Individual Uniqueness in the Neonatal Functional Connectome

2021 ◽  
Author(s):  
Qiushi Wang ◽  
Yuehua Xu ◽  
Tengda Zhao ◽  
Zhilei Xu ◽  
Yong He ◽  
...  
Keyword(s):  
Individual Differences ◽  
Individual Identification ◽  
Imaging Data ◽  
Functional Connectome ◽  
Middle Range ◽  
Human Connectome Project ◽  
The Individual ◽  
And Behavior ◽  
Identification Analysis ◽  
The Brain

Abstract The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0–30 mm) and middle-range (30–60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

scholarly journals Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging

2019 ◽  
Author(s):  
Emily S. Finn ◽  
Enrico Glerean ◽  
Arman Y. Khojandi ◽  
Dylan Nielson ◽  
Peter J. Molfese ◽  
...  
Keyword(s):  
Individual Differences ◽  
Brain Activity ◽  
Future Research ◽  
Imaging Data ◽  
Stimulus Choice ◽  
Human Connectome Project ◽  
Single Subjects ◽  
Using Data ◽  
The Individual ◽  
Empirical Demonstration

Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for naturalistic imaging data are geared toward detecting shared responses rather than between-subject variability. Here, we review recent work using naturalistic stimuli to study individual differences, and advance a framework for detecting structure in idiosyncratic patterns of brain activity, or “idiosynchrony”. Specifically, we outline the emerging technique of inter-subject representational similarity analysis (IS-RSA), including its theoretical motivation and an empirical demonstration of how it recovers brain-behavior relationships during movie watching using data from the Human Connectome Project. We also consider how stimulus choice may affect the individual signal and discuss areas for future research. We argue that naturalistic neuroimaging paradigms have the potential to reveal meaningful individual differences above and beyond those observed during traditional tasks or at rest.

scholarly journals Selective enhancement of object representations through multisensory integration

2019 ◽  
Author(s):  
David A. Tovar ◽  
Micah M. Murray ◽  
Mark T. Wallace
Keyword(s):  
Multisensory Integration ◽  
Sensory Information ◽  
Stimulus Presentation ◽  
Building Blocks ◽  
Object Representations ◽  
The Individual ◽  
And Behavior ◽  
Living Objects ◽  
Selective Enhancement ◽  
The Brain

AbstractObjects are the fundamental building blocks of how we create a representation of the external world. One major distinction amongst objects is between those that are animate versus inanimate. Many objects are specified by more than a single sense, yet the nature by which multisensory objects are represented by the brain remains poorly understood. Using representational similarity analysis of human EEG signals, we show enhanced encoding of audiovisual objects when compared to their corresponding visual and auditory objects. Surprisingly, we discovered the often-found processing advantages for animate objects was not evident in a multisensory context due to greater neural enhancement of inanimate objects—the more weakly encoded objects under unisensory conditions. Further analysis showed that the selective enhancement of inanimate audiovisual objects corresponded with an increase in shared representations across brain areas, suggesting that neural enhancement was mediated by multisensory integration. Moreover, a distance-to-bound analysis provided critical links between neural findings and behavior. Improvements in neural decoding at the individual exemplar level for audiovisual inanimate objects predicted reaction time differences between multisensory and unisensory presentations during a go/no-go animate categorization task. Interestingly, links between neural activity and behavioral measures were most prominent 100 to 200ms and 350 to 500ms after stimulus presentation, corresponding to time periods associated with sensory evidence accumulation and decision-making, respectively. Collectively, these findings provide key insights into a fundamental process the brain uses to maximize information it captures across sensory systems to perform object recognition.Significance StatementOur world is filled with an ever-changing milieu of sensory information that we are able to seamlessly transform into meaningful perceptual experience. We accomplish this feat by combining different features from our senses to construct objects. However, despite the fact that our senses do not work in isolation but rather in concert with each other, little is known about how the brain combines the senses together to form object representations. Here, we used EEG and machine learning to study how the brain processes auditory, visual, and audiovisual objects. Surprisingly, we found that non-living objects, the objects which were more difficult to process with one sense alone, benefited the most from engaging multiple senses.

Visual Perception in Specific Learning Difficulties

2021 ◽  
Vol 1 (1) ◽  
pp. 25-40
Author(s):  
Neriman Aral
Keyword(s):  
Individual Differences ◽  
Visual Perception ◽  
Learning Difficulties ◽  
Learning Difficulty ◽  
The Family ◽  
Mental Problems ◽  
The Moment ◽  
The Individual ◽  
The Brain ◽  
Specific Learning

From the moment the child is born, learning becomes meaningful and it is interpreted as a result of the experiences first in the family and then in school. However, it is sometimes not possible to talk about the fact that learning takes place in all children although the process has taken place in this direction. Sometimes the individual differences that exist in children and the inability to get the necessary support in structuring their learning experiences can be effective in the failure of learning, while sometimes the type of congenital difficulty can be effective. One of these types of difficulty is a specific learning difficulty. It is not always possible for children with specific learning difficulties to learn, even if they do not have any mental problems. In this case, many factors can be effective, especially the problems that children experience in their visual perception can become effective. Since visual perception is the processing of symbols received from the environment in the brain, the problem that may be experienced in this process can also make it difficult to learn this situation. In line with these considerations, it is aimed to focus on the importance of visual perception in specific learning difficulties.

The Tell-Tale Hand

2007 ◽  
Vol 28 (2) ◽  
pp. 88-97 ◽  
Author(s):  
Martin Voracek ◽  
Stefanie Pavlovic
Keyword(s):  
Individual Differences ◽  
Statistical Significance ◽  
Digit Ratio ◽  
Future Research ◽  
Fourth Digit ◽  
Brain And Behavior ◽  
And Behavior ◽  
The Brain ◽  
Intense Research ◽  
Anatomical Trait

Abstract. The second-to-fourth digit ratio (2D:4D), an inconspicuous, but sexually differentiated anatomical trait (men present lower 2D:4D than women), has received intense research interest recently. Fairly strong evidence points to 2D:4D as a biomarker for the organizational (permanent) effects of prenatal testosterone on the brain and behavior. 2D:4D has been shown to be a correlate of a wealth of sex-dependent, hormonally influenced traits and phenotypes, which reach into the domains of behavior, fertility, health, physique, sexuality, and sports and also deeply into differential psychology (ability, cognition, and personality). This study investigated whether individual differences in 2D:4D are related to individual differences in attractiveness, sex typicality, and other attributes ascribed to palm images by raters. For both sexes, more sex-atypical trait expressions (i.e., higher 2D:4D in male, but lower 2D:4D in female palm specimens) were related to higher aggregate ratings of attractiveness, healthiness, sexiness, imagined handshake pleasantness, and imagined person dominance, albeit only the last association achieved formal statistical significance with two-tailed testing. These findings suggest that 2D:4D might be a correlate of perceived dominance and possibly also of other attributes. Digit ratio associations with sex-typicality ratings (sex-of-hand judgments and perceived palm masculinity and femininity) were inconsistent and mostly of smaller size. Finger lengths (2D and 4D) were generally more strongly and consistently related to palm attributes than 2D:4D was. Implications of the findings, study limitations, and directions for future research are considered.

scholarly journals Robust prediction of individual personality from brain functional connectome

2020 ◽  
Vol 15 (3) ◽  
pp. 359-369 ◽  
Author(s):  
Huanhuan Cai ◽  
Jiajia Zhu ◽  
Yongqiang Yu
Keyword(s):  
Personality Traits ◽  
Large Scale ◽  
Five Factor Model ◽  
Brain Regions ◽  
Individual Variability ◽  
Personality Factors ◽  
Imaging Features ◽  
Imaging Data ◽  
Functional Connectome ◽  
Human Connectome Project

Abstract Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were used to construct large-scale brain networks. Personality traits of the five-factor model (FFM) were assessed by the NEO Five Factor Inventory. We found that CPM successfully and reliably predicted all the FFM personality factors (agreeableness, openness, conscientiousness and neuroticism) other than extraversion in novel individuals. At the neural level, we found that the personality-associated functional networks mainly included brain regions within default mode, frontoparietal executive control, visual and cerebellar systems. Although different feature selection thresholds and parcellation strategies did not significantly influence the prediction results, some findings lost significance after controlling for confounds including age, gender, intelligence and head motion. Our finding of robust personality prediction from an individual’s unique functional connectome may help advance the translation of ‘brain connectivity fingerprinting’ into real-world personality psychological settings.

scholarly journals Do you see the “face”? Individual differences in face pareidolia

2020 ◽  
Vol 14 ◽  
Author(s):  
Liu-Fang Zhou ◽  
Ming Meng
Keyword(s):  
Sex Differences ◽  
Individual Differences ◽  
Personality Traits ◽  
Face Perception ◽  
Clinical Applications ◽  
Neural Mechanisms ◽  
Developmental Factors ◽  
The Face ◽  
The Individual ◽  
The Brain

Abstract People tend to see faces from non-face objects or meaningless patterns. Such illusory face perception is called face pareidolia. Previous studies have revealed an interesting fact that there are huge individual differences in face pareidolia experience among the population. Here, we review previous findings on individual differences in face pareidolia experience from four categories: sex differences, developmental factors, personality traits and neurodevelopmental factors. We further discuss underlying cognitive or neural mechanisms to explain why some perceive the objects as faces while others do not. The individual differences in face pareidolia could not only offer scientific insights on how the brain works to process face information, but also suggest potential clinical applications.

scholarly journals A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome

2020 ◽  
Author(s):  
Kwangsun Yoo ◽  
Monica D. Rosenberg ◽  
Young Hye Kwon ◽  
Dustin Scheinost ◽  
Robert T Constable ◽  
...  
Keyword(s):  
Functional Organization ◽  
Transformation Model ◽  
Cognitive State ◽  
Imaging Data ◽  
State Transformation ◽  
Cognitive States ◽  
Brain Connectome ◽  
Human Connectome Project ◽  
Task Goals ◽  
The Brain

The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model changes in the brain's functional organization. Here, we present a novel connectome-to-connectome (C2C) state transformation framework that enables us to model the brain's functional reorganization in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-specific connectomes from their task-free connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions. Finally, the C2C model reveals how the connectome reorganizes between cognitive states. Previous studies have reported that task-induced modulation of the brain connectome is domain-specific as well as domain-general, but did not specify how brain systems reconfigure to specific cognitive states. Our observations support the existence of reliable state-specific systems in the brain and indicate that we can quantitatively describe patterns of brain reorganization, common across individuals, in a computational model.

scholarly journals Generalizable predictive modeling of semantic processing ability from functional brain connectivity

2021 ◽  
Author(s):  
Danting Meng ◽  
Suiping Wang ◽  
Patrick Wong ◽  
Gangyi Feng
Keyword(s):  
Individual Differences ◽  
Predictive Modeling ◽  
Semantic Processing ◽  
Brain Connectivity ◽  
Brain Network ◽  
Conceptual Information ◽  
Human Connectome Project ◽  
Unseen Data ◽  
Out Of Sample ◽  
The Individual

Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating meaningful and conceptual information. Neuroimaging studies of SP typically collapse data from many subjects, but both its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural organizations contribute to the individual differences in SP behaviors. Here we aim to identify the neural signatures underlying SP variabilities by analyzing individual functional connectivity (FC) patterns based on a large-sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two-stage predictive modeling approach to build an internally cross-validated model and to test the model's generalizability with unseen data from different HCP sub-populations and task states as well as other out-of-sample datasets that are independent of the HCP. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five semantic tasks. This cross-validated predictive model can be used to predict unseen HCP data. The model generalizability was enhanced with FCs in language tasks than resting state and other task states and was better for females than males. The model constructed from the HCP dataset can be generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out-of-sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education and improve intervention practice for patients with SP and language deficits.

scholarly journals Individual Differences in Brain Responses: New Opportunities for Tailoring Health Communication Campaigns

2020 ◽  
Vol 14 ◽  
Author(s):  
Richard Huskey ◽  
Benjamin O. Turner ◽  
René Weber
Keyword(s):  
Individual Differences ◽  
Brain Activity ◽  
Brain Regions ◽  
Future Research ◽  
Message Processing ◽  
Response Patterns ◽  
Message Tailoring ◽  
Brain Responses ◽  
And Behavior ◽  
The Brain

Prevention neuroscience investigates the brain basis of attitude and behavior change. Over the years, an increasingly structurally and functionally resolved “persuasion network” has emerged. However, current studies have only identified a small handful of neural structures that are commonly recruited during persuasive message processing, and the extent to which these (and other) structures are sensitive to numerous individual difference factors remains largely unknown. In this project we apply a multi-dimensional similarity-based individual differences analysis to explore which individual factors—including characteristics of messages and target audiences—drive patterns of brain activity to be more or less similar across individuals encountering the same anti-drug public service announcements (PSAs). We demonstrate that several ensembles of brain regions show response patterns that are driven by a variety of unique factors. These results are discussed in terms of their implications for neural models of persuasion, prevention neuroscience and message tailoring, and methodological implications for future research.

Sensitive Periods in the Development of the Brain and Behavior

2004 ◽  
Vol 16 (8) ◽  
pp. 1412-1425 ◽  
Author(s):  
Eric I. Knudsen
Keyword(s):  
Neural Circuits ◽  
Neural Circuit ◽  
Critical Role ◽  
Sensitive Period ◽  
Critical Periods ◽  
Sensitive Periods ◽  
Brain And Behavior ◽  
The Individual ◽  
And Behavior ◽  
The Brain

Experience exerts a profound influence on the brain and, therefore, on behavior. When the effect of experience on the brain is particularly strong during a limited period in development, this period is referred to as a sensitive period. Such periods allow experience to instruct neural circuits to process or represent information in a way that is adaptive for the individual. When experience provides information that is essential for normal development and alters performance permanently, such sensitive periods are referred to as critical periods. Although sensitive periods are reflected in behavior, they are actually a property of neural circuits. Mechanisms of plasticity at the circuit level are discussed that have been shown to operate during sensitive periods. A hypothesis is proposed that experience during a sensitive period modifies the architecture of a circuit in fundamental ways, causing certain patterns of connectivity to become highly stable and, therefore, energetically preferred. Plasticity that occurs beyond the end of a sensitive period, which is substantial in many circuits, alters connectivity patterns within the architectural constraints established during the sensitive period. Preferences in a circuit that result from experience during sensitive periods are illustrated graphically as changes in a “stability landscape,” a metaphor that represents the relative contributions of genetic and experiential influences in shaping the information processing capabilities of a neural circuit. By understanding sensitive periods at the circuit level, as well as understanding the relationship between circuit properties and behavior, we gain a deeper insight into the critical role that experience plays in shaping the development of the brain and behavior.

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