Biology subjects

Salo, T.

Publications and source records attributed to Salo, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

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↗

bioRxiv2024

Patient-derived functional immuno-oncology platform identifies responders to ATR inhibitor and immunotherapy combinations in ovarian cancer

Responses to single agent immunotherapies have remained modest in high-grade serous ovarian cancer (HGSC), suggesting the need for combination treatments. Identifying clinically effective immunotherapy combinations (IC) requires pre-clinical testing using models representing the patient-specific immune microenvironment. Here, we established a functional immuno-oncology platform for high-throughput and functional testing of IC using HGSC patient-derived immunocompetent cultures (iPDCs) established on patient-derived omentum gel matrix. We employed genomic and single-cell analysis to assess the intricate and functional characteristics of the iPDCs combined with tumor and immune cell-specific cytotoxic responses. Corroborating the clinical response to Poly (ADP-ribose) polymerase inhibitors (PARPi), iPDCs showed homologous recombination deficiency (HRD) - specific response to PARPi. Importantly, drug responses from iPDCs of chemotherapy and PARPi refractory patients corresponded with patient outcomes and aligned with distinct pathway activities from single-cell RNA sequencing analysis. Furthermore, iPDCs from HRD tumors showed response to anti-PD1 antibody as measured by decrease in tumor cells combined with augmented T cell activation. High-throughput drug testing followed by single cell-imaging from iPDCs revealed patient-specific responses to combination of ataxia telangiectasia and Rad3-related inhibitor (ATRi) with DNA damaging agents or immunotherapies. Integration of cytotoxic responses with immune cell states uncovered patient-specific immune activation with the combination of ATRi and a novel immunotherapy targeting Autotaxin (ATX), and this response was significantly associated with a tumor-cell replication stress biomarker in single-cell analysis of tCycIF highly multiplexed imaging. In conclusion, iPDCs provide a platform for high-throughput screening and functional testing of immuno-oncology agents for precision oncology in HGSC.

cancer biology↗

bioRxiv2023

Framewise multi-echo distortion correction for superior functional MRI

Functional MRI (fMRI) data are severely distorted by magnetic field (B0) inhomogeneities which currently must be corrected using separately acquired field map data. However, changes in the head position of a scanning participant across fMRI frames can cause changes in the B0 field, preventing accurate correction of geometric distortions. Additionally, field maps can be corrupted by movement during their acquisition, preventing distortion correction altogether. In this study, we use phase information from multi-echo (ME) fMRI data to dynamically sample distortion due to fluctuating B0 field inhomogeneity across frames by acquiring multiple echoes during a single EPI readout. Our distortion correction approach, MEDIC (Multi-Echo DIstortion Correction), accurately estimates B0 related distortions for each frame of multi-echo fMRI data. Here, we demonstrate that MEDICs framewise distortion correction produces improved alignment to anatomy and decreases the impact of head motion on resting-state functional connectivity (RSFC) maps, in higher motion data, when compared to the prior gold standard approach (i.e., TOPUP). Enhanced framewise distortion correction with MEDIC, without the requirement for field map collection, furthers the advantage of multi-echo over single-echo fMRI.

bioengineering↗

bioRxiv2023

XCP-D: A Robust Pipeline for the post-processing of fMRI data

Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they tend not to support output from disparate pre-processing pipelines, may have limited documentation, and may not follow BIDS best practices. Here we present XCP-D, which presents a solution to these issues. XCP-D is a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University at Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Singularity image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NifTI or CIFTI files, following pre-processing with fMRIPrep, HCP, and ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded >3,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.

neuroscience↗

bioRxiv2023

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps

Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS) - BIDS Apps - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n=2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.

neuroscience↗

bioRxiv2023

Generalizable links between symptoms of borderline personality disorder and functional connectivity

BackgroundSymptoms of borderline personality disorder (BPD) often manifest in adolescence, yet the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here we aimed to investigate how multivariate patterns of functional connectivity are associated with symptoms of BPD in a large sample of young adults and adolescents. MethodsWe used high-quality functional Magnetic Resonance Imaging (fMRI) data from young adults from the Human Connectome Project: Young Adults (HCP-YA; N = 870, ages 22-37 years, 457 female) and youth from the Human Connectome Project: Development (HCP-D; N = 223, age range 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five Factor Inventory (NEO-FFI). A ridge regression model with 10-fold cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity, while controlling for in-scanner motion, age, and sex. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with agerelated changes in connectivity. ResultsMultivariate functional connectivity patterns significantly predicted out-of-sample BPD proxy scores in unseen data in both young adults (HCP-YA; pperm = 0.001) and older adolescents (HCP-D; pperm = 0.001). Predictive capacity of regions was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD proxy scores aligned with those associated with development in youth. ConclusionIndividual differences in functional connectivity in developmentally-sensitive regions are associated with the symptoms of BPD.

neuroscience↗

bioRxiv2023

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↗

bioRxiv2023

Functional Connectivity Development along the Sensorimotor-Association Axis Enhances the Cortical Hierarchy

Human cortical maturation has been posited to be organized along the sensorimotor-association (S-A) axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the S-A axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3,355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1,207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1,126). In each dataset, the development of functional connectivity systematically varied along the S-A axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These robust and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

neuroscience↗

bioRxiv2023

Graded functional organisation in the left inferior frontal gyrus: evidence from task-free and task-based functional connectivity

The left inferior frontal gyrus (LIFG) has been ascribed key roles in numerous cognitive domains, including language, executive function and social cognition. However, its functional organisation, and how the specific areas implicated in these cognitive domains relate to each other, is unclear. Possibilities include that the LIFG underpins a domain-general function or, alternatively, that it is characterized by functional differentiation, which might occur in either a discrete or a graded pattern. The aim of the present study was to explore the topographical organisation of the LIFG using a bimodal data-driven approach. To this end, we extracted functional connectivity (FC) gradients from 1) the resting-state fMRI time-series of 150 participants (77 female), and 2) patterns of co-activation derived meta-analytically from task data across a diverse set of cognitive domains. We then sought to characterize the FC differences driving these gradients with seed-based resting-state FC and meta-analytic co-activation modelling analyses. Both analytic approaches converged on an FC profile that shifted in a graded fashion along two main organisational axes. An anterior-posterior gradient shifted from being preferentially associated with high-level control networks (anterior LIFG) to being more tightly coupled with perceptually-driven networks (posterior). A second dorsal-ventral axis was characterized by higher connectivity with domain-general control networks on one hand (dorsal LIFG), and with the semantic network, on the other (ventral). These results provide novel insights into a graded functional organisation of the LIFG underpinning both task-free and task-constrained mental states, and suggest that the LIFG is an interface between distinct large-scale functional networks. Significance statementTo understand how function varies across the LIFG, we conducted a detailed, bimodal exploration of the spatial transitions in its voxel-wise FC patterns. We provide novel evidence of graded changes along two main organisational axes. Specifically, the LIFG was characterized by an anterior-posterior gradient, which could reflect a shift in function from perceptually-driven processing to task-oriented control processes. Moreover, we revealed a dorsal-ventral shift in FC that is consistent with the idea that domain-specificity is a core principle underpinning functional organisation of the LIFG. These gradients were replicated across task-free and task-constrained FC measures, suggesting that a similar fundamental organisation underpins both mental states.

neuroscience↗

bioRxiv2022

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↗

bioRxiv2022

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↗

bioRxiv2022

Extended functional connectivity of convergent structural alterations among individuals with PTSD: A neuroimaging meta-analysis

BackgroundPost-traumatic stress disorder (PTSD) is a debilitating disorder defined by the onset of intrusive, avoidant, negative cognitive or affective, and/or hyperarousal symptoms after witnessing or experiencing a traumatic event. Previous voxel-based morphometry studies have provided insight into structural brain alterations associated with PTSD with notable heterogeneity across these studies. Furthermore, how structural alterations may be associated with brain function, as measured by task-free and task-based functional connectivity, remains to be elucidated. MethodsUsing emergent metaanalytic techniques, we sought to first identify a consensus of structural alterations in PTSD using the anatomical likelihood estimation (ALE) approach. Next, we generated functional profiles of identified convergent structural regions utilizing resting-state functional connectivity (rsFC) and meta-analytic coactivation modeling (MACM) methods. Finally, we performed functional decoding to examine mental functions associated with our ALE, rsFC, and MACM brain characterizations. ResultsWe observed convergent structural alterations in a single region located in the medial prefrontal cortex. The resultant rsFC and MACM maps identified functional connectivity across a widespread, whole-brain network that included frontoparietal and limbic regions. Functional decoding revealed overlapping associations with attention, memory, and emotion processes. ConclusionsConsensus-based functional connectivity was observed in regions of the default mode, salience, and central executive networks, which play a role in the tripartite model of psychopathology. Taken together, these findings have important implications in understanding the neurobiological mechanisms associated with PTSD.

neuroscience↗

bioRxiv2022

Neural systems underlying RDoC social constructs: An activation likelihood estimation meta-analysis

Neuroscientists have sought to identify the underlying neural systems supporting social processing that allow interaction and communication, forming social relationships, and navigating the social world. Through the use of NIMHs Research Domain Criteria (RDoC) framework, we evaluated consensus among studies that examined brain activity during social tasks to elucidate regions comprising the "social brain". We examined convergence across tasks corresponding to the four RDoC social constructs, including Affiliation and Attachment, Social Communication, Perception and Understanding of Self, and Perception and Understanding of Others. We performed a series of coordinate-based meta-analyses using the activation likelihood estimate (ALE) method. Meta-analysis was performed on whole-brain coordinates reported from 864 fMRI contrasts using the NiMARE Python package, revealing convergence in medial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, temporoparietal junction, bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. Additionally, four separate RDoC-based meta-analyses revealed differential convergence associated with the four social constructs. These outcomes highlight the neural support underlying these social constructs and inform future research on alterations among neurotypical and atypical populations.

neuroscience↗

bioRxiv2021

PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets. It serves not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data (PET-BIDS). We describe the PET-BIDS standard in detail and share several open-access datasets curated following PET-BIDS. Additionally, we highlight several tools which are already available for converting, validating and analyzing PET-BIDS datasets.

neuroscience↗

bioRxiv2021

Denoising physiological data collected during multi-band, multi-echo EPI sequences

Collecting physiological data during fMRI experiments can improve fMRI data cleaning and contribute to our understanding of psychophysiological processes; however, these recordings are frequently fraught with artifacts from the MRI pulse sequence. Here, we assess data from BIOPAC Systems, Inc., one of the more widely-used manufacturers of physiological monitoring equipment, and evaluate their recommendations for filtering such artifacts from electrocardiogram and electrodermal activity data collected during single-band, single-echo fMRI sequences and extend these recommendations to address artifacts associated with multiband, multi-echo fMRI sequences. While the magnitude and frequencies of artifacts differ with these aspects of pulse sequences, their effects can be mitigated via application of digital filters incorporating slice collection, multiband factor, and repetition time. The implementation of these filters is provided both in interactive online notebooks and an open source denoising tool.

physiology↗

bioRxiv2021

SLC4A2 Anion Exchanger Promotes Tumor Cell Malignancy via Enhancing H+ Leak across Golgi Membranes

Proper functioning of each secretory and endocytic compartment relies on its unique pH micro-environment that is known to be dictated by the rates of V-ATPase-mediated H+ pumping and its leakage back to the cytoplasm via an elusive "H+ leak" pathway. Here, we show that this proton leak across Golgi membranes involves AE2a (SLC4A2a)-mediated bicarbonate-chloride exchange, as it is strictly dependent on both bicarbonate import (in exchange of chloride export) and the AE2a expression level in the cells. Imported bicarbonate anions and luminal protons then facilitate a common buffering reaction that yields carbon dioxide and water before their egress back to the cytoplasm via diffusion or water channels. The high surface-volume ratio of flattened Golgi cisternae helps this process, as their shape is optimal for water and gas exchange. Interestingly, this pathway is often upregulated in cancers and established cancer cell lines, and responsible for their markedly elevated Golgi resting pH and attenuated glycosylation potential. Accordingly, AE2 knockdown in SW-48 colorectal cancer cells was able to restore these two phenomena, and at the same time, to reverse cells invasive and anchorage-independent growth phenotype. These findings suggest that a malignant cell can be returned to a benign state by normalizing its Golgi resting pH.

cancer biology↗

bioRxiv2021

Intelligence and academic performance: Is it all in your head?

Academic performance relies, in part, on intelligence; however, intelligence quotient (IQ) is limited in predicting academic success. Furthermore, while the search for the biological seat of intelligence predates neuroscience itself, its findings remain conflicting. Here, we assess the interplay between IQ, academic performance, and brain connectivity with behavioral and functional MRI data collected from undergraduate students as they completed an active learning or lecture-based semester-long university physics course. IQ (i.e., full-scale WAIS scores) increased significantly pre-to post-instruction, were associated with physics knowledge and reasoning measures, but were unrelated to overall course grade. IQ was related to brain connectivity during physics-related cognition, but connectivity did not mediate IQs association with task performance. These relations depended on students sex and instructional environment, providing evidence that physics classroom environment and pedagogy may have a gendered influence on students performance. Discussion focuses on opportunities to improve physics reasoning skills for all students.

neuroscience↗

bioRxiv2020

Transcriptome profiling of a multiuse model species Lymnaea stagnalis (Gastropoda) for ecoimmunological research

Host immune function can contribute to numerous ecological/evolutionary processes. Ecoimmunological studies, however, typically use one/few phenotypic immune assays and thus do not consider the complexity of the immune system. Therefore, "omics" resources that allow quantifying immune activity across multiple pathways are needed for ecoimmunological models. We applied short-read based RNAseq (Illumina NextSeq 500, PE-81) to characterise transcriptome profiles of a multipurpose model species Lymnaea stagnalis (Gastropoda). We used a genetically diverse snail stock and exposed individuals to immune elicitors (injury, bacterial/trematode pathogens) and changes in environmental conditions that can alter immune activity (temperature, food availability). Immune defence factors identified in the de novo assembly indicated uniform aspects of molluscan immunity: pathogen-recognition receptors (PRR) and lectins activate Toll-like receptor (TLR) pathway and cytokines that regulate cellular and humoral defences. However, also apparent differences to other taxa were detected (i.e., modest numbers of antimicrobial peptides and fibrinogen related proteins). Identified factors also indicate that several of them might contribute to the phenotypic immune assays used on this species. Experimental treatments revealed factors from non-self recognition (lectins) and signalling (TLR pathway, cytokines) to effectors [e.g., antibacterial proteins, phenoloxidase (PO) enzymes] whose gene expression depended on immune activations and environmental conditions, as well as components of snail physiology/metabolism that may drive these effects. Interestingly, gene expression of many factors (e.g., PRR, lectins, cytokines, PO enzymes, antibacterial proteins) showed high among-individual variation. Such factors are important to include in ecoimmunological research because they may explain among-individual differences in parasite resistance and fitness in natural populations.

genomics↗