about the performance. In this paper, we propose a unified framework to design the architectures of deep neural networks with a universality guarantee based on first-order optimization algorithms, where the forward pass is interpreted as the updates of an optimization algorithm. The (explicit or implicit) network is designed by replacing each gradient term in the algorithm with a learnable module similar to a two-layer network or its derivatives. Specifically, we explore the realm of width-bounded neural networks, a common practical scenario, showcasing their universality. Moreover, adding operations of normalization, downsampling, and upsampling does not hurt the universality. To the best of our knowledge, this is the first work that width-bounded networks with universal approximation guarantee can be designed in a principled way. Our framework can inspire a variety of neural architectures including some renowned structures such as ResNet and DenseNet, as well as novel innovations. The experimental results on image classification problems demonstrate that the newly inspired networks are competitive and surpass the baselines of ResNet, DenseNet, as well as the advanced ConvNeXt and ViT, testifying to the effectiveness of our framework.

Neuroprotective effects of Paederia foetida Linn. on scopolamine-induced cognitive impairment in rats

Background and Aim: Alzheimer’s disease (AD) poses a significant health-care challenge, often linked to cognitive decline caused by oxidative stress. This study investigated the potential neuroprotective effects of the Paederia foetida leaf extract (PFE) in rats that exhibited scopolamine-induced dementia mimicking AD. Materials and Methods: Forty-two male rats were treated with either donepezil (0.5 mg/kg) or PFE at doses of 250, 500, and 1000 mg/kg for 14 days before and 14 days after the beginning of Alzheimer’s-like symptoms after 14 consecutive days of scopolamine administration. Behavioral tests, including the open-field test for locomotor activity and the Morris water maze task for learning and memory assessment, were conducted. Neuronal cell counts and biochemical assays were performed to further analyze outcomes. Results: All groups exhibited normal locomotor activity. The scopolamine group displayed longer escape latency times, reduced time in the target quadrant, decreased number of surviving neurons, and increased malondialdehyde and decreased glutathione levels compared with the control group. However, pre-treatment with 1000 mg/kg PFE notably mitigated the neurotoxic effects of scopolamine. Conclusion: The neuroprotective properties of PFE are highlighted, suggesting its potential as a promising treatment strategy for AD. Keywords: Alzheimer’s disease, neuroprotection, oxidative stress, Paederia foetida, scopolamine.

Learning to imitate facial expressions through sound

The question of how young infants learn to imitate others’ facial expressions has been central in developmental psychology for decades. Facial imitation has been argued to constitute a particularly challenging learning task for infants because facial expressions are perceptually opaque: infants cannot see changes in their own facial configuration when they execute a motor program, so how do they learn to match these gestures with those of their interacting partners? Here we argue that this apparent paradox mainly appears if one focuses only on the visual modality, as most existing work in this field has done so far. When considering other modalities, in particular the auditory modality, many facial expressions are not actually perceptually opaque. In fact, every orolabial expression that is accompanied by vocalisations has specific acoustic consequences, which means that it is relatively transparent in the auditory modality. Here, we describe how this relative perceptual transparency can allow infants to accrue experience relevant for orolabial, facial imitation every time they vocalise. We then detail two specific mechanisms that could support facial imitation learning through the auditory modality. First, we review evidence showing that experiencing correlated proprioceptive and auditory feedback when they vocalise – even when they are alone – enables infants to build audio-motor maps that could later support facial imitation of orolabial actions. Second, we show how these maps could also be used by infants to support imitation even for silent, orolabial facial expressions at a later stage. By considering non-visual perceptual domains, this paper expands our understanding of the ontogeny of facial imitation and offers new directions for future investigations.

Hybrid embedded and filter feature selection methods in big-dimension mammary cancer and prostatic cancer data

<p>The feature selection method enhances machine learning performance by enhancing learning precision. Determining the optimal feature selection method for a given machine learning task involving big-dimension data is crucial. Therefore, the purpose of this study is to make a comparison of feature selection methods highlighting several filters (information gain, chi-square, ReliefF) and embedded (Lasso, Ridge) hybrid with logistic regression (LR). A sample size of n=100, 75 is chosen randomly, and the reduction features d=50, 22, and 10 are applied. The procedure for feature reduction makes use of the entire sample sizes. Each sample size's results are compared, including tests with no feature selection process. The results indicate that LR+ReliefF is the best method for mammary cancer data, whereas LR+IG is the best for prostatic cancer data, making the filter more suitable than embedded for big-dimension data. This study revealed that the sample's features and size influence the most effective method for selecting features from big-dimension data. Therefore, it provides insight into the most effective methods for particular features and sample sizes in high-dimensional data.</p>

Zipfian distributions facilitate children's learning of novel word-referent mappings

The word-frequency distributions children hear during language learning are highly skewed (Zipfian). Previous studies suggest that such skewed environments confer a learnability advantage in tasks that require the learner to discover the units that have to be learned, as in word-segmentation or cross-situational learning. This facilitative effect has been attributed to contextual facilitation from high frequency items in learning lower frequency items, and to better learning under the increased predictability (lower entropy) of skewed distributions. Here, we ask whether Zipfian distributions facilitate learning beyond the discovery of units, as expected under the predictability account. We tested children's learning of novel word-referent mappings in a learning task where each mapping was presented in isolation during training, and did not need to be dicovered. We compared learning in a uniform environment to two skewed environments with different entropy levels. Children's learning was overall better in the two skewed environments, also for low frequency items. These results extend the facilitative effect of Zipfian distributions to additional learning tasks and show they can facilitate learning beyond the discovery of units.

MRS-assessed brain GABA modulation in response to task performance and learning.

Gamma-aminobutyric acid (GABA), the most important inhibitory neurotransmitter in the human brain, has long been considered essential in human behavior in general and learning in particular. GABA concentration can be quantified using magnetic resonance spectroscopy (MRS). Using this technique, numerous studies have reported associations between baseline GABA levels and various human behaviors. However, regional GABA concentration is not fixed and may exhibit rapid modulation as a function of environmental factors. Hence, quantification of GABA levels at several time points during the performance of tasks can provide insights into the dynamics of GABA levels in distinct brain regions. This review reports on findings from studies using repeated measures (n = 41) examining the dynamic modulation of GABA levels in humans in response to various interventions in the perceptual, motor, and cognitive domains to explore associations between GABA modulation and human behavior. GABA levels in a specific brain area may increase or decrease during task performance or as a function of learning, depending on its precise involvement in the process under investigation. Here, we summarize the available evidence and derive two overarching hypotheses regarding the role of GABA modulation in performance and learning. Firstly, training-induced increases in GABA levels appear to be associated with an improved ability to differentiate minor perceptual differences during perceptual learning. This observation gives rise to the 'GABA increase for better neural distinctiveness hypothesis'. Secondly, converging evidence suggests that reducing GABA levels may play a beneficial role in effectively filtering perceptual noise, enhancing motor learning, and improving performance in visuomotor tasks. Additionally, some studies suggest that the reduction of GABA levels is related to better working memory and successful reinforcement learning. These observations inspire the 'GABA decrease to boost learning hypothesis', which states that decreasing neural inhibition through a reduction of GABA in dedicated brain areas facilitates human learning. Additionally, modulation of GABA levels is also observed after short-term physical exercise. Future work should elucidate which specific circumstances induce robust GABA modulation to enhance neuroplasticity and boost performance.

INFOGRAPHIC: Insights into the design of practice tasks for learning of decision-making on motor skill of passing in sport of futsal

INFOGRAPHIC: Insights into the design of practice tasks for learning of decision-making on motor skill of passing in sport of futsal

Real-Time Human Profiling: Unveiling Age, Gender and Emotions Using Deep Learning

This paper introduces the development of a real-time system, which deploys an integrated use of Flask, deep learning, Convolutional Neural Networks (CNNs) and cascade classifier approach to detect age, gender, and emotion from facial images. While its numerous applications—ranging from marketing and medical services to security surveillance—immediately catches the eye, the facial recognition technology is fast becoming the hot topic. Our suggested system aims at absolutely determining the age, gender or emotional state of a person instantaneously in real life. Flask, Python micro web framework, is at the basis of the software, providing the desired functionalities for the exchange of information between the processing engine and the front end. Convolutional Neural Networks (CNNs) which are a part of deep learning for tasks involving feature extraction and classification are the main tool used by most learning algorithms. CNNs are widely used in face recognition systems mainly due to the fact that they perform very well at such tasks as image processing. On the one hand, the model uses cascade classifiers for superior face detection, thereby finding and separating face regions in input images or video streams. Unlike some of the approaches that require heavy computation, these classifiers are computationally light solutions that can run in real-time even on resource- limited devices. The system capacity to identify age, gender, and emotion accurately in real-time is exemplified through: performance evaluation. We have used various means to stress the system and ensure that it is precise and offers timely results over the given period. By means of varied testing stages, we have discovered that the system usually brings high levels of precision and validity for the numerous data sets and trial situations. In this regard, be it differentiating between individuals’ age or readily identifying the exact gender or recognizing nuanced emotional clues, the system performs at the optimum level.

Enhanced "learning to learn" through a hierarchical dual-learning system: the case of action video game players.

In contrast to conventional cognitive training paradigms, where learning effects are specific to trained parameters, playing action video games has been shown to produce broad enhancements in many cognitive functions. These remarkable generalizations challenge the conventional theory of generalization that learned knowledge can be immediately applied to novel situations (i.e., immediate generalization). Instead, a new "learning to learn" theory has recently been proposed, suggesting that these broad generalizations are attained because action video game players (AVGPs) can quickly acquire the statistical regularities of novel tasks in order to increase the learning rate and ultimately achieve better performance. Although enhanced learning rate has been found for several tasks, it remains unclear whether AVGPs efficiently learn task statistics and use learned task knowledge to guide learning. To address this question, we tested 34 AVGPs and 36 non-video game players (NVGPs) on a cue-response associative learning task. Importantly, unlike conventional cognitive tasks with fixed task statistics, in this task, cue-response associations either remain stable or change rapidly (i.e., are volatile) in different blocks. To complete the task, participants should not only learn the lower-level cue-response associations through explicit feedback but also actively estimate the high-level task statistics (i.e., volatility) to dynamically guide lower-level learning. Such a dual learning system is modelled using a hierarchical Bayesian learning framework, and we found that AVGPs indeed quickly extract the volatility information and use the estimated higher volatility to accelerate learning of the cue-response associations. These results provide strong evidence for the "learning to learn" theory of generalization in AVGPs. Taken together, our work highlights enhanced hierarchical learning of both task statistics and cognitive abilities as a mechanism underlying the broad enhancements associated with action video game play.

Improving class probability estimates in asymmetric health data classification: An experimental comparison of novel calibration methods

In the context of health data classification, imbalanced and asymmetric class distributions can significantly impact the performance of machine learning models. One critical aspect affected by these issues is the reliability of class probability estimates, which are crucial for informed decision-making in healthcare applications. Instead of predicting class values directly for a classification problem, it can be more convenient to predict the probability of an observation belonging to each possible class. This research aims to address the challenges posed by imbalanced and asymmetric responses in health data classification by evaluating the effectiveness of recent calibration methods in improving class probability estimates. We propose Beta calibration techniques and the Stratified Brier score and Jaccard's Score as novel calibration methods and evaluation metrics respectively. The experimental comparison involves implementing and assessing various calibration techniques to determine their impact on model performance and calibration accuracy of simulated and healthcare datasets with varying imbalance ratios. Our results show that the Beta calibration method consistently improved the classifiers' predictive ability. The findings of this study provide valuable insights into selecting the most suitable calibration method for enhancing class probability estimates in healthcare-related machine learning tasks.

Acute stress does not influence the learning of a precise manual task: a randomized clinical trial.

Acute stress is frequent in sports and rehabilitation contexts and can impact cognitive processes essential for motor learning. This study aimed to investigate the influence of induced acute stress on the learning of a precise manual task, examining its effect on five key parameters of fine motor control: trajectory error, trajectory error direction, time error, tracing accuracy, and task accuracy. A double-masked, randomized clinical trial with 62 participants (average age 20.65 ± 2.54 years; 39 females; 23 males) was conducted. To examine the effects of stress, participants were assigned to either a stress or a control group through stratified randomization by sex. Initially, all participants underwent the Maastricht Acute Stress Test (in its acute stress and control versions, respectively). Subsequently, they performed the precise manual task on a graphic tablet at three stages of the learning process: acquisition, short-term retrieval, and long-term retrieval. Electrodermal activity and heart rate variability were recorded to assess stress induction. Data analysis from 30 stress group participants and 25 control group participants revealed no statistically significant differences between groups in any of the variables studied at the three learning stages. Both groups exhibited statistically significant improvements in time error, trajectory error direction, and tracing accuracy during both short-term and long-term retrieval compared to acquisition. Our findings suggest that acute physical and psychological stress does not markedly impair learning a precise manual task of adhering to a specific trajectory and pace among young adults.

A shared temporal window of integration across cognitive control and reinforcement learning paradigms: A correlational study.

Cognitive control refers to the ability to override prepotent response tendencies to achieve goal-directed behavior. On the other hand, reinforcement learning refers to the learning of actions through feedback and reward. Although cognitive control and reinforcement learning are often viewed as opposing forces in driving behavior, recent theories have emphasized possible similarities in their underling processes. With this study, we aimed to investigate whether a similar time window of integration could be observed during the learning of control on the one hand, and the learning rate in reinforcement learning paradigms on the other. To this end, we performed a correlational analysis on a large public dataset (n = 522) including data from two reinforcement learning tasks, i.e., a probabilistic selection task and a probabilistic Wisconsin Card Sorting Task (WCST), and data from a classic conflict task (i.e., the Stroop task). Results showed expected correlations between the time scale of control indices and learning rate in the probabilistic WCST. Moreover, the learning-rate parameters of the two reinforcement learning tasks did not correlate with each other. Together, these findings suggest a reliance on a shared learning mechanism between these two traditionally distinct domains, while at the same time emphasizing that value updating processes can still be very task-specific. We speculate that updating processes in the Stroop and WCST may be more related because both tasks require task-specific updating of stimulus features (e.g., color, word meaning, pattern, shape), as opposed to stimulus identity.

How do social interaction and group-level regulation shape task perceptions in collaborative learning task?

ABSTRACT This study explored patterns of social interaction and group-level regulation in terms of co- and socially shared regulation in collaborative learning and their relation to learners’ metacognitive task perceptions. 72 secondary school students performed collaborative physics tasks in small groups over multiple 90-minute sessions. Video recordings (∼148.5 h) of collaborative interactions and pre- and post- situated self-reports of learners’ task perceptions were collected. The collaborative sessions were clustered and collaborative interaction patterns were examined. Finally, learners’ task perceptions in the clusters were investigated. The results showed different collaborative interaction patterns and differences in learners’ task perceptions between the clusters. The main finding is that continuous active participation in socioemotional interactions alongside cognitive-focused interactions and appropriate group-level regulation of learning positively influence learners’ perceived task understanding. These findings add to current understanding of collaborative interactions as temporal processes and contribute to practical implications for supporting collaborative interactions to enhance metacognitive skills.

White Matter Microstructural Changes Using Ultra-Strong Diffusion Gradient MRI in Adult-Onset Idiopathic Focal Cervical Dystonia.

Adult-onset idiopathic focal cervical dystonia (AOIFCD) involves abnormal posturing of the cervical musculature and, in some individuals, an associated head tremor. Existing neuroimaging studies have implicated key motor networks. However, measures used to date lack specificity toward underlying pathophysiologic differences. We aim to assess white matter motor pathways for localized, microstructural differences, which may aid in understanding underlying mechanisms. Individuals diagnosed with AOIFCD and an age- and sex-matched control group were prospectively recruited through the Welsh Movement Disorders Research Network. All participants underwent in-depth clinical phenotyping and MRI (structural and diffusion sequences) using ultra-strong diffusion gradients. Tractography (whole-tract median values) and tractometry (along tract profiling) were performed for key white matter motor pathways assessing diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and standard model parameters. Groups were compared using linear model analysis with Bonferroni multiple comparison correction. Fifty participants with AOIFCD and 30 healthy control participants were recruited, with 46 with AOIFCD and 30 healthy controls included for analysis (33 without head tremor, 13 with head tremor). Significant differences were observed in the anterior thalamic radiations (lower mid-tract fractional anisotropy [estimate = -0.046, p = 3.07 × 10-3], radial kurtosis [estimate = -0.165, p = 1.42 × 10-4], f-intra-axonal signal fraction [estimate = -0.044, p = 2.78 × 10-3], p2 orientation coherence [estimate = -0.043, p = 1.64 × 10-3], higher Orientation Dispersion Index [ODI, estimate = 0.023, p = 2.22 × 10-3]) and thalamopremotor tracts (higher mid-tract mean kurtosis [estimate = 0.064, p = 7.56 × 10-4], lower Neurite Density Index [estimate = 0.062, p = 2.1 × 10-3], higher distal tract ODI [estimate = 0.062, p = 3.1 × 10-3], lower f [estimate = -0.1, p = 2.3 × 10-3], and striatopremotor tracts [proximal lower f: estimate = -0.075, p = 1.06 × 10-3]). These measures correlated with clinical measures: dystonia duration (right thalamopremotor distal ODI: r = -0.9, p = 1.29 × 10-14), psychiatric symptoms (obsessive compulsive symptoms: left anterior thalamic radiation p2 r = 0.92, p = 2.797 × 10-11), sleep quality (Sleep Disorders Questionnaire Score: left anterior thalamic radiation ODI: r = -0.84, p = 4.84 × 10-11), pain (left anterior thalamic radiation ODI: r = -0.89, p = 1.4 × 10-13), and cognitive functioning (paired associated learning task p2, r = 0.94, p = 6.68 × 10-20). Overall, localized microstructural differences were identified within tracts linking the prefrontal and premotor cortices with thalamic and basal ganglia regions, suggesting pathophysiologic processes involve microstructural aberrances of motor system modulatory pathways, particularly involving intra-axonal and fiber orientation dispersion measures.

Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification

We present a novel physics-constrained polynomial chaos expansion as a surrogate modeling method capable of performing both scientific machine learning (SciML) and uncertainty quantification (UQ) tasks. The proposed method possesses a unique capability: it seamlessly integrates SciML into UQ and vice versa, which allows it to quantify the uncertainties in SciML tasks effectively and leverage SciML for improved uncertainty assessment during UQ-related tasks. The proposed surrogate model can effectively incorporate a variety of physical constraints, such as governing partial differential equations (PDEs) with associated initial and boundary conditions constraints, inequality-type constraints (e.g., monotonicity, convexity, non-negativity, among others), and additional a priori information in the training process to supplement limited data. This ensures physically realistic predictions and significantly reduces the need for expensive computational model evaluations to train the surrogate model. Furthermore, the proposed method has a built-in uncertainty quantification (UQ) feature to efficiently estimate output uncertainties. To demonstrate the effectiveness of the proposed method, we apply it to a diverse set of problems, including linear/non-linear PDEs with deterministic and stochastic parameters, data-driven surrogate modeling of a complex physical system, and UQ of a stochastic system with parameters modeled as random fields.

Complexity affects performance, cognitive load, and awareness

BackgroundProblem-solving encourages learners to engage in deep knowledge processing. However, merely providing problem-solving activities without considering task complexity may not optimize learning. AimsThe present study analyzed the effects of task complexity (consistently high vs. gradually increasing) on performance, germane cognitive load, meta-awareness, and intrinsic interest using a learning task conducted in a laboratory. SampleWe analyzed data collected from 98 graduate and ungraduated university students (N = 98; Mage = 21.22 years, SD = 3.03). MethodsThe study comprised a main session and a post-test. During the main session participants completed a problem-solving task either with consistently high complexity or gradually increasing complexity. During the post-test all participants solved the task once with high complexity. Metacognition (regulation, knowledge), germane cognitive load, meta-awareness, and intrinsic interest were assessed by self-evaluation questionnaires. ResultsThe findings indicated positive effects of consistently high complexity on immediate performance, germane cognitive load, and meta-awareness. Additionally, an important relationship between metacognition (regulation, knowledge) and meta-awareness was identified when learning with gradually increasing complexity. However, no impact of task complexity on intrinsic interest was found. ConclusionsThe findings suggest that exposing learners to consistently high complexity tasks can have beneficial effects on learning without negatively influencing intrinsic interest.

Military Image Captioning for Low-Altitude UAV or UGV Perspectives

Low-altitude unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), which boast high-resolution imaging and agile maneuvering capabilities, are widely utilized in military scenarios and generate a vast amount of image data that can be leveraged for textual intelligence generation to support military decision making. Military image captioning (MilitIC), as a visual-language learning task, provides innovative solutions for military image understanding and intelligence generation. However, the scarcity of military image datasets hinders the advancement of MilitIC methods, especially those based on deep learning. To overcome this limitation, we introduce an open-access benchmark dataset, which was termed the Military Objects in Real Combat (MOCO) dataset. It features real combat images captured from the perspective of low-altitude UAVs or UGVs, along with a comprehensive set of captions. Furthermore, we propose a novel encoder–augmentation–decoder image-captioning architecture with a map augmentation embedding (MAE) mechanism, MAE-MilitIC, which leverages both image and text modalities as a guiding prefix for caption generation and bridges the semantic gap between visual and textual data. The MAE mechanism maps both image and text embeddings onto a semantic subspace constructed by relevant military prompts, and augments the military semantics of the image embeddings with attribute-explicit text embeddings. Finally, we demonstrate through extensive experiments that MAE-MilitIC surpasses existing models in performance on two challenging datasets, which provides strong support for intelligence warfare based on military UAVs and UGVs.

The unique face of comorbid anxiety and depression: Increased frontal, insula and cingulate cortex response during Pavlovian fear-conditioning

BackgroundSensitivity to threat with dysregulation of fear processing is thought to contribute to the development of anxiety disorders and major depressive disorder (MDD). However, fewer studies have examined fear processing in MDD than in anxiety disorders. The current study used propensity matching to examine the hypothesis that comorbid MDD and anxiety (AnxMDD) shows greater neural correlates of fear processing than MDD, suggesting that the co-occurrence of AnxMDD is exemplified by exaggerated defense related processes. Methods195 individuals with MDD (N = 65) or AnxMDD (N = 130) were recruited from the community and completed multi-level assessments, including a Pavlovian fear learning task during functional imaging. Visual images paired with threat (conditioned stimuli: CS+) were compared to stimuli not paired with threat (CS-). ResultsMDD and AnxMDD showed significantly different patterns of activation for CS+ vs CS- in the dorsal anterior insula/inferior frontal gyrus (partial eta squared; ηp2 = 0.02), dorsolateral prefrontal cortex (ηp2 = 0.01) and dorsal anterior/mid cingulate cortex (ηp2 = 0.01). These differences were driven by greater activation to the CS+ in AnxMDD versus MDD. LimitationsLimitations include the cross-sectional design, a scream US rather than shock and half the number of MDD as AnxMDD participants. ConclusionsAnxMDD showed a pattern of increased activation in regions identified with fear processing. Effects were consistently driven by threat, further suggesting fear signaling as the emergent target process. Differences emerged in regions associated with salience processing, attentional orienting/conflict, self-relevant processing and executive functioning in comorbid anxiety and depression, thereby highlighting potential treatment targets for this prevalent and treatment resistant group.