A unique role for the human amygdala in novelty detection.
Ontology highlight
ABSTRACT:
SUBMITTER: Blackford JU
PROVIDER: S-EPMC2830341 | biostudies-literature | 2010 Apr
REPOSITORIES: biostudies-literature
Publications
A unique role for the human amygdala in novelty detection.
Blackford Jennifer Urbano JU Buckholtz Joshua W JW Avery Suzanne N SN Zald David H DH
NeuroImage 20100104 3
Previous research indicates that the amygdala and hippocampus are sensitive to novelty; however, two types of novelty can be distinguished - stimuli that are ordinary, but novel in the current context, and stimuli that are unusual. Using functional magnetic resonance imaging, we examined blood oxygen dependent level (BOLD) response of the human amygdala and hippocampus to novel, commonly seen objects versus novel unusual objects. When presented with the novel common stimuli, the BOLD signal incr ...[more]
Similar Datasets
Project description:The primary focus of research on the amygdala has been on the detection of and response to emotion but the amygdala also sometimes responds to new or unexpected stimuli without specific emotional content. Very little is currently known about why the amygdala responds to some new stimuli but not to others. Here we investigated the conditions that are necessary and sufficient for the expression of novelty specific amygdala responses by presenting novel and repeated images to human participants and varying the content of these images while measuring blood-oxygenation level dependent (BOLD) responses. In Experiment 1 we presented novel and repeated emotional and neutral images. Both emotional and neutral images of humans evoked more amygdala activity when novel than when repeated. In Experiment 2 we presented novel and repeated images of humans and scenes. Images of humans but not scenes evoked more amygdala activity when novel than when repeated. Our results suggest that the amygdala plays a stimulus-specific role in the brain's novelty detection network. Surprisingly, emotion was not necessary for amygdalar novelty responses, but the presence of a human representation was important. Amygdala responses evoked by novel faces may reflect our need to use others' faces as clues for important events in the environment.
| S-EPMC3062695 | biostudies-other
Project description:"Novelty-seeking" behavior describes the variability of rats' locomotor response, namely high and low responders (HR and LR respectively), when exposed to a novel environment. Novelty-seeking in the rat is considered to model "sensation-seeking" in humans, a personality trait related to substance abuse. It is assumed that HR rats and LR rats differ in their emotional reactivity because of the disparate incentive value of contextual stimulus, thus differentially interacting with their environment. However, little is known about how HR and LR rats recognize novelty arising from the environment. The present study evaluates whether phenotype may affect spontaneous, non-spatial novelty discrimination. Selectively bred HR and LR rats were submitted to the novel-object recognition test. The task involved a delay of 3h after a first encounter with an object ("old"), which had to be discriminated from a second object ("new"). Object discrimination was assessed minute-by-minute during a 3-min choice session. Amnesic effects of scopolamine (0.5mg/kg, intraperitoneal) were also analyzed. HR-bred rats showed sustained novel-object recognition throughout the 3-min choice session, whereas LR-bred rats began to discriminate between objects only in the last minute. Surprisingly, level of discrimination in scopolamine-treated HR-bred rats was significant during the first minute of the choice test and diminished thereafter, presumably because both objects became equally familiar as they were explored. Additionally, scopolamine induced changes in muscarine M(2) receptor gene expression in a phenotype-dependent manner. Because consistent object discrimination mainly arises during the first minute, these findings may reflect differential novelty detection in HR-bred respect to LR-bred rats.
| S-EPMC3087883 | biostudies-literature
Project description:Novelty detection is a fundamental biological problem that organisms must solve to determine whether a given stimulus departs from those previously experienced. In computer science, this problem is solved efficiently using a data structure called a Bloom filter. We found that the fruit fly olfactory circuit evolved a variant of a Bloom filter to assess the novelty of odors. Compared with a traditional Bloom filter, the fly adjusts novelty responses based on two additional features: the similarity of an odor to previously experienced odors and the time elapsed since the odor was last experienced. We elaborate and validate a framework to predict novelty responses of fruit flies to given pairs of odors. We also translate insights from the fly circuit to develop a class of distance- and time-sensitive Bloom filters that outperform prior filters when evaluated on several biological and computational datasets. Overall, our work illuminates the algorithmic basis of an important neurobiological problem and offers strategies for novelty detection in computational systems.
| S-EPMC6304992 | biostudies-literature
Project description:The lateral septum (LS) is a basal forebrain GABAergic region that is implicated in social novelty. However, the neural circuits and cell signaling pathways that converge on the LS to mediate social behaviors aren't well understood. Multiple lines of evidence suggest that signaling of brain-derived neurotrophic factor (BDNF) through its receptor TrkB plays important roles in social behavior. BDNF is not locally produced in LS, but we demonstrate that nearly all LS GABAergic neurons express TrkB. Local TrkB knock-down in LS neurons decreased social novelty recognition and reduced recruitment of neural activity in LS neurons in response to social novelty. Since BDNF is not synthesized in LS, we investigated which inputs to LS could serve as potential BDNF sources for controlling social novelty recognition. We demonstrate that selectively ablating inputs to LS from the basolateral amygdala (BLA), but not from ventral CA1 (vCA1), impairs social novelty recognition. Moreover, depleting BDNF selectively in BLA-LS projection neurons phenocopied the decrease in social novelty recognition caused by either local LS TrkB knockdown or ablation of BLA-LS inputs. These data support the hypothesis that BLA-LS projection neurons serve as a critical source of BDNF for activating TrkB signaling in LS neurons to control social novelty recognition.
| S-EPMC9852457 | biostudies-literature
Project description:Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. In this study, EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm (Novelty-MDM). Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity-specificity ROC curve. This evaluation compares three different EEG normalization methods, namely a fixed baseline buffer (FB), the median decaying memory (MDM) approach, and our novelty median decaying memory (Novelty-MDM) method. It is demonstrated that MDM did not improve overall performance compared to FB (p < 0.27), partly because seizure like episodes were included in the baseline. More importantly, Novelty-MDM significantly outperforms both FB (p = 0.015) and MDM (p = 0.0065).
| S-EPMC5104774 | biostudies-literature
Project description:Freshwater sponges (Spongillida) are a unique lineage of demosponges that secondarily colonized lakes and rivers and are now found ubiquitously in these ecosystems. They developed specific adaptations to freshwater systems, including the ability to survive extreme thermal ranges, long-lasting dessication, anoxia, and resistance to a variety of pollutants. While spongillids have colonized all freshwater systems, the family Lubomirskiidae is endemic to Lake Baikal, and plays a range of key roles in this ecosystem. Our work compares the genomic content and microbiome of individuals of three species of the Lubomirskiidae, providing hypotheses for how molecular evolution has allowed them to adapt to their unique environments. We have sequenced deep (>92% of the metazoan 'Benchmarking Universal Single-Copy Orthologs' (BUSCO) set) transcriptomes from three species of Lubomirskiidae and a draft genome resource for Lubomirskia baikalensis. We note Baikal sponges contain unicellular algal and bacterial symbionts, as well as the dinoflagellate Gyrodinium. We investigated molecular evolution, gene duplication and novelty in freshwater sponges compared to marine lineages. Sixty one orthogroups have consilient evidence of positive selection. Transporters (e.g. zinc transporter-2), transcription factors (aristaless-related homeobox) and structural proteins (for example actin-3), alongside other genes, are under strong evolutionary pressure in freshwater, with duplication driving novelty across the Spongillida, but especially in the Lubomirskiidae. This addition to knowledge of freshwater sponge genetics provides a range of tools for understanding the molecular biology and, in the future, the ecology (for example, colonization and migration patterns) of these key species.
| S-EPMC6805232 | biostudies-literature
Project description:Novelty detection is a core feature of behavioral adaptation and involves cascades of neuronal responses-from initial evaluation of the stimulus to the encoding of new representations-resulting in the behavioral ability to respond to unexpected inputs. In the past decade, a new important novelty detection feature, beta2 (~20-30 Hz) oscillations, has been described in the hippocampus (HC). However, the interactions between beta2 and the hippocampal network are unknown, as well as the role-or even the presence-of beta2 in other areas involved with novelty detection. In this work, we combined multisite local field potential (LFP) recordings with novelty-related behavioral tasks in mice to describe the oscillatory dynamics associated with novelty detection in the CA1 region of the HC, parietal cortex, and mid-prefrontal cortex. We found that transient beta2 power increases were observed only during interaction with novel contexts and objects, but not with familiar contexts and objects. Also, robust theta-gamma phase-amplitude coupling was observed during the exploration of novel environments. Surprisingly, bursts of beta2 power had strong coupling with the phase of delta-range oscillations. Finally, the parietal and mid-frontal cortices had strong coherence with the HC in both theta and beta2. These results highlight the importance of beta2 oscillations in a larger hippocampal-cortical circuit, suggesting that beta2 plays a role in the mechanism for detecting and modulating behavioral adaptation to novelty.
| S-EPMC7921172 | biostudies-literature
Project description:Memory for odors is believed to be longer-lasting than memory for visual stimuli, as is evidenced by flat forgetting curves. However, performance on memory tasks is typically weaker in olfaction than vision. Studies of odor memory that use forced-choice methods confound responses that are a result of a trace memory and responses that can be obtained through process of elimination. Moreover, odor memory is typically measured with common stimuli, which are more familiar and responses may be confounded by verbal memory, and measure memory in intentional learning conditions, which are ecologically questionable. Here we demonstrate the value of using tests of memory in which hit rate and correct rejection rate are evaluated separately (i.e., not using forced-choice methods) and uncommon stimuli are used. This study compared memory for common and uncommon odors and pictures that were learned either intentionally (Exp. 1) or incidentally (Exp. 2) and tested with either a forced-choice or a one-stimulus-at-a-time ("monadic") recognition task after delays of 15 min, 48 h or 1 week. As expected, memory declined with delay in most conditions, but depended upon the particular measure of memory and was better for pictures than odors and for common than uncommon stimuli. For common odors, hit rates decreased with delay but correct rejection rates remained constant with delay. For common pictures, we found the opposite result, constant hit rates and decreased correct rejection rates. Our results support the 'misfit theory of conscious olfactory perception', which highlights the importance of the detection of novelty in olfactory memory and suggests that olfactory memory should be studied using more ecologically valid methods.
| S-EPMC8470488 | biostudies-literature
Project description:Background: To develop a tool for assessing normalcy of the thoracic aorta (TA) by echocardiography, based on either a linear regression model (Z-score), or a machine learning technique, namely one-class support vector machine (OC-SVM) (Q-score). Methods: TA diameters were measured in 1112 prospectively enrolled healthy subjects, aging 5 to 89 years. Considering sex, age and body surface area we developed two calculators based on the traditional Z-score and the novel Q-score. The calculators were compared in 198 adults with TA > 40 mm, and in 466 patients affected by either Marfan syndrome or bicuspid aortic valve (BAV). Results: Q-score attained a better Area Under the Curve (0.989; 95% CI 0.984-0.993, sensitivity = 97.5%, specificity = 95.4%) than Z-score (0.955; 95% CI 0.942-0.967, sensitivity = 81.3%, specificity = 93.3%; p < 0.0001) in patients with TA > 40 mm. The prevalence of TA dilatation in Marfan and BAV patients was higher as Z-score > 2 than as Q-score < 4% (73.4% vs. 50.09%, p < 0.00001). Conclusions: Q-score is a novel tool for assessing TA normalcy based on a model requiring less assumptions about the distribution of the relevant variables. Notably, diameters do not need to depend linearly on anthropometric measurements. Additionally, Q-score can capture the joint distribution of these variables with all four diameters simultaneously, thus accounting for the overall aortic shape. This approach results in a lower rate of predicted TA abnormalcy in patients at risk of TA aneurysm. Further prognostic studies will be necessary for assessing the relative effectiveness of Q-score versus Z-score.
| S-EPMC7912952 | biostudies-literature
Project description:Complex mixtures containing natural products are still an interesting source of novel drug candidates. High content screening (HCS) is a popular tool to screen for such. In particular, multiplexed HCS assays promise comprehensive bioactivity profiles, but generate also high amounts of data. Yet, only some machine learning (ML) applications for data analysis are available and these usually require a profound knowledge of the underlying cell biology. Unfortunately, there are no applications that simply predict if samples are biologically active or not (any kind of bioactivity). Within this work, we benchmark ML algorithms for binary classification, starting with classical ML models, which are the standard classifiers of the scikit-learn library or ensemble models of these classifiers (a total of 92 models tested). Followed by a partial least square regression (PLSR)-based classification (44 tested models in total) and simple artificial neural networks (ANNs) with dense layers (72 tested models in total). In addition, a novelty detection (ND) was examined, which is supposed to handle unknown patterns. For the final analysis the models, with and without upstream ND, were tested with two independent data sets. In our analysis, a stacking model, an ensamble model of class ML algorithms, performed best to predict new and unknown data. ND improved the predictions of the models and was useful to handle unknown patterns. Importantly, the classifier presented here can be easily rebuilt and be adapted to the data and demands of other groups. The hit detector (ND + stacking model) is universal and suitable for a broader application to support the search for new drug candidates.
| S-EPMC9530837 | biostudies-literature