Biology subjects

Goodfellow, M.

Publications and source records attributed to Goodfellow, M..

9 recordsLinked to original sources

bioRxiv2025

Dynamics-Informed Priors (DIP) for Neural Mass Modelling

Neural Mass Models (NMMs) are important mathematical tools for inferring hidden neural mechanisms that generate healthy and pathological brain activities. A critical step in the inference process is parameter estimation, which calibrates NMMs based on measured neuroimaging data. While parameter estimation can be conducted via various approaches, one of the most influential methods is Dynamic Causal Modelling (DCM). DCM adopts a Bayesian inference approach that relies on, and is sensitive to, the specification of prior parameter distributions reflecting a priori hypotheses about the causes of data. However, most parameters of NMMs encode neuronal properties that are not directly measurable. For this reason, in the absence of sufficient empirical data and well-founded prior beliefs, inference becomes increasingly susceptible to bias. Therefore, it was imperative to establish a comprehensive strategy for mapping model parameters to data. This study proposes a computational extension of DCM, named DCM with dynamics-informed priors (DIP-DCM), which adopts a genetic algorithm (GA) to map parameter values to model dynamics. Optimal sub-regions of the parameter space were subsequently selected and translated into groups of parameter priors for DCM. DIP-DCM was compared to the standard DCM inference and to the standalone GA, using two independent neuroimaging datasets. Results indicated that DIP-DCM models were the best predictors of data and captured key mechanistic signatures of psychiatric disease and pharmacological interventions. Overall, DIP-DCM addressed degeneracy, handled local minima, and explored diverse parameter regimes following trajectories informed directly by model dynamics and data. This study suggests that DIP-DCM is an advantageous route to parameter estimation when information is limited, enabling a data-driven derivation of parameter priors - or hypotheses - in exploratory studies, across different biological contexts and datasets.

systems biology↗

bioRxiv2025

Global search metaheuristics for neural mass model calibration

Neural mass models (NMMs) are often used to help understand the circuitry that underpins observed brain dynamics in basic and clinical research. A key step is to fuse models with data so that model parameter values can be inferred for a given data set--a process called model fitting or model calibration. This can shed light on putative physiological mechanisms underlying the observed signals. Calibration is notoriously challenging in biology since models are often non-identifiable, high-dimensional, and nonlinear. Established methods such as dynamic causal modelling (DCM) circumvent some of these issues, for example, by incorporating prior information and employing fast local search methods in the space of feasible parameter values ("parameter space"). However, it is pertinent to better understand the potential limitations of these methods so that we can increase our confidence in the use of models to interpret brain activity, and to develop new approaches as required. Here we use tools from dynamical systems theory to illustrate some of the complexities of model calibration in an archetypal NMM. We use this information to motivate the use of calibration methods that work across large regions of parameter space, rather than focusing on informative priors or localised search methods. We subsequently evaluate the performance of approximate Bayesian computation (ABC) and evolutionary search metaheuristics (ESMs) for mapping feasible sets of parameters for which an NMM can recreate electroen-cephalographic recordings during an eyes-closed resting state. Our results demonstrate the superiority of ESMs in terms of computational efficiency and accuracy. Furthermore, we elucidate potential reasons why ESMs are able to perform better than ABC, i.e. that they are less susceptible to biases induced by the complexity of underlying cost landscapes. These results highlight the importance of incorporating ESMs in future efforts to model brain dynamics.

neuroscience↗

bioRxiv2024

Longitudinal assessment of the conversion of mild cognitive impairment into Alzheimer's dementia: Observations and mechanisms from neuropsychological testing and electrophysiology

INTRODUCTIONElucidating and better understanding functional biomarkers of Alzheimers disease (AD) is crucial. By analysing a detailed longitudinal dataset, this study aimed to create a model-based toolset to characterise and understand the conversion of mild cognitive impairment (MCI) to AD. METHODSEEG, MRI, and neuropsychological data were collected from participants in San Marino: AD (n = 10), MCI (n = 20), and controls (n = 11). Across two additional years, MCI participants were classified as converters or non-converters. RESULTSWe identified the Stroop Color and Word Test as the largest differentiator for MCI conversion (ROC AUC = 0.795). This was underpinned by disconnectivity in working memory and attention networks. Unsupervised clustering of EEG spectra also differentiated MCI conversion (ROC AUC = 0.710) and was underpinned by reduced excitatory and enhanced inhibitory synaptic efficacy in (prodromal) AD. Combining electrophysiological and neuropsychological assessments increased the accuracy of the differentiation (ROC AUC = 0.880) in comparison to each measure considered individually. CONCLUSIONCombining electrophysiological and neuropsychological assessment with mathematical models can inform the development of non-invasive, low-cost tools for the early diagnosis of AD. HighlightsO_LIWe analysed longitudinal changes in EEG and neuropsychological assessments in MCI C_LIO_LIStroop Color and Word Test error scores were lower in MCI converters C_LIO_LIThe degree of impairment was found to be correlated with functional disconnectivity C_LIO_LIUnsupervised clustering of EEG spectra characterised patterns associated with disease C_LIO_LIMathematical modelling revealed reduced excitatory synaptic efficacy in (prodromal) AD C_LI Research in ContextSystematic review: The authors used PubMed to review the literature on the use of inexpensive modalities, including EEG and neurophysiological testing, for characterising the progression of MCI to AD. Although promising, existing work suggests the full potential of these methods as tools for understanding prodromal AD is still lacking. Interpretation: A novel application of a clustering algorithm to EEG spectra revealed different patient diagnoses could largely be characterised by their cluster assignment. We also found differences in a particular neuropsychological test, the Stroop Color and Word Test. Using mathematical modelling we found there were both network and synaptic mechanisms that underlie these differences. Future directions: Using the methods described herein to build markers for testing MCI to AD conversion on a large independent cohort will be crucial to understanding the full impact and applicability of these approaches. This may ultimately lead to a better characterisation and understanding of the diagnosis and prognosis of AD.

neuroscience↗

bioRxiv2022

Systematic whole-genome sequencing reveals an unexpected diversity among actinomycetoma pathogens and provides insights into their antibacterial susceptibilities

Mycetoma is a neglected tropical chronic granulomatous inflammatory disease of the skin and subcutaneous tissues. More than 70 species with a broad taxonomic diversity have been implicated as agents of mycetoma. Understanding the full range of causative organisms and their antibiotic sensitivity profiles are essential for the appropriate treatment of infections. The present study focuses on the analysis of full genome sequences and antibiotic resistance profiles of actinomycetoma strains from patients seen at the Mycetoma Research Centre in Sudan with a view to developing rapid diagnostic tests. Seventeen pathogenic isolates obtained by surgical biopsies were sequenced using MinION and Illumina methods, and their antibiotic resistance profiles determined. The results highlight an unexpected diversity of actinomycetoma causing pathogens, including three Streptomyces isolates assigned to species not previously associated with human actinomycetoma and one new Streptomyces species. Thus, current approaches for clinical and histopathological classification of mycetoma may need to be updated. The standard treatment for actinomycetoma is a combination of sulfamethoxazole/trimethoprim and amoxicillin/clavulanic acid. Most tested isolates were not susceptible to sulfamethoxazole/trimethoprim or to amoxicillin alone. However, the addition of the {beta}-lactamase inhibitor clavulanic acid to amoxicillin increased susceptibility, particularly for Streptomyces somaliensis and Streptomyces sudanensis. Actinomadura madurae isolates appear to be particularly resistant under laboratory conditions, suggesting that alternative agents, such as amikacin, should be considered for more effective treatment. The results obtained will inform future diagnostic methods for the identification of actinomycetoma and treatment. Author SummaryMycetoma is a common health and medical problem that is endemic in many tropical and subtropical countries and has devastating effects on patients. The destructive nature of late-stage infection means that treatment often requires long term use of antibiotic therapy, massive surgical excisions and amputation. Several different bacterial species have been described as causing this disease but our understanding of the true diversity of mycetoma causing bacteria has been limited by a lack of molecular sequence data. We have now sequenced the genomes of 17 samples isolated from patients at the Mycetoma Research Centre in Sudan, revealing a diverse range of species associated with infection including one new Streptomyces species, and three species with no previous association with human mycetoma. Crucially, all isolates had a high level of resistance against the current first-line antibiotics used to treat actinomycetoma under laboratory conditions. This resistance was strongest in Actinomadura madurae, which was also the most frequently observed species isolated from patients in our study. We hope that these results will aid in the development of future rapid diagnostic tools and the improvement of treatment outcomes.

genomics↗

bioRxiv2021

Using Deep Clustering to Improve fMRI Dynamic Functional Connectivity Analysis

Dynamic functional connectivity (dFC) analysis of resting-state fMRI data is commonly performed by calculating sliding-window correlations (SWC), followed by k-means clustering in order to assign each window to a given state. Studies using synthetic data have shown that k-means performance is highly dependent on sliding window parameters and signal-to-noise ratio. Additionally, sources of heterogeneity between subjects may affect the accuracy of group-level clustering, thus affecting measurements of dFC state temporal properties such as dwell time and fractional occupancy. This may result in spurious conclusions regarding differences between groups (e.g. when comparing a clinical population to healthy controls). Therefore, is it important to quantify the ability of k-means to estimate dFC state temporal properties when applied to cohorts of multiple subjects, and to explore ways in which clustering performance can be maximised. Here, we explore the use of dimensionality reduction methods prior to clustering in order to map high-dimensional data to a lower dimensional space, providing salient features to the subsequent clustering step. We assess the use of deep autoencoders for feature selection prior to applying k-means clustering to the encoded data. We compare this deep clustering method to feature selection using principle component analysis (PCA), uniform manifold approximation and projection (UMAP), as well as applying k-means to the original feature space using either L1 or L2 distance. We provide extensive quantitative evaluation of clustering performance using synthetic datasets, representing data from multiple heterogeneous subjects. In synthetic data we find that deep clustering gives the best performance, while other approaches are often insufficient to capture temporal properties of dFC states. We then demonstrate the application of each method to real-world data from human subjects and show that the choice of feature selection method has a significant effect on group-level measurements of state temporal properties. We therefore advocate for the use of deep clustering as a precursor to clustering in dFC.

neuroscience↗

bioRxiv2021

A Large-Scale Brain Network Mechanism for Increased Seizure Propensity in Alzheimer's Disease

People with Alzheimers disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider increased excitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, both local dynamics and large-scale brain network structure are believed to be crucial for determining seizure likelihood and phenotype. In this study, we combine computational modelling with electrophysiological data to demonstrate a potential large-scale brain network mechanism for increased seizure propensity in people with AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions using an approach previously validated on participants with epilepsy vs controls. As cortical tissue excitability was increased in the simulations, network models of AD simulations were more likely to transition into seizures than simulations from healthy controls. Our results suggest an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. These included cingulate, medial temporal, and orbital regions. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with amyloid/tau pathology and cortical atrophy. Author summaryPeople with Alzheimers disease (AD) are more likely to develop seizures than cognitively healthy people. In this study, we aimed to understand whether whole-brain network structure is related to this increased seizure likelihood. We used electroencephalography (EEG) to estimate brain networks from people with AD and healthy controls. We subsequently inserted these networks into a model brain and simulated disease progression by increasing the excitability of brain tissue. We found the simulated AD brains were more likely to develop seizures than the simulated control brains. No participants had seizures when we collected data, so our results suggest an increased probability of developing seizures at a future time for AD participants. Therefore functional brain network structure may play a role in increased seizure likelihood in AD. We also used the model to examine which brain regions were most important for generating seizures, and found that the seizure-generating regions corresponded to those typically affected in early AD. Our results also provide a potential explanation for why people with AD are more likely to have generalized seizures (i.e. seizures involving the whole brain, as opposed to focal seizures which only involve certain areas) than the general population with epilepsy.

neuroscience↗

bioRxiv2019

EEG microstate complexity for aiding early diagnosis of Alzheimer's disease

IntroductionElectroencephalogram (EEG) is a potentially useful clinical tool for aiding diagnosis of Alzheimers disease (AD). We hypothesized we can increase the accuracy of EEG for aiding diagnosis of AD using microstates, which are epochs of quasi-stability at the millisecond scale. MethodsEEG was collected from two independent cohorts of AD and control participants and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. Microstates were analysed, including a novel measure of complexity. ResultsMicrostate complexity significantly decreased in AD, and when combined with a spectral EEG measure, could classify AD with sensitivity and specificity >80%. These results were validated on an independent cohort and were also found to be generalizable to predict progression from MCI to AD. Additionally, microstates associated with the frontoparietal network were altered in AD. DiscussionEEG has the potential to be a non-invasive functional biomarker that predicts progression from MCI to AD.

neuroscience↗

bioRxiv2019

Quantification and selection of ictogenic zones in epilepsy surgery

Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy.\n\nHere, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we develop a global optimisation approach to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy.

neuroscience↗

bioRxiv2018

Classification of the gifted natural product producer Streptomyces roseofaciens sp. nov. by polyphasic taxonomy

A novel verticillate strain of streptomycetes, Streptomyces strain MBT76T, was isolated from the QinLing mountains, which harbours more than 40 biosynthetic gene clusters for natural products. Here we present full taxonomic classification of strain MBT76T, and show that it has chemotaxonomic, genomic and morphological properties consistent with its classification in the genus Streptomyces. Strain MBT76T is part of the cluster of Streptoverticillates, a group within the genus Streptomyces that has characteristic whorl-forming spores produced in chains along the lateral wall of the hyphae. Multi-locus sequence analysis based on five housekeeping gene alleles showed that MBT76T is closely related to Streptomyces hiroshimensis. Average Nucleotide Identification (ANI) and Genome to Genome Distance Calculation (GGDC) of the genomes of strain MBT76T and S. hiroshimensis separated them into distinct species. Strain MBT76T represents a novel species of the genus Streptomyces for which we propose the name Streptomyces roseofaciens sp. nov. The type strain is MBT76T (=NCCB 100637T =DSM 106196T). The whole genome of MBT76T has 7974 predicted open reading frames and a total genome size of 8.64 Mb. Further genomic analysis showed that verticillate streptomycetes lack the sporulation gene ssgE, and our data suggest that this is a useful genetic marker for the spore-chain morphology of the verticillates.

microbiology↗