Biology subjects

Hutchison, K.

Publications and source records attributed to Hutchison, K..

7 recordsLinked to original sources

bioRxiv2024

Evidence for white matter intrinsic connectivity networks at rest and during a task: a large-scale study and templates

Understanding white matter (WM) functional connectivity is crucial for unraveling brain function and dysfunction. In this study, we present a novel WM intrinsic connectivity network (ICN) template derived from over 100,000 fMRI scans, identifying 97 robust WM ICNs using spatially constrained independent component analysis (scICA). This WM template, combined with a previously identified gray matter (GM) ICN template from the same dataset, was applied to analyze a resting-state fMRI (rs-fMRI) dataset from the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (BSNIP2; 590 subjects) and a task-based fMRI dataset from the MIND Clinical Imaging Consortium (MCIC; 75 subjects). Our analysis highlights distinct spatial maps for WM and GM ICNs, with WM ICNs showing higher frequency profiles. Modular structure within WM ICNs and interactions between WM and GM modules were identified. Task-based fMRI revealed event-related BOLD signals in WM ICNs, particularly within the corticospinal tract, lateralized to finger movement. Notable differences in static functional network connectivity (sFNC) matrices were observed between controls (HC) and schizophrenia (SZ) subjects in both WM and GM networks. This open-source WM NeuroMark template and automated pipeline offer a powerful tool for advancing WM connectivity research across diverse datasets.

neuroscience↗

bioRxiv2024

Cannabidiol promotes apoptosis and downregulation of oncogenic factors

Patients with high-grade serous carcinoma of tubo-ovarian origin (HGSC) often experience significant side effects related to their disease and treatments, such as pain, discomfort, nausea, and vomiting. Over the last two decades, the use of cannabinoids (CBD) to manage pain and anxiety has become more mainstream. However, there is limited data on how CBD interacts with HGSC tumor cells or whether CBD impacts the effect of chemotherapy. Prior preclinical data has suggested the antitumor benefits of cannabinoids; however, the mechanism and data in ovarian cancer are limited. The objectives of this proposed research are to define the endocannabinoid system milieu in ovarian cancer, determine if CBD influences the growth of ovarian cancer cells, measure the cell viability when cannabinoids such as CBD are combined with standard-of-care therapies, and identify potential molecular pathways in which cannabinoids have a therapeutic effect. We conducted publicly available database searches, in vitro proliferation and apoptotic assays, functional protein signaling via reverse phase protein array analysis of CBD-treated cells using 2D cultured cells, and immunohistological analysis of ex vivo cultured patient-derived tumor slices treated with CBD. Our data suggests that CBD is unlikely to affect the growth of cancer cells at physiologic doses but promotes apoptosis and can have growth inhibitory effects at higher concentrations. The inhibitory effects seen at high dose concentrations are likely from the upregulation of apoptotic pathways and inhibition of oncogenic pathways. Overall, physiologic CBD levels have minimal impact on cancer cell growth or chemotherapy efficacy.

cancer biology↗

bioRxiv2024

Conditional Knockout of Striatal Gnal Produces Dystonia-like Motor Phenotypes

Loss-of-function mutations in GNAL have been linked to an adult-onset, isolated dystonia that is largely indistinguishable from idiopathic dystonia. GNAL encodes Golf, a heterotrimeric G-protein subunit with a defined molecular function to increase the production of the second messenger cAMP. Golf is abundant in the striatum, and is the only stimulatory G-protein in many cell types of the striatum. Due to the defined molecular signaling pathway and expression pattern of Golf, the clear genetic link to dystonia makes GNAL an exciting target to understand the pathological mechanisms of not only this genetic dystonia, but also the larger idiopathic disease. To better understand GNAL-linked dystonia, we generated a novel genetic mouse model that allows us to conditionally knock out Gnal in a site and time-specific manner. In the current study we used genetic or AAV based approaches to express Cre to knockout striatal Gnal in our novel Gnal fl/fl model. We then performed motor behavioral testing and ex vivo whole-cell patch clamp electrophysiology of striatal spiny projection neurons to interrogate how loss of Gnal leads to dystonia. Mice with conditional striatal knockout of Gnal show hindlimb clasping, other dystonia-like postures, less motor coordination, slowness, and torticollis as compared to age-matched controls. Furthermore, striatal spiny projection neurons show increased excitability in Gnal knockout animals. These exciting data are the first to report uninduced, overt dystonia in a mouse model of GNAL-linked dystonia, and directly correlate these with changes in spiny projection neuron electrophysiological properties. Our results show that adult loss of Gnal in the striatum leads to the development of dystonia, through homeostatic, paradoxical increases in spiny projection neuron excitability, and suggest that therapeutic strategies aimed at decreasing this hyperexcitable phenotype may provide symptomatic relief for patients with disease. One Sentence Summary: When Gnal is knocked out in the striatum of mice we observe overt behavioral symptoms and hyperexcitability in striatal spiny projection neurons.

neuroscience↗

bioRxiv2022

Canonical and Replicable Multi-Scale Intrinsic Connectivity Networks in 100k+ Resting-State fMRI Datasets

Resting-state functional magnetic resonance imaging (rsfMRI) has shown considerable promise for improving our understanding of brain function and characterizing various mental and cognitive states in the healthy and disordered brain. However, the lack of accurate and precise estimations of comparable functional patterns across datasets, individuals, and ever-changing brain states in a way that captures both individual variation and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. We posit that using reliable network templates and advanced group-informed network estimation approaches to accurately and precisely obtain individualized (dynamic) networks that retain cross-subject correspondence while maintaining subject-specific information is one potential solution to overcome the aforementioned barrier when considering cross-study comparability, independence of subject-level estimates, the limited data available in single studies, and the low signal-to-noise ratio (SNR) of rsfMRI. Toward this goal, we first obtained a reliable and replicable network template. We combined rsfMRI data of over 100k individuals across private and public datasets and selected around 58k that meet quality control (QC) criteria. We then applied multi-model-order independent component analysis (ICA) and subsampling to obtain reliable canonical intrinsic connectivity networks (ICNs) across multiple spatial scales. The selected ICNs (i.e., network templates) were also successfully replicated by independently analyzing the data that did not pass the QC criteria, highlighting the robustness of our adaptive template to data quality. We next studied the feasibility of estimating the corresponding subject-specific ICNs using a multivariate-spatially constrained ICA as an example of group-informed network estimation approaches. The results highlight that several factors, including ICNs themselves, data length, and spatial resolution, play key roles in successfully estimating the ICNs at the subject level. Large-scale ICNs, in general, require less data to achieve a specific level of spatial similarity with their templates (as well as within- and between-subject spatial similarity). Moreover, increasing data length can reduce an ICNs subject-level specificity, suggesting longer scans might not always be desirable. We also show spatial smoothing can alter results, and the positive linear relationship we observed between data length and spatial smoothness (we posit that it is at least partially due to averaging over intrinsic dynamics or individual variation) indicates the importance of considering this factor in studies such as those focused on optimizing data length. Finally, the consistency in the spatial similarity between ICNs estimated using the full-length of data and subset of it across different data lengths may suggest that the lower within-subject spatial similarity in shorter data lengths is not necessarily only defined by lower reliability in ICN estimates; rather, it can also be an indication of brain dynamics (i.e., different subsets of data may reflect different ICN dynamics), and as we increase the data length, the result approaches the average (also known as static) ICN pattern, and therefore loses its distinctiveness.

neuroscience↗

bioRxiv2021

Proinflammatory responses in SARS-CoV-2 infected and soluble spike glycoprotein S1 subunit activated human macrophages

Critically ill COVID-19 patients infected with SARS-CoV-2 display signs of generalized hyperinflammation. Macrophages trigger inflammation to eliminate pathogens and repair tissue, but this process can also lead to hyperinflammation and resulting exaggerated disease. The role of macrophages in dysregulated inflammation during SARS-CoV-2 infection is poorly understood. We used SARS-CoV-2 infected and glycosylated soluble SARS-CoV-2 Spike S1 subunit (S1) treated THP-1 human-derived macrophage-like cell line to clarify the role of macrophages in pro-inflammatory responses. Soluble S1 upregulated TNF- and CXCL10 mRNAs, and induced secretion of TNF- from THP-1 macrophages. While THP-1 macrophages did not support productive SARS-CoV-2 replication, virus infection resulted in upregulation of both TNF- and CXCL10 genes. Our study shows that S1 is a key viral component inducing inflammatory response in macrophages, independently of virus replication. Thus, virus-infected or soluble S1-activated macrophages may become sources of pro-inflammatory mediators contributing to hyperinflammation in COVID-19 patients.

immunology↗

bioRxiv2021

An interpretable connectivity-based decoding model for classification of chronic marijuana use

BackgroundPsychiatric neuroimaging typically proceeds with one of two approaches: encoding models, which aim to model neural mechanisms, or decoding models, which aim to predict behavioral or clinical characteristics from brain imaging data. In this study, we seek to combine these aims by developing interpretable decoding models that offer both accurate prediction and novel neural insights. We demonstrate the effectiveness of this combined approach in a case study of chronic marijuana use. MethodsChronic marijuana (MJ) users (n=195) and non-using healthy controls (n=128) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic MJ users and non-users based on task-evoked, whole-brain functional connectivity. We then used graph theoretic analyses to identify predictive functional connectivities among brain regions that contributed most substantially to the classification of chronic marijuana use. ResultsWe obtained high (~80% out-of-sample) accuracy across four different classification models, demonstrating that task-evoked, whole-brain functional connectivity can successfully differentiate chronic marijuana users from non-users. Subsequent network analyses revealed key predictive regions (e.g., anterior cingulate cortex, dorsolateral prefrontal cortex, and precuneus) that are often implicated in neuroimaging studies of substance use disorders, as well as some key exceptions. We also identified a core set of networks of brain regions that contributed to successful classification, comprised of many of the same predictive regions. ConclusionsOur dual aims of accurate prediction and interpretability were successful, producing a predictive model that also provides interpretability at the neural level. This novel approach may complement other predictive-exploratory approaches for a more complete understanding of neural mechanisms in drug use and other neuropsychiatric disorders.

neuroscience↗

bioRxiv2018

Correlation between the oral microbiome and brain resting state connectivity in smokers

Recent studies have shown a critical role of the gastrointestinal microbiome in brain and behavior via the complex gut-microbiome-brain axis, however, the influence of the oral microbiome in neurological processes is much less studied, especially in response to the stimuli in the oral microenvironment such as smoking. Additionally, given the complex structural and functional networks in brain system, our knowledge about the relationship between microbiome and brain function in specific brain circuits is still very limited. In this pilot work, we leveraged next generation microbial sequencing with functional neuroimaging techniques to enable the delineation of microbiome-brain network links as well as their relationship to cigarette smoking. Thirty smokers and 30 age- and sex- matched non-smokers were recruited for measuring both microbial community and brain functional networks. Statistical analyses were performed to demonstrate the influence of smoking on the abundance of the constituents within the oral microbial community and functional network connectivity among brain regions as well as the associations between microbial shifts and the brain functional network connectivity alternations. Compared to non-smokers, we found a significant decrease in beta diversity (p = 6x10-3) in smokers and identified several classes (Betaproteobacteria, Spirochaetia, Synergistia, and Mollicutes) as having significant alterations in microbial abundance. Taxonomic analyses demonstrate that the microbiota with altered abundance are mainly involved in pathways related to cell processes, DNA repair, immune system, and neurotransmitters signaling. One brain functional network connectivity component was identified to have a significant difference between smokers and nonsmokers (p = 0.033), mainly including connectivity between brain default network and other task-positive networks. The brain functional component was also significantly associated with some smoking related oral microbiota, suggesting a potential link between smoking-induced oral microbiome dysbiosis and brain functional connectivity, possibly through immunological and neurotransmitter signaling pathways. This work is the first attempt to link oral microbiome and brain functional networks, and provides support for future work in characterizing the role of oral microbiome in mediating smoking effects on brain activity.

microbiology↗