scholarly journals Adolescent brain development in normality and psychopathology

2013 ◽  
Vol 25 (4pt2) ◽  
pp. 1325-1345 ◽  
Author(s):  
Monica Luciana
Keyword(s):  
Brain Development ◽  
Sensation Seeking ◽  
Brain Activity ◽  
Activity Patterns ◽  
Theoretical Models ◽  
Brain Regions ◽  
Incentive Motivation ◽  
Prefrontal Function ◽  
Adolescent Risk ◽  
Adolescent Brain Development

AbstractSince this journal's inception, the field of adolescent brain development has flourished, as researchers have investigated the underpinnings of adolescent risk-taking behaviors. Explanations based on translational models initially attributed such behaviors to executive control deficiencies and poor frontal lobe function. This conclusion was bolstered by evidence that the prefrontal cortex and its interconnections are among the last brain regions to structurally and functionally mature. As substantial heterogeneity of prefrontal function was revealed, applications of neuroeconomic theory to adolescent development led to dual systems models of behavior. Current epidemiological trends, behavioral observations, and functional magnetic resonance imaging based brain activity patterns suggest a quadratic increase in limbically mediated incentive motivation from childhood to adolescence and a decline thereafter. This elevation occurs in the context of immature prefrontal function, so motivational strivings may be difficult to regulate. Theoretical models explain this patterning through brain-based accounts of subcortical–cortical integration, puberty-based models of adolescent sensation seeking, and neurochemical dynamics. Empirically sound tests of these mechanisms, as well as investigations of biology–context interactions, represent the field's most challenging future goals, so that applications to psychopathology can be refined and so that developmental cascades that incorporate neurobiological variables can be modeled.

scholarly journals Neural Correlates of Feedback Processing in Toddlers

2014 ◽  
Vol 26 (7) ◽  
pp. 1519-1527 ◽  
Author(s):  
Marlene Meyer ◽  
Harold Bekkering ◽  
Denise J. C. Janssen ◽  
Ellen R. A. de Bruijn ◽  
Sabine Hunnius
Keyword(s):  
Early Childhood ◽  
Brain Development ◽  
Brain Activity ◽  
Feedback System ◽  
Brain Regions ◽  
Adaptive Performance ◽  
Feedback Processing ◽  
Essential Information ◽  
Electrophysiological Studies ◽  
Neural Feedback

External feedback provides essential information for successful learning. Feedback is especially important for learning in early childhood, as toddlers strongly rely on external signals to determine the consequences of their actions. In adults, many electrophysiological studies have elucidated feedback processes using a neural marker called the feedback-related negativity (FRN). The neural generator of the FRN is assumed to be the ACC, located in medial frontal cortex. As frontal brain regions are the latest to mature during brain development, it is unclear when in early childhood a functional feedback system develops. Is feedback differentiated on a neural level in toddlers and in how far is neural feedback processing related to children's behavioral adjustment? In an EEG experiment, we addressed these questions by measuring the brain activity and behavioral performance of 2.5-year-old toddlers while they played a feedback-guided game on a touchscreen. Electrophysiological results show differential brain activity for feedback with a more negative deflection for incorrect than correct outcomes, resembling the adult FRN. This provides the first neural evidence for feedback processing in toddlers. Notably, FRN amplitudes were predictive of adaptive behavior: the stronger the differential brain activity for feedback, the better the toddlers' adaptive performance during the game. Thus, already in early childhood toddlers' feedback-guided performance directly relates to the functionality of their neural feedback processing. Implications for early feedback-based learning as well as structural and functional brain development are discussed.

scholarly journals Visual Number Beats Abstract Numerical Magnitude: Format-dependent Representation of Arabic Digits and Dot Patterns in Human Parietal Cortex

2015 ◽  
Vol 27 (7) ◽  
pp. 1376-1387 ◽  
Author(s):  
Jessica Bulthé ◽  
Bert De Smedt ◽  
Hans P. Op de Beeck
Keyword(s):  
Activity Pattern ◽  
Numerical Cognition ◽  
Brain Activity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Numerical Magnitude ◽  
Visual Objects ◽  
Arabic Digits ◽  
The One ◽  
Object Cognition

In numerical cognition, there is a well-known but contested hypothesis that proposes an abstract representation of numerical magnitude in human intraparietal sulcus (IPS). On the other hand, researchers of object cognition have suggested another hypothesis for brain activity in IPS during the processing of number, namely that this activity simply correlates with the number of visual objects or units that are perceived. We contrasted these two accounts by analyzing multivoxel activity patterns elicited by dot patterns and Arabic digits of different magnitudes while participants were explicitly processing the represented numerical magnitude. The activity pattern elicited by the digit “8” was more similar to the activity pattern elicited by one dot (with which the digit shares the number of visual units but not the magnitude) compared to the activity pattern elicited by eight dots, with which the digit shares the represented abstract numerical magnitude. A multivoxel pattern classifier trained to differentiate one dot from eight dots classified all Arabic digits in the one-dot pattern category, irrespective of the numerical magnitude symbolized by the digit. These results were consistently obtained for different digits in IPS, its subregions, and many other brain regions. As predicted from object cognition theories, the number of presented visual units forms the link between the parietal activation elicited by symbolic and nonsymbolic numbers. The current study is difficult to reconcile with the hypothesis that parietal activation elicited by numbers would reflect a format-independent representation of number.

scholarly journals FFA and OFA encode distinct types of face identity information

2020 ◽  
Author(s):  
Maria Tsantani ◽  
Nikolaus Kriegeskorte ◽  
Katherine Storrs ◽  
Adrian Lloyd Williams ◽  
Carolyn McGettigan ◽  
...  
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Image Encoding ◽  
Low Level ◽  
Social Traits ◽  
Face Area ◽  
Face Similarity ◽  
And Gender ◽  
High Level

AbstractFaces of different people elicit distinct functional MRI (fMRI) patterns in several face-selective brain regions. Here we used representational similarity analysis to investigate what type of identity-distinguishing information is encoded in three face-selective regions: fusiform face area (FFA), occipital face area (OFA), and posterior superior temporal sulcus (pSTS). We used fMRI to measure brain activity patterns elicited by naturalistic videos of famous face identities, and compared their representational distances in each region with models of the differences between identities. Models included low-level to high-level image-computable properties and complex human-rated properties. We found that the FFA representation reflected perceived face similarity, social traits, and gender, and was well accounted for by the OpenFace model (deep neural network, trained to cluster faces by identity). The OFA encoded low-level image-based properties (pixel-wise and Gabor-jet dissimilarities). Our results suggest that, although FFA and OFA can both discriminate between identities, the FFA representation is further removed from the image, encoding higher-level perceptual and social face information.

Discriminative Learning of Propagation and Spatial Pattern for Motor Imagery EEG Analysis

2013 ◽  
Vol 25 (10) ◽  
pp. 2709-2733 ◽  
Author(s):  
Xinyang Li ◽  
Haihong Zhang ◽  
Cuntai Guan ◽  
Sim Heng Ong ◽  
Kai Keng Ang ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Statistical Significance ◽  
Activity Patterns ◽  
Signal Propagation ◽  
Brain Regions ◽  
Computer Interface ◽  
Scalp Eeg

Effective learning and recovery of relevant source brain activity patterns is a major challenge to brain-computer interface using scalp EEG. Various spatial filtering solutions have been developed. Most current methods estimate an instantaneous demixing with the assumption of uncorrelatedness of the source signals. However, recent evidence in neuroscience suggests that multiple brain regions cooperate, especially during motor imagery, a major modality of brain activity for brain-computer interface. In this sense, methods that assume uncorrelatedness of the sources become inaccurate. Therefore, we are promoting a new methodology that considers both volume conduction effect and signal propagation between multiple brain regions. Specifically, we propose a novel discriminative algorithm for joint learning of propagation and spatial pattern with an iterative optimization solution. To validate the new methodology, we conduct experiments involving 16 healthy subjects and perform numerical analysis of the proposed algorithm for EEG classification in motor imagery brain-computer interface. Results from extensive analysis validate the effectiveness of the new methodology with high statistical significance.

scholarly journals Computational mechanisms for neural representation of words

2021 ◽  
Author(s):  
Ze Fu ◽  
Xiaosha Wang ◽  
Xiaoying Wang ◽  
Huichao Yang ◽  
Jiahuan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Activity Patterns ◽  
Explanatory Power ◽  
Word Meaning ◽  
Brain Regions ◽  
Neural Representation ◽  
Space Representation ◽  
Neural Representations

A critical way for humans to acquire, represent and communicate information is through language, yet the underlying computation mechanisms through which language contributes to our word meaning representations are poorly understood. We compared three major types of word computation mechanisms from large language corpus (simple co-occurrence, graph-space relations and neural-network-vector-embedding relations) in terms of the association of words’ brain activity patterns, measured by two functional magnetic resonance imaging (fMRI) experiments. Word relations derived from a graph-space representation, and not neural-network-vector-embedding, had unique explanatory power for the neural activity patterns in brain regions that have been shown to be particularly sensitive to language processes, including the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were robust across different window sizes and graph sizes and were relatively specific to language inputs. These findings highlight the role of cumulative language inputs in organizing word meaning neural representations and provide a mathematical model to explain how different brain regions capture different types of language-derived information.

scholarly journals Stimulation-specific information is represented as local activity patterns across the brain

2019 ◽  
Author(s):  
Amirouche Sadoun ◽  
Tushar Chauhan ◽  
Samir Mameri ◽  
Yifan Zhang ◽  
Pascal Barone ◽  
...  
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
Three Dimensional ◽  
Correlation Method ◽  
Brain Regions ◽  
Specific Information ◽  
Spatially Correlated ◽  
Face Area ◽  
Spatial Configurations ◽  
The Brain

AbstractModern neuroimaging represents three-dimensional brain activity, which varies across brain regions. It remains unknown whether activity within brain regions is organized in spatial configurations to reflect perceptual and cognitive processes. We developed a rotational cross-correlation method allowing a straightforward analysis of spatial activity patterns for the precise detection of the spatially correlated distributions of brain activity. Using several statistical approaches, we found that the seed patterns in the fusiform face area were robustly correlated to brain regions involved in face-specific representations. These regions differed from the non-specific visual network meaning that activity structure in the brain is locally preserved in stimulation-specific regions. Our findings indicate spatially correlated perceptual representations in cerebral activity and suggest that the 3D coding of the processed information is organized in locally preserved activity patterns. More generally, our results provide the first demonstration that information is represented and transmitted as local spatial configurations of brain activity.

scholarly journals Neural heterogeneity underlying late adolescent reward processing is linked to individual differences in behavioral sensation seeking.

2021 ◽  
Author(s):  
Michael I Demidenko ◽  
Edward Huntley ◽  
Alexander Samuel Weigard ◽  
Daniel Keating ◽  
Adriene M. Beltz
Keyword(s):  
Individual Differences ◽  
Sensation Seeking ◽  
A Priori ◽  
Network Connectivity ◽  
Brain Regions ◽  
Reward Processing ◽  
Multiple Model ◽  
Late Adolescent ◽  
Adolescent Risk ◽  
Brain Behavior

Adolescent risk-taking, including sensation seeking (SS), is often attributed to developmental changes in connectivity among brain regions implicated in cognitive control and reward processing. Despite considerable scientific and popular interest in this neurodevelopmental framework, there are few empirical investigations of adolescent network connectivity–let alone examinations of its links to SS behavior. The studies that have been done focus on mean-based approaches and leave unanswered questions about individual differences in neurodevelopment and behavior. The goal of this paper is to take a person-specific approach to the study of adolescent functional connectivity during reward processing, and to examine links between connectivity and self-reported SS behavior in 104 adolescents (MAge=19.3; SDAge=1.3). Using group iterative multiple model estimation (GIMME), person-specific connectivity during two neuroimaging runs of a monetary incentive delay task was estimated among 12 a priori brain regions of interest representing reward, cognitive, and salience networks. Two data-driven subgroups were detected, a finding that was consistent between both neuroimaging runs, but associations with SS were only found in the first run, potentially reflecting neural habituation in the second run. Specifically, the subgroup that had unique connections between reward-related regions had greater SS and showed a distinctive relation between connectivity strength in the reward network and SS. These findings provide novel evidence for heterogeneity in adolescent brain-behavior relations by showing that subsets of adolescents have unique associations between neural reward processing and SS. Findings have broader implications for future work on reward processing, as they demonstrate that brain-behavior relations may attenuate across runs.

scholarly journals Enhanced reinstatement of naturalistic event memories due to hippocampal-network-targeted stimulation

2020 ◽  
Author(s):  
Melissa Hebscher ◽  
James E. Kragel ◽  
Thorsten Kahnt ◽  
Joel L. Voss
Keyword(s):  
Specific Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Occipital Cortex ◽  
Brain Regions ◽  
Network Location ◽  
Hippocampal Activity ◽  
Spatially Distributed ◽  
Posterior Parietal ◽  
Hippocampal Network

AbstractEpisodic memory involves the reinstatement of distributed patterns of brain activity present when events were initially experienced. The hippocampus is thought to coordinate reinstatement via its interactions with a network of brain regions, but this hypothesis has not been causally tested in humans. The current study directly tested the involvement of the hippocampal network in reinstatement using network-targeted noninvasive stimulation. We measured reinstatement of multi-voxel patterns of fMRI activity during encoding and retrieval of naturalistic video clips depicting everyday activities. Reinstatement of video-specific activity patterns was robust in posterior-parietal and occipital areas previously implicated in event reinstatement. Theta-burst stimulation targeting the hippocampal network increased videospecific reinstatement of fMRI activity patterns in occipital cortex and improved memory accuracy relative to stimulation of a control out-of-network location. Furthermore, stimulation targeting the hippocampal network influenced the trial-by-trial relationship between hippocampal activity during encoding and later reinstatement in occipital cortex. These findings implicate the hippocampal network in the reinstatement of spatially distributed patterns of event-specific activity, and identify a role for the hippocampus in encoding complex naturalistic events that later undergo cortical reinstatement.

scholarly journals Trust me if you can – neurophysiological insights on the influence of consumer impulsiveness on trustworthiness evaluations in online settings

2018 ◽  
Vol 52 (1/2) ◽  
pp. 118-146 ◽  
Author(s):  
Marco Hubert ◽  
Mirja Hubert ◽  
Marc Linzmajer ◽  
René Riedl ◽  
Peter Kenning
Keyword(s):  
Decision Making ◽  
Brain Activity ◽  
Activity Patterns ◽  
Dorsal Striatum ◽  
Region Of Interest ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Neural Activation ◽  
Content Type ◽  
Brain Functions

Purpose The purpose of this study is to examine how consumer personality trait impulsiveness influences trustworthiness evaluations of online-offers with different trust-assuring and trust-reducing elements by measuring the brain activity of consumers. Shoppers with high degrees of impulsiveness are referred to as hedonic shoppers, and those with low degrees are referred to as prudent consumers. Design/methodology/approach To investigate the differences between neural processes in the brains of hedonic and prudent shoppers during the trustworthiness evaluation of online-offers, the present study used functional magnetic resonance imaging (fMRI) and region-of-interest analysis to correlate neural activity patterns with behavioral measures of the study participants. Findings Drawing upon literature reviews on the neural correlates of both trust in online settings and consumer impulsiveness and using an experimental design that links behavioral and fMRI data, the study shows that consumer impulsiveness can exert a significant influence on the evaluation of online-offers. With regard to brain activation, both groups (hedonic and prudent shoppers) exhibit similar neural activation tendencies, but differences exist in the magnitude of activation patterns in brain regions that are closely related to trust and impulsiveness such as the dorsal striatum, anterior cingulate, the dorsolateral prefrontal cortex and the insula cortex. Research limitations/implications The data provide evidence that consumers within the hedonic group evaluate online-offers differently with regard to their trustworthiness compared to the prudent group, and that these differences in evaluation are rooted in neural activation differences in the shoppers’ brains. Practical implications Marketers need to be made aware of the fact that neurological insights can be used for market segmentation, because consumers’ decision-making processes help explain behavioral outcomes (here, trustworthiness evaluations of online-offers). In addition, consumers can learn from an advanced understanding of their brain functions during decision-making and their relation to personal traits such as impulsiveness. Originality/value Considering the importance of trust in online shopping, as well as the fact that personality traits such as impulsiveness influence the purchase process to a high degree, this study is the first to systematically investigate the interplay of online trustworthiness perceptions and differences in consumer impulsiveness with neuroscientific methods.

Brain kernel: a new spatial covariance function for fMRI data

2021 ◽  
Author(s):  
Anqi Wu ◽  
Samuel A. Nastase ◽  
Christopher A Baldassano ◽  
Nicholas B Turk-Browne ◽  
Kenneth A. Norman ◽  
...  
Keyword(s):  
Covariance Function ◽  
Brain Activity ◽  
Activity Patterns ◽  
Bilateral Symmetry ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fmri Data ◽  
Spatial Covariance ◽  
Spatial Correlation Structure ◽  
The Brain

A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore the fact that correlations in neural activity may fall off at different rates in different brain areas, or exhibit discontinuities across anatomical or functional boundaries. Moreover, such methods do not exploit the fact that widely separated brain regions may exhibit strong correlations due to bilateral symmetry or the network organization of brain regions. To capture this non-stationary spatial correlation structure, we introduce the brain kernel, a continuous covariance function for whole-brain activity patterns. We define the brain kernel in terms of a continuous nonlinear mapping from 3D brain coordinates to a latent embedding space, parametrized with a Gaussian process (GP). The brain kernel specifies the prior covariance between voxels as a function of the distance between their locations in embedding space. The GP mapping warps the brain nonlinearly so that highly correlated voxels are close together in latent space, and uncorrelated voxels are far apart. We estimate the brain kernel using resting-state fMRI data, and we develop an exact, scalable inference method based on block coordinate descent to overcome the challenges of high dimensionality (10-100K voxels). Finally, we illustrate the brain kernel's usefulness with applications to brain decoding and factor analysis with multiple task-based fMRI datasets.

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