Biology subjects

Eck, A.

Publications and source records attributed to Eck, A..

6 recordsLinked to original sources

bioRxiv2025

Comparative Evaluation of Assumption Lean Community Detection Methods for Human Connectome Networks

Community detection on resting-state functional connectivity provides a principled lens on mesoscale organization in functional brain networks. Currently, the choice of community count K, lacks a standardized or principled guideline. We conducted a systematic benchmark of three assumption-lean approaches on weighted functional connectivity matrices: the Weighted Stochastic Block Model, Spectral Clustering, and K-means Clustering. Performance was assessed on synthetic networks with known ground truth and on three neuroimaging cohorts which included adult and infant datasets. We compared several strategies for selecting K, including the silhouette index and other approaches commonly used in the existing literature. These were evaluated alongside a likelihood-based criterion for the weighted stochastic block model that employs bootstrap confidence intervals for differences in log-likelihood between successive values of K. For the synthetic networks, WSBM and Spectral Clustering correctly identified the true number of communities, whereas K-means Clustering did not. In adult datasets, most indices did not yield a unique optimum, whereas the likelihood-based criterion selected K = 11, which is consistent with established sensory and association systems. In infants and toddlers, the same procedure supports a larger K around 15 and reveals developmentally distinct mesoscale architecture, including anterior and posterior subdivisions within default mode and fronto parietal systems.

neuroscience↗

bioRxiv2025

Stimulant medications affect arousal and reward, not attention

Prescription stimulants such as methylphenidate are being used by an increasing portion of the population, primarily children. These potent norepinephrine and dopamine reuptake inhibitors promote wakefulness, suppress appetite, enhance physical performance, and are purported to increase attentional abilities. Prior functional magnetic resonance imaging (fMRI) studies have yielded conflicting results about the effects of stimulants on the brains attention, action/motor, and salience regions that are difficult to reconcile with their proposed attentional effects. Here, we utilized resting-state fMRI (rs-fMRI) data from the large Adolescent Brain Cognitive Development (ABCD) Study to understand the effects of stimulants on brain functional connectivity (FC) in children (n = 11,875; 8-11 years old) using network level analysis (NLA). We validated these brain-wide association study (BWAS) findings in a controlled, precision imaging drug trial (PIDT) with highly-sampled (165-210 minutes) healthy adults receiving high-dose methylphenidate (Ritalin, 40 mg). In both studies, stimulants were associated with altered FC in action and motor regions, matching patterns of norepinephrine transporter expression. Connectivity was also changed in the salience (SAL) and parietal memory networks (PMN), which are important for reward-motivated learning and closely linked to dopamine, but not the brains attention systems (e.g. dorsal attention network, DAN). Stimulant-related differences in FC closely matched the rs-fMRI pattern of getting enough sleep, as well as EEG- and respiration-derived brain maps of arousal. Taking stimulants rescued the effects of sleep deprivation on brain connectivity and school grades. The combined noradrenergic and dopaminergic effects of stimulants may drive brain organization towards a more wakeful and rewarded configuration, explaining improved task effort and persistence without direct effects on attention networks.

neuroscience↗

bioRxiv2023

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels--a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasnt definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruptions severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in A{beta} PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.

neuroscience↗

bioRxiv2023

Network level enrichment provides a framework for biological interpretation of machine learning results

Machine learning algorithms are increasingly used to identify brain connectivity biomarkers linked to behavior and clinical outcomes. However, non-standard methodological choices in neuroimaging datasets, especially those with families or twins, have prevented robust machine learning applications. Additionally, prioritizing prediction accuracy over biological interpretability has made it challenging to understand the biological processes behind psychopathology. In this study, we employed a linear support vector regression model to study the relationship between resting-state functional connectivity networks and chronological age using data from the Human Connectome Project. We examined the effect of shared variance from twins and siblings by using cross-validation, either randomly assigning or keeping family members together. We also compared models with and without a Pearson feature filter and utilized a network enrichment approach to identify predictive brain networks. Results indicated that not accounting for shared family variance inflated prediction performance, and the Pearson filter reduced accuracy and reliability. Enhancing biological interpretability was achieved by inverting the machine learning model and applying network-level enrichment on the connectome, while directly using regression coefficients as feature weights led to misleading interpretations. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for comprehensible biological interpretation.

neuroscience↗

bioRxiv2018

Variability of core microbiota in newly diagnosed treatment-naïve paediatric Inflammatory Bowel Disease patients

BACKGROUND & AIMSIntestinal microbiota is considered to play a crucial role in the aetiology of inflammatory bowel disease (IBD). We aimed to describe faecal microbiota composition and dynamics in a large cohort of children with de novo (naive) IBD, in comparison to healthy paediatric controls (HC).\n\nMETHODSIn this prospective study, performed at two tertiary centres, faecal samples from newly diagnosed, treatment-naive paediatric IBD patients were collected prior to bowel cleansing for colonoscopy (t0) and 1, 3 and 6 weeks and 3 months after initiation of therapy. The microbial profiles of Crohns disease (CD) and Ulcerative colitis (UC) patients were compared with HC and linked to therapeutic response. Microbiota composition was analysed by IS-pro technology.\n\nRESULTSMicrobial profiles of 104 new IBD-patients (63 CD, 41 UC, median age 14.0 years) were compared to 61 HC (median 7.8 years). IBD was mainly characterised by decreased abundance of Alistipes finegoldi and Alistipes putredinis, which characterize a healthy state microbial core. The classifier including these core species as predictors achieved an AUC of the ROC curve of .87. Core bacteria tended to regain abundance during treatment, but did not reach healthy levels.\n\nCONCLUSIONFaecal microbiota profiles of children with de novo CD and UC can be discriminated from HC with high accuracy, mainly driven by a decreased abundance of species shaping the microbial core in the healthy state. Paediatric IBD can therefore be characterized by decreased abundance of certain bacterial species reflecting the healthy state rather than by the introduction of pathogens.

microbiology↗

bioRxiv2018

Molecular Genetic Analysis of Rbm45/Drbp1: Genomic Structure, Expression, and Evolution

RNA recognition motif-type RNA-binding domain containing proteins (RBDPs) participate in RNA metabolism including regulating mRNA stability, nuclear-cytoplasmic shuttling, and splicing. Rbm45 is an RBDP first cloned from rat brain and expressed spatiotemporally during rat neural development. More recently, RBM45 has been associated with pathological aggregates in the human neurological disorders amyotrophic lateral sclerosis, frontotemporal lobar degeneration, and Alzheimers. Rbm45 and the neural developmental protein musashi-1 are in the same family of RDBPs and have similar expression patterns. In contrast to Musashi-1, which is upregulated during colorectal carcinogenesis, we found no association of RBM45 overexpression in human colon cancer tissue. In order to begin characterizing RNA-binding partners of Rbm45, we have successfully cloned and expressed human RBM45 in an Intein fusion-protein expression system. Furthermore, to gain a better understanding of the molecular genetics and evolution of Rbm45, we used an in silico approach to analyze the gene structure of the human and mouse Rbm45 homologues and explored the evolutionary conservation of Rbm45 in metazoans. Human RBM45 and mouse Rbm45 span {bsim}17 kb and 13 kb, respectively, and contain 10 exons, one of which is non-coding. Both genes have TATA-less promoters with an initiator and a GC-rich element. Downstream of exon 10, both homologues have canonical polyadenylation signals and an embryonic cytoplasmic polyadenylation element. Moreover, our data indicate Rbm45 is conserved across all metazoan taxa from sponges (phylum Porifera) to humans (phylum Chordata), portending a fundamental role in metazoan development.

genetics↗