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•The fixel-based analysis framework was proposed for fibre-specific statistical analysis of diffusion MRI data.•A “fixel” represents an individual fibre population in a voxel, allowing for increased specificity over voxel-wise measures.•A state-of-the-art fixel-based analysis pipeline consists of several bespoke steps, but is conceptually similar to a voxel-based analysis.•Fixel-based analysis has seen increased adoption recently, with 75 published studies to date.•The framework has unique benefits and future opportunities, but specific challenges and limitations exist as well. Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple “crossing” fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the “Fixel-Based Analysis” (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities. [Display omitted]

ObjectivePsychosis of epilepsy (POE) occurs more frequently in temporal lobe epilepsy, raising the question as to whether abnormalities of the hippocampus are aetiologically important. Despite decades of investigation, it is unclear whether hippocampal volume is reduced in POE, perhaps due to small sample sizes and methodological limitations of past research.MethodsIn this study, we examined the volume of the total hippocampus, and the hippocampal head, body and tail, in a large cohort of patients with POE and patients with epilepsy without psychosis (EC). One hundred adults participated: 50 with POE and 50 EC. Total and subregional hippocampal volumes were manually traced and compared between (1) POE and EC; (2) POE with temporal lobe epilepsy, extratemporal lobe epilepsy and generalised epilepsy; and (3) patients with POE with postictal psychosis (PIP) and interictal psychosis (IP).ResultsCompared with EC the POE group had smaller total left hippocampus volume (13.5% decrease, p<0.001), and smaller left hippocampal body (13.3% decrease, p=0.002), and left (41.5% decrease, p<0.001) and right (36.4% decrease, p<0.001) hippocampal tail volumes. Hippocampal head volumes did not differ between groups.ConclusionPosterior hippocampal volumes are bilaterally reduced in POE. Volume loss was observed on a posteroanterior gradient, with severe decreases in the tail and moderate volume decreases in the body, with no difference in the hippocampal head. Posterior hippocampal atrophy is evident to a similar degree in PIP and IP. Our findings converge with those reported for the paradigmatic psychotic disorder, schizophrenia, and suggest that posterior hippocampal atrophy may serve as a biomarker of the risk for psychosis, including in patients with epilepsy.

Objective Neuropsychological comorbidities found in chronic epilepsy have also been reported earlier in the disease course. However, recurrent seizures, antiseizure medication (ASM), and adjustment to a chronic diagnosis remain potential confounds of this literature. It thus remains unclear whether these comorbidities are primary or secondary attributes of epilepsy. To capture individuals as close to disease onset as possible, we studied the cognitive and psychological functioning in adults after their first seizure, yet prior to epilepsy diagnosis and treatment. Methods Using a telehealth‐based prospective design, we screened cognition, mood, and anxiety symptoms in adult patients referred to a First Seizure Clinic (FSC), who were over 18 years, English‐speaking and not taking ASM. We screened cognition via telephone, and psychological symptoms via online questionnaires, all prior to the patients' diagnostic evaluation. Data were collected on 32 individuals subsequently diagnosed with epilepsy at the FSC, and 30 healthy controls from the community, who were matched to the epilepsy group for age, gender, and education. Results A multivariate analysis of variance revealed that the groups differed significantly on combined cognitive measures with a large effect size (F[1,56] = 5.75, p < 0.001, η2 = 0.45). Post‐hoc analyses showed that performances on measures of verbal memory, working memory, and executive functions were significantly worse for the newly diagnosed epilepsy group than controls. The epilepsy group also exhibited higher rates of clinically significant depressive and anxiety symptoms. Significance Cognitive and psychological dysfunction is prevalent in people with epilepsy as early as the first seizure event, before the influence of diagnosis, ASM and recurrent seizures. Their neuropsychological profile parallels that seen in chronic epilepsy, showing that this dysfunction is already present at the very onset of the disease. The current study demonstrates the viability of telehealth neuropsychological screening for all new epilepsy cases. Plain Language Statement The results of this study show, using telephone‐based cognitive assessment and online questionnaires, that people with newly diagnosed epilepsy can experience problems with their thinking and memory skills, and low mood and anxiety, as early as after their first seizure. These issues are apparent at the very beginning of the disease, before an epilepsy diagnosis is made and before antiseizure medication is commenced, which suggests that they are due to the underlying brain disturbance, rather than the secondary effects of seizures, treatment, or lifestyle changes. Telehealth‐screening of thinking skills and mental health for all new epilepsy cases is recommended to promote early management of such problems.

IntroductionSelection of antiseizure medications (ASMs) for newly diagnosed epilepsy remains largely a trial-and-error process. We have developed a machine learning (ML) model using retrospective data collected from five international cohorts that predicts response to different ASMs as the initial treatment for individual adults with new-onset epilepsy. This study aims to prospectively evaluate this model in Australia using a randomised controlled trial design.Methods and analysisAt least 234 adult patients with newly diagnosed epilepsy will be recruited from 14 centres in Australia. Patients will be randomised 1:1 to the ML group or usual care group. The ML group will receive the ASM recommended by the model unless it is considered contraindicated by the neurologist. The usual care group will receive the ASM selected by the neurologist alone. Both the patient and neurologists conducting the follow-up will be blinded to the group assignment. Both groups will be followed up for 52 weeks to assess treatment outcomes. Additional information on adverse events, quality of life, mood and use of healthcare services and productivity will be collected using validated questionnaires. Acceptability of the model will also be assessed.The primary outcome will be the proportion of participants who achieve seizure-freedom (defined as no seizures during the 12-month follow-up period) while taking the initially prescribed ASM. Secondary outcomes include time to treatment failure, time to first seizure after randomisation, changes in mood assessment score and quality of life score, direct healthcare costs, and loss of productivity during the treatment period.This trial will provide class I evidence for the effectiveness of a ML model as a decision support tool for neurologists to select the first ASM for adults with newly diagnosed epilepsy.Ethics and disseminationThis study is approved by the Alfred Health Human Research Ethics Committee (Project 130/23). Findings will be presented in academic conferences and submitted to peer-reviewed journals for publication.Trial registration numberACTRN12623000209695.

Voxel-based analysis of diffusion MRI data is increasingly popular. However, most white matter voxels contain contributions from multiple fibre populations (often referred to as crossing fibres), and therefore voxel-averaged quantitative measures (e.g. fractional anisotropy) are not fibre-specific and have poor interpretability. Using higher-order diffusion models, parameters related to fibre density can be extracted for individual fibre populations within each voxel (‘fixels’), and recent advances in statistics enable the multi-subject analysis of such data. However, investigating within-voxel microscopic fibre density alone does not account for macroscopic differences in the white matter morphology (e.g. the calibre of a fibre bundle). In this work, we introduce a novel method to investigate the latter, which we call fixel-based morphometry (FBM). To obtain a more complete measure related to the total number of white matter axons, information from both within-voxel microscopic fibre density and macroscopic morphology must be combined. We therefore present the FBM method as an integral piece within a comprehensive fixel-based analysis framework to investigate measures of fibre density, fibre-bundle morphology (cross-section), and a combined measure of fibre density and cross-section. We performed simulations to demonstrate the proposed measures using various transformations of a numerical fibre bundle phantom. Finally, we provide an example of such an analysis by comparing a clinical patient group to a healthy control group, which demonstrates that all three measures provide distinct and complementary information. By capturing information from both sources, the combined fibre density and cross-section measure is likely to be more sensitive to certain pathologies and more directly interpretable. [Display omitted] •A fixel is defined as a specific fibre population within a voxel.•We describe a comprehensive approach to fixel-based analysis (FBA) of white matter.•A novel method to investigate fibre bundle morphology (cross-section) is presented.•We compare fibre density, cross-section and a combined measure in a clinical cohort.•The three different analyses give unique yet complementary information.

Objective To examine the neuropsychological morbidity across the spectrum of patients presenting to a First Seizure Clinic, and test the hypothesis that cognitive and psychological compromise is especially prominent in those diagnosed with epilepsy. Methods A sample of 201 patients referred to the Austin Hospital First Seizure Clinic (FSC) underwent cognitive screening via telephone and psychological screening via online questionnaire, all prior to their diagnostic evaluation (and any attendant treatment recommendation) at the FSC. Rates of cognitive (i.e., scores <10th percentile) and psychological impairment (using established clinical cut scores) were compared against 35 demographically matched controls. Cognitive differences were explored between the most frequently encountered patient subgroups (epilepsy, n = 48; first unprovoked seizure, n = 24; acute symptomatic seizure, n = 24; syncope, n = 35) via a multivariate analysis of variance, with diagnostic labels applied retrospectively after a period of follow‐up. Results People with epilepsy were most likely to show cognitive impairments, particularly in learning and memory, with performances worse than all other FSC groups (F [3127] = 2.44, p = 0.03). Clinically significant depressive symptoms were similarly prevalent in all patient groups, with one in three at risk for Major Depressive Disorder. Elevated anxiety symptoms were common across patient groups; however, not significantly different to controls. Significance Cognitive impairment in epilepsy and mood problems in all FSC groups are detectable via remote screening as early as the first seizure. Learning and memory difficulties are particularly prevalent in new‐onset epilepsy and may lend diagnostic information when paired with clinical factors. Plain Language Summary This study explored cognitive and psychological differences between various patient groups attending an Australian First Seizure Clinic. We found that learning and memory abilities were poorer in people with epilepsy than other patient groups including those with non‐epileptic seizures, and seizure‐mimics (fainting episodes). Therefore, along with standard epilepsy investigations, memory performances could help to predict which patients have epilepsy versus a non‐epileptic condition after a first suspected seizure. Further, approximately one in three from each patient group showed high symptoms of depression and anxiety. The findings highlight the importance of evaluating cognition and mood in people with first seizures.

Objective Memory is one of the most sensitive markers of cognitive compromise in people with new‐onset epilepsy. Nonetheless, around half of these cases score within the normal range on standard memory testing. Here we explore whether memory retention at a 1‐week delay reveals otherwise undetected memory compromise in such individuals, and how it relates to subjective memory complaints and mood. Methods Using a prospective design, 38 adults with new‐onset epilepsy underwent baseline memory screening via telephone using an abbreviated Rey Auditory Verbal Learning Test (RAVLT). Psychological screening occurred via online questionnaires. One week later, without forewarning, participants completed three follow‐up memory tasks. Of particular focus, we explored longer‐term memory performances and forgetting trajectories in those individuals (n = 23) who demonstrated normal memory performances (scores >10th percentile) at baseline (30‐min delay). Outcomes were compared to 32 healthy controls, matched for age, sex, and education. Results As a group, people with epilepsy performed worse than controls on all memory measures, with 44 percent impaired at baseline testing. Of those unimpaired at baseline, the rate and volume of information loss over 1 week was significantly greater than for controls. Contextual memory performance at 1 week was also significantly poorer for people with epilepsy. At the individual level, the prevalence of impaired forgetting was not significantly different between patients and controls. Subjective memory complaints were not related to any objective tests but were strongly related to self‐reported mood and anxiety symptoms. Significance People with new‐onset epilepsy show reduced memory at short and extended intervals. For those showing normal memory at baseline, information does appear to be forgotten more quickly than in healthy controls, though the effect is not large. The findings indicate that while extended delay memory testing is not essential in all new epilepsy cases, it could provide useful information for particular individuals. Plain Language Statement Memory problems are common in people with epilepsy shortly after seizure onset, however, many individuals still show normal memory performances on standard neuropsychological testing. Through testing memory at an extended timepoint (1 week), our study found that on average, these individuals showed a slightly quicker rate of forgetting over a 1‐week period than people without a brain condition. Self‐reported memory complaints in people with new epilepsy were unrelated to their actual memory skills on testing at short and long timepoints but were rather linked to lower mood and quality of life.

Objective Epilepsy is a common and serious neurological disorder. This cross‐sectional analysis addresses the burden of epilepsy at different stages of the disease. Methods This pilot study is embedded within the Australian Epilepsy Project (AEP), aiming to provide epilepsy support through a national network of dedicated sites. For this analysis, adults aged 18–65 years with first unprovoked seizure (FUS), newly diagnosed epilepsy (NDE), or drug‐resistant epilepsy (DRE) were recruited between February–August 2022. Baseline clinicodemographic data were collected from the participants who completed questionnaires to assess their quality of life (QOLIE‐31, EQ‐5D‐5L), work productivity (Work Productivity and Activity Impairment [WPAI]), and care needs. Univariate analysis and multivariate regression was performed. Results 172 participants formed the study cohort (median age 34, interquartile range [IQR]: 26–45), comprising FUS (n = 44), NDE (n = 53), and DRE (n = 75). Mean QOLIE‐31 score was 56 (standard deviation [SD] ± 18) and median EQ‐5D‐5L score was 0.77 (IQR: 0.56–0.92). QOLIE‐31 but not EQ‐5D‐5L scores were significantly lower in the DRE group compared to FUS and NDE groups (p < 0.001). Overall, 64.5% of participants participated in paid work, with fewer DRE (52.0%) compared with FUS (76.7%) and NDE (72.5%) (p < 0.001). Compared to those not in paid employment, those in paid employment had significantly higher quality of life scores (p < 0.001). Almost 5.8% of participants required formal care (median 20 h/week, IQR: 12–55) and 17.7% required informal care (median 16 h/week, IQR: 7–101). Significance Epilepsy is associated with a large burden in terms of quality of life, productivity and care needs. Plain Language Summary This is a pilot study from the Australian Epilepsy Project (AEP). It reports health economic data for adults of working age who live with epilepsy. It found that people with focal drug‐resistant epilepsy had lower quality of life scores and were less likely to participate in paid employment compared to people with new diagnosis epilepsy. This study provides important local data regarding the burden of epilepsy and will help researchers in the future to measure the impact of the AEP on important personal and societal health economic outcomes.

In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as fractional anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method by comparing apparent fibre density between motor neurone disease (MND) patients with control subjects. The MND results illustrate the benefit of fixel-specific statistical inference in white matter regions that contain crossing fibres. [Display omitted] •We introduce the fixel—a specific fibre population within a voxel.•A novel method for whole-brain fixel-based analysis of diffusion MRI is presented.•Structural connectivity between fixels is derived from template-based tractography.•Connectivity information is used for tract-specific smoothing and enhancement.•Quantitative assessment and an in vivo demonstration is performed.

With technology such as simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) now more readily available, it is possible to non-invasively map epileptiform activity throughout the entire brain at millimeter resolution. In application to MTLE and healthy controls, three distinct reliable networks were revealed: two that exhibited increased activity in patients (a network including hippocampus and amygdala bilaterally, and a network including postcentral gyri and temporal poles), and a network identified as specific to healthy controls (i.e., effectively decreased in patients, consisting of bilateral precuneus, anterior cingulate, thalamus, and parahippocampal gyrus). Confirming with computer modeling and phantom measurements that lead positioning can have a substantial effect on the amplitude of the MRI gradient artifact present on the EEG, they optimized the positions in a novel cap design. [...]whilst this substantially reduced gradient artifact amplitude on the phantom, it made things worse when used on human subjects. [...]improvements in model accuracy are required if one is to make accurate predictions for the human context.