Biology subjects

Tait, L.

Publications and source records attributed to Tait, L..

9 recordsLinked to original sources

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↗

bioRxiv2024

Transient cortical Beta-frequency oscillations associated with contextual novelty in high density mouse EEG

Beta-frequency oscillations (20-30 Hz) are prominent in both human and rodent electroencephalogram (EEG) recordings. Discrete epochs of beta (or Beta2) oscillations are prevalent in the hippocampus and other brain areas during exploration of novel environments. However, little is known about the spatial distribution and temporal relationships of beta oscillations across the cortex in response to novelty. To investigate this, mice fitted with 30-channel EEG-style multi-electrode arrays underwent a single recording session in a novel environment. While changes to spectral properties of cortical oscillations were minimal, there was a profound increase in the rate of beta bursts during the initial part of the recording session, when the environment was most novel. This was true across the cortex but most notable in recording channels situated above the retrosplenial cortex. Additionally, novelty was associated with greater connectivity between retrosplenial areas and the rest of the cortex, specifically in the beta frequency range. However, it was also found that the cortex in general, is highly modulated by environmental novelty. This data further suggests the retrosplenial cortex is an important hub for distinguishing environmental context and highlights the diversity of functions for beta oscillations across the brain, which can be observed using high-density EEG.

neuroscience↗

bioRxiv2023

Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes

Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimers disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasise the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.

neuroscience↗

bioRxiv2021

+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for microstate analysis for other neuroimaging modalities such as sensor-space MEG and source-space data. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as microstate-segmented functional connectivity analysis, cluster-permutation topographic ANOVAs, and{chi} 2 analysis of microstate probabilities in response to stimuli. Additionally, codes for simulating microstate sequences and their associated M/EEG data are included in the toolbox, which can be used to generate artificial data with ground truth microstates and to validate the methodology. +microstate integrates with widely used toolboxes for M/EEG processing including Fieldtrip, SPM, LORETA/sLORETA, EEGLAB, and Brainstorm to aid with accessibility, and includes wrappers for pre-existing toolboxes for brain-state estimation such as Hidden Markov modelling (HMM-MAR) and independent component analysis (FastICA) to aid with direct comparison with these techniques. In this paper, we first introduce +microstate before subsequently performing example analyses using open access datasets to demonstrate and validate the methodology. MATLAB live scripts for each of these analyses are included in +microstate, to act as a tutorial and to aid with reproduction of the results presented in this manuscript.

neuroscience↗

bioRxiv2021

Individual variability in the human connectome maintains selective cross-modal consistency and shares microstructural signatures

Individuals are different in their behavioural responses and cognitive abilities. Neural underpinnings of individual differences are largely unknown. Here, by using multimodal imaging data including diffusion MRI, functional MRI and MEG, we show the consistency of interindividual variation of connectivity across modalities. We demonstrated that regional differences in individual variability of structural and functional connectomes is characterized by higher variability in association cortices and lower variability in sensory and visual cortices. This pattern is consistent across all modalities at varying degrees as shown by significant alignment between functional and structural connectome variabilities at several clusters of brain regions. Variability in connectivity is associated with cortical myelin content and microstructural properties of connections. Our findings contribute to understanding of individual differences in functional and structural organization of brain and facilitate fingerprinting applications.

neuroscience↗

bioRxiv2021

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

EEG microstate analysis is a useful approach for studying brain states - nicknamed atoms of thought - and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight into the cortical mechanisms underpinning these states. In this study, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level MEG microstates were associated with distinct functional connectivity patterns. Using a passive auditory paradigm, we further demonstrated that the occurrence probability of MEG microstates were altered by evoked auditory responses, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of MEG source-level microstates as a data-driven method for investigating brain dynamic activity and connectivity at the millisecond scale.

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↗

bioRxiv2020

Cortical source imaging of resting-state MEG with a high resolution atlas: An evaluation of methods

Non-invasive functional neuroimaging of the human brain at rest can give crucial insight into the mechanisms that underpin healthy cognition and neurological disorders. Magnetoencephalography (MEG) measures extracranial magnetic fields originating from neuronal activity with very high temporal resolution, but requires source reconstruction to make neuroanatomical inferences from these signals. Many source reconstruction algorithms for task-based MEG data are available. However, no consensus yet exists on the optimum algorithm for resting-state data. Here, we evaluated the performance of six commonly-used source reconstruction algorithms based on minimum-norm and beamforming estimates. In the context of human resting-state MEG, we compared the algorithms using quantitative metrics, including resolution properties of inverse solutions and explained variance in sensor-level data. Next, we proposed a data-driven approach to reduce the atlas from the Human Connectome Projects multimodal parcellation of the human cortex. This procedure produced a reduced cortical atlas with 250 regions, optimized to match the spatial resolution and the rank of MEG data from the current generation of MEG scanners. For both voxel-wise and parcellated source reconstructions, we showed that the eLORETA inverse algorithm had zero localization error, high spatial resolution, and superior performance in predicting sensor-level activity. Our comprehensive comparisons and recommandations can serve as a guide for choosing appropriate methodologies in future studies of resting-state MEG.

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↗