Search bioRxiv⌕ Search

Biology subjects

Peraza, J. A.

Publications and source records attributed to Peraza, J. A..

12 recordsLinked to original sources

Human white matter tracts aligned with canonical functional brain networks

White matter tracts form the structural backbone of large-scale brain networks, yet their relation to functional organization remains poorly defined. Although major pathways are well characterized anatomically, their relationship to distributed cognitive systems has not been systematically established. Here, we constructed a population-level white matter tract termination atlas and projected tract endpoints onto the cortical surface to characterize their spatial organization. We integrated this atlas with large-scale meta-analytic decoding to derive functional profiles for individual tracts. Functional decoding revealed that white matter tracts exhibited distinct and biologically interpretable cognitive signatures. Hierarchical clustering of these profiles further showed that tracts organize into coherent ensembles defined by shared functional associations. These tract ensembles recapitulated canonical intrinsic brain networks across multiple cortical atlases, including a notable ensemble that demonstrated alignment with the default mode, salience, and frontoparietal control networks, corresponding to the core architecture of the triple-network model of cognitive control. This finding identified a candidate structural backbone linking distributed functional systems implicated across neuropsychiatric conditions. Together, these results demonstrate that white matter architecture is organized according to large-scale functional principles and establish a tract-to-network framework for linking structural connectivity to cognition.

neuroscience↗

Hippocampal functional connectivity changes associated with active and lecture-based physics learning

Introductory university physics courses often face the dual challenge of introducing students to new physics concepts while also addressing their preconceived notions that conflict with scientific principles. Active learning pedagogical approaches, which employ constructivist principles and emphasize active participation in the learning process, have been shown to be effective in teaching complex physics concepts. However, while the behavioral effects of constructivist methodologies are largely understood, the neurobiological underpinnings that facilitate this process remain unclear. Using functional magnetic resonance imaging (fMRI), we assessed students enrolled in either an active learning or lecture-based physics course before and after a 15-week semester of learning and examined changes in hippocampal whole-brain connectivity. We focused on the hippocampus given its critical role in learning and memory. Our findings revealed that hippocampal connectivity with brain regions in the frontal and parietal lobes decreased over time, regardless of instructional approach. Results also indicated that active learning students exhibited increased hippocampal connectivity with parietal, cerebellar, and frontal regions, reflecting experiential learning based on constructivist principles, whereas lecture-based students exhibited increased hippocampal connectivity with the fusiform gyrus, suggesting learning through passive observation. Our findings demonstrate that while some aspects of hippocampal functional connectivity may decrease over time, active vs. passive learning may preferentially enhance hippocampal connectivity during physics learning.

neuroscience↗

Ethnic Identity Profiles Among Adolescents in the ABCD Study: Associations with Resting State Functional Connectivity and Perceived Discrimination

Ethnic identity refers to how individuals perceive and experience themselves in the context of social groups, racial background, or culture (Phinney & Ong., 2007). Ethnic identity is positively associated with psychological well-being (Rivas-Drake et al., 2014) and negatively associated with depression and anxiety (Forstmeier et al., 2021). Those with strong ethnic identity may display resiliency to the negative effects of discrimination on psychological well-being (Urzua et al., 2021). Phinneys model describes four profiles for how people put effort into, participate in, and reflect upon their ethnic identity (Phinney, 1989). Despite prior work addressing ethnic identity and psychosocial outcomes (for review, see Meca et al., 2023), few studies have considered its neurobiological underpinnings. In the current study, we identified profiles of ethnic identity among participants in The Adolescent Brain Cognitive Development Study (ABCD Study) using latent profile analysis. Next, we examined resting state functional connectivity differences across observed profiles and assessed the moderating effects of perceived discrimination. Results indicated heightened cingulo-parietal (CPAR) network connectivity among adolescents with highly diffuse ethnic identities; among moderately achieved ethnic identities, perceived discrimination moderated the association between ethnic identity and CPAR connectivity. We discuss how these findings may be related to attentional shift, error monitoring, autobiographical memory, and social judgements.

neuroscience↗

NiCLIP: Neuroimaging contrastive language-image pretraining model for predicting text from brain activation images

Predicting cognitive processes from brain activation maps has remained an open question within the neuroscience community for many years. Meta-analytic functional decoding methods aim to tackle this issue by providing a quantitative estimation of behavioral profiles associated with specific brain regions. Existing methods face intrinsic challenges in neuroimaging meta-analysis, particularly in consolidating textual information from publications, as they rely on limited metrics that do not capture the semantic context of the text. The combination of large language models (LLMs) with advanced deep contrastive learning models (e.g., CLIP) for aligning text with images has revolutionized neuroimaging meta-analysis, potentially offering solutions to functional decoding challenges. In this work, we present NiCLIP, a contrastive language-image pretrained model that predicts cognitive tasks, concepts, and domains from brain activation patterns. We leveraged over 23,000 neuroscientific articles to train a CLIP model for text-to-brain association. Evaluation of NiCLIP predictions revealed that performance is optimized when using full-text articles instead of abstracts, as well as a curated cognitive ontology with precise task-concept-domain mappings. Furthermore, domain-specific fine-tuned LLMs (e.g., BrainGPT models) show numerically similar performance to their base LLM counterparts. Our results indicated that NiCLIP accurately predicts cognitive tasks from group-level activation maps provided by the Human Connectome Project across multiple domains (e.g., emotion, language, motor) and precisely characterizes the functional roles of specific brain regions, including the amygdala, hippocampus, and temporoparietal junction. However, NiCLIP showed limitations with noisy subject-level activation maps. NiCLIP represents a significant advancement in quantitative functional decoding for neuroimaging, offering researchers a powerful tool for hypothesis generation and scientific discovery.

neuroscience↗

Habenula alterations in resting state functional connectivity among autistic individuals

BackgroundThe reward-based theoretical framework of autism suggests that altered reward circuitry contributes to core symptoms. Recent prior research has revealed autism-related structural alterations in the habenula, a small epithalamic structure associated with motivation and emotion; however, potential alterations in functional connectivity (FC) remain unexplored. MethodsAnatomical and resting state functional magnetic resonance imaging (rs-fMRI) data were accessed for 1,479 participants (N=661 autism; agem: 16.68{+/-}8.23 years) in the Autism Brain Imaging Data Exchange (ABIDE). To investigate habenula alterations, we conducted a whole-brain resting state FC analysis using manually delineated subject-specific seeds, followed by regression analyses to explore age and brain-behavior interactions. ResultsAcross the entire sample, extensive habenula connectivity was observed within the midbrain dopaminergic reward system. Compared to neurotypical (NT) controls, autistic participants exhibited significantly increased habenular connectivity with the bilateral middle and superior temporal gyri. From childhood to early adulthood, autistic adolescents displayed an accelerated developmental habenula FC trajectory than NTs with the cingulate gyrus. Between groups, habenula hyperconnectivity was inversely associated with behavioral scores for social motivation and communication. ConclusionsThis study provides novel evidence of habenula connectivity alterations in autism, highlighting atypical FC with sensory processing regions. Further findings suggest that habenula circuitry develops differently among autistic adolescents, with links between habenula hyperconnectivity and social behaviors. Taken together, these results contribute to emerging evidence that the dopaminergic reward system may play a critical role in the pathophysiology of autism.

neuroscience↗

Advancing image-based meta-analysis for fMRI: A framework for leveraging NeuroVault data

Image-based meta-analysis (IBMA) is a powerful method for synthesizing results from various fMRI studies. However, challenges related to data accessibility and the lack of available tools and methods have limited its widespread use. This study examined the current state of the NeuroVault repository and developed a comprehensive framework for selecting and analyzing neuroimaging statistical maps within it. By systematically assessing the quality of NeuroVault's data and implementing novel selection and meta-analysis techniques, we demonstrated the repository's potential for IBMA. We created a multi-stage selection framework that includes preliminary, heuristic, and manual image selection methods. We conducted meta-analyses for three distinct domains: working memory, motor, and emotion processing. The results from the three manual IBMA approaches closely resembled reference maps from the Human Connectome Project. Importantly, we found that while manual selection provides the most precise results, heuristic methods can serve as robust alternatives, especially for domains that include a heterogeneous set of fMRI tasks and contrasts, such as emotion processing. Additionally, we evaluated five different meta-analytic estimator methods to assess their effectiveness in handling spurious images. For domains characterized by heterogeneous tasks, employing a robust estimator (e.g., median) is essential. This study is the first to present a systematic approach for implementing IBMA using the NeuroVault repository. We introduce an accessible and reproducible methodology that allows researchers to make the most of NeuroVault's extensive neuroimaging resources, potentially fostering greater interest in data sharing and future meta-analyses utilizing NeuroVault data.

neuroscience↗

Dynamic reconfiguration of brain coactivation states associated with active and lecture-based learning of university physics

Academic institutions are increasingly adopting active learning methods to enhance educational outcomes. Using functional magnetic resonance imaging (fMRI), we investigated neurobiological differences between active learning and traditional lecture-based approaches in university physics education. Undergraduate students enrolled in an introductory physics course underwent an fMRI session before and after a 15-week semester. Coactivation pattern (CAP) analysis was used to examine the temporal dynamics of brain states across different cognitive contexts, including physics conceptual reasoning, physics knowledge retrieval, and rest. CAP results identified seven distinct brain states, with contributions from frontoparietal, somatomotor, and visuospatial networks. Among active learning students, physics learning was associated with increased engagement of a somatomotor network, supporting an embodied cognition framework, while lecture-based students demonstrated stronger engagement of a visuospatial network, consistent with observational learning. These findings suggest significant neural restructuring over a semester of physics learning, with different instructional approaches preferentially modulating distinct patterns of brain dynamics.

neuroscience↗

Meta all the way down: An overview of neuroimaging meta-analyses

Meta-analyses are invaluable tools for navigating the rapidly expanding scientific literature. Given their high value, ensuring the quality of meta-analyses is paramount. We conducted a multifaceted overview, examining each step in a manual neuroimaging meta-analysis on a large scale. We used four novel datasets comprising over 14,000 papers, including fMRI meta-analyses, fMRI studies, studies included in meta-analyses, and studies associated with image data on NeuroVault. Regarding successes, two-thirds of meta-analyses stated that they followed PRISMA guidelines, and 65% included a flowchart describing their inclusion process. We point out several areas for improvement. Pre-registration was fairly rare (20%), and only half listed their exact search strategy. There could be a location bias in which papers are included, and many did not include enough studies to be robust against publication bias (68% of meta analyses have less than 30 studies included). We also offer ideas for future directions. As image based meta-analysis is the gold standard, we have indicated which topics have the most image data available. The potential redundancy of topics can be visualized in our paper, and we recommend future meta-analyses be in conversation with past ones by citing and discussing previous similar work. By addressing these findings, the neuroimaging community can collectively improve the field of neuroimaging meta-analyses.

neuroscience↗

Dense Investigation of Variability in Affect (DIVA): A Neuroimaging Study of Premenopausal Female Participants

The rise of large neuroimaging datasets and multi-dataset mega-analyses brings the power to study interindividual differences in brain structure and function on a heretofore unseen scale. However, unknown and poorly characterized intra-individual variability continues to undermine the detection of robust brain-behavior associations and, ultimately, our understanding of the brain on the whole. Womens and reproductive health underlie variability in more than half of the population, but have long been overlooked in the study of both inter- and intra-individual differences in the brain. To this end, the Dense Investigation of Variability in Affect (DIVA) Study was designed to study intra-individual variability in the brain and behavior across the menstrual cycle in a small cohort of premenopausal female participants. The DIVA Study acquired weekly actigraphy, self-report, biospecimen, and both functional and structural magnetic resonance imaging data with concurrent peripheral physiological recordings. These data facilitate the study of several common sources of variability in the brain and behavior: the menstrual cycle and ovarian hormones, sleep, stress, exercise, and exogenous sources of hemodynamic variability.

neuroscience↗

Meta-analytic decoding of the cortical gradient of functional connectivity

Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network and representative of abstract functioning at the other. The functional significance and interpretation of macroscale gradients remains a central topic of discussion in the neuroimaging community, with some studies demonstrating that gradients may be described using meta-analytic functional decoding techniques. However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance. Here, we conducted a comprehensive series of analyses to investigate and improve the framework of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient segmentation and functional decoding. We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding functional connectivity gradients. Finally, we proposed a method for decoding additional components of the gradient decomposition. The current work aims to provide recommendations on best practices and flexible methods for gradient-based functional decoding of fMRI data.

neuroscience↗

Task-based attentional and default mode connectivity associated with STEM anxiety profiles among university physics students

Attentional control theory (ACT) posits that elevated anxiety increases the probability of re-allocating cognitive resources needed to complete a task to processing anxiety-related stimuli. This process impairs processing efficiency and can lead to reduced performance effectiveness. Science, technology, engineering, and math (STEM) students frequently experience STEM-related anxiety, which can interfere with learning and performance and negatively impact student retention and graduation rates. The objective of this study was to extend the ACT framework to investigate the neurobiological associations between STEM-related anxiety and cognitive performance among 123 physics undergraduate students. Latent profile analysis (LPA) identified four profiles of student STEM-related anxiety, including two profiles that represented the majority of the sample (Low STEM Anxiety; 59.3% and High Math Anxiety; 21.9%) and two additional profiles that were not well represented (High STEM Anxiety; 6.5% and High Science Anxiety; 4.1%). Students underwent a functional magnetic resonance imaging (fMRI) session in which they performed two tasks involving physics cognition: the Force Concept Inventory (FCI) task and the Physics Knowledge (PK) task. No significant differences were observed in FCI or PK task performance between High Math Anxiety and Low STEM Anxiety students. During the three phases of the FCI task, we found no significant brain connectivity differences during scenario and question presentation, yet we observed significant differences during answer selection within and between the dorsal attention network (DAN), ventral attention network (VAN), and default mode network (DMN). Further, we found significant group differences during the PK task were limited to the DAN, including DAN-VAN and within-DAN connectivity. These results highlight the different cognitive processes required for physics conceptual reasoning compared to physics knowledge retrieval, provide new insight into the underlying brain dynamics associated with anxiety and physics cognition, and confirm the relevance of ACT theory for STEM-related anxiety.

neuroscience↗

Acculturative orientations among Hispanic/Latinx caregivers in the ABCD Study: Associations with caregiver and youth mental health and youth brain function

BackgroundPopulation-based neuroscience offers opportunities to examine important but understudied sociocultural factors, such as acculturation. Acculturation refers to the extent to which an individual retains their cultural heritage and / or adopts the receiving societys culture and is particularly salient among Hispanic/Latinx immigrants. Specific acculturative orientations have been linked to vulnerability to substance use, depression, and suicide and are known to influence family dynamics between caregivers and their children. MethodsWe investigated first- and second-generation Hispanic/Latinx caregivers in the ABCD Study and examined how caregivers acculturative orientation impacts their mental health, as well as the mental health of their children. In addition, we evaluated how caregiver orientation is associated with adolescent socio-affiliative neural function in the ventromedial prefrontal cortex, insula, and temporoparietal junction. ResultsWe identified two caregiver acculturation profiles: bicultural (retains heritage culture while adopting US culture) and detached (discards heritage culture and rejects US culture). Bicultural caregivers exhibited fewer symptoms of depression, avoidant behaviors, and inattention compared to detached caregivers; further, youth exhibited similar internalizing effects across caregiver profiles. Moreover, youth with bicultural caregivers displayed increased resting-state brain activity in the left insula; however, differences in long-range functional connectivity were not significant. ConclusionsCaregiver acculturation is an important familial-environmental factor in Hispanic/Latinx families linked to significant differences in caregiver and youth mental health and youth insula activity. Future work should examine sociocultural and neurodevelopmental changes across adolescence to assess health outcomes and determine whether localized, corticolimbic brain effects are ultimately translated into long-range connectivity differences.

neuroscience↗