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MRS-BIDS, an extension to the Brain Imaging Data Structure for magnetic resonance spectroscopy.
The Brain Imaging Data Structure (BIDS) is an increasingly adopted standard for organizing scientific data and metadata. It facilitates easier and more straightforward data sharing and reuse. BIDS currently encompasses several biomedical imaging and non-imaging techniques, and as more research groups begin to use it, additional experimental techniques are being incorporated into the standard, allowing diverse experimental methods to be stored within the same cohesive structure. Here, we present an extension for magnetic resonance spectroscopy (MRS) data, termed MRS-BIDS.
Dual-site beta transcranial alternating current stimulation during a bimanual coordination task modulates functional connectivity between motor areas.
BACKGROUND: Communication within brain networks depends on functional connectivity. One promising approach to modulate such connectivity between cortical areas is dual-site transcranial alternating current stimulation (tACS), which non-invasively applies weak alternating currents to two brain areas. OBJECTIVES: /Hypotheses: In the current study, we aimed to modulate inter-regional functional connectivity with dual-site tACS to bilateral primary motor cortices (M1s) during bimanual coordination and, in turn, alter behaviour. METHODS: Using functional magnetic resonance imaging (fMRI), we recorded participants' brain responses during a bimanual coordination task in a concurrent tACS-fMRI design. While performing a slow and fast version of the task, participants received one of three types of beta (20 Hz) dual-site tACS over both M1s: zero-phase, jittered-phase or sham, in a within-participant, repeated measures design. RESULTS: While we did not observe any significant tACS effects on behaviour, the study revealed an attenuation effect of zero-phase tACS on interhemispheric connectivity. Additionally, the two active types of tACS (zero-phase and jittered-phase) differed in the task-related M1 connectivity with other motor cortical regions, such as premotor cortex and supplementary motor area. Furthermore, individual E-field strengths were related to functional connectivity in the zero-phase condition. CONCLUSIONS: Dual-site beta tACS over both M1s altered functional connectivity between motor areas. However, this effect did not translate significantly to the behavioural level in the presence of a restricted sample size. Future studies may thus integrate mechanistic measures, such as measures of interhemispheric inhibition, to strengthen causal interpretations.
Urinary P75: a promising biomarker for amyotrophic lateral sclerosis.
BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive and fatal disease. The urinary neurotrophin receptor p75 extracellular domain (p75ECD) has previously been reported as a potential disease biomarker for diagnosis, severity assessment and monitoring therapeutic response. METHODS: This study measured urinary p75ECD using an enzyme-linked immunoassay and normalised the results against urinary creatinine. Participants were recruited via A Multicentre Biomarker Resource Strategy in ALS (AMBroSIA) programme. Study participants included 97 ALS patients, 24 of whom were studied longitudinally, and 27 healthy controls. The study focused on urinary p75ECD and its potential association with different subtypes of ALS, change over time, disease progression, severity of symptoms and survival from symptom onset. RESULTS: Confirming previous findings, urinary p75ECD levels were significantly higher in patients with ALS (median 6.78 ng/mg, 95% CI (5.12 to 9.23)) compared with controls (4.57 ng/mg, 95% CI (3.35 to 5.89)) at first study visit. There was a significant negative correlation between absolute change in the Revised ALS Functional Rating Scale score and p75ECD levels (Spearman's rho=-0.371, p≤0.0004, 95% CI (-0.543 to -0.169)), indicating that an increase in the severity of motor neuron injury correlated with an increase in p75ECD levels. There was a significant increase in p75ECD between first and second samples in the same participants, indicating an increase in the level of this biomarker longitudinally during the disease course (moderate effect size of -0.3). CONCLUSIONS: Urinary p75ECD is a promising candidate as a biomarker, which increases with disease progression and has the potential to serve as a pharmacodynamic biomarker.
The past, present, and future of the brain imaging data structure (BIDS).
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.
Altered network efficiency in isolated REM sleep behavior disorder: A multicentric study.
INTRODUCTION: Isolated rapid eye movement (REM) sleep behavior disorder (iRBD), characterized by abnormal movements during REM sleep, is a prodromal stage of dementia with Lewy bodies (DLB) and Parkinson's disease (PD). While iRBD shows emerging brain changes, their impact on structural connectivity and network efficiency, and their predictive value, remain poorly characterized. METHODS: In this international prospective study, 198 polysomnography-confirmed iRBD patients and 174 controls underwent diffusion magnetic resonance imaging and were analyzed. Cutting-edge diffusion tractography and network-based statistics were applied to reconstruct individual connectomes and assess network properties predicting DLB or PD. RESULTS: Structural architecture was already disrupted in iRBD, with both reduced and compensatory increased connections. Global efficiency was decreased. Local efficiency in motor regions was altered and associated with early clinical symptoms. Altered local efficiency in the supramarginal gyrus predicted DLB only. DISCUSSION: Early disruption of brain architecture in iRBD predicts progression to synucleinopathy-related dementia, offering a novel potential prognostic biomarker. HIGHLIGHTS: Isolated rapid eye movement sleep behavior disorder (iRBD) patients show significant alterations in inter-regional structural connectivity. Global efficiency is reduced in iRBD compared to controls. Areas with increased local efficiency contribute to decreased global efficiency. Altered network efficiency is associated with emerging Parkinsonian features. Higher supramarginal efficiency predicts dementia with Lewy bodies in iRBD.
Detection of cognitive deficits years prior to clinical diagnosis across neurological conditions
Abstract Understanding the cognitive trajectory of a neurological disease can provide important insight on underlying mechanisms and disease progression. Cognitive impairment is now well established as beginning many years before the diagnosis of Alzheimer’s disease, but pre-diagnostic profiles are unclear for other neurological conditions that may be associated with cognitive impairment. We analysed data from the prospective UK Biobank cohort with study baseline assessment performed between 2006-2010 and participants followed until 2021. We examined data from 497,252 participants, aged between 38 and 72 years at baseline, with an imaging sub-sample of 42,468 participants. Using time-to-diagnosis and time-from-diagnosis data in relation to time of assessment, we compared a continuous measure of executive function and magnetic resonance imaging brain measures of total grey matter and hippocampal volume in individuals with ischaemic stroke, focal epilepsy, Parkinson’s disease, multiple sclerosis, motor neurone disease (amyotrophic lateral sclerosis) and migraine. Of the 497,252 participants (226,206 [45.5%] men, mean [SD] age, 57.5[8.1] years), 12,755 had ischaemic stroke, 6,758 had a diagnosis of focal epilepsy, 3,315 had Parkinson’s disease, 2,315 had multiple sclerosis, 559 had motor neurone disease and 18,254 had migraine either at study baseline or diagnosed during the follow-up period. Apart from motor neurone disease, all conditions had lower pre-diagnosis executive function compared to controls (assessment performed median 7.4 years before diagnosis). At a group level, focal epilepsy and multiple sclerosis showed a gradual worsening in executive function up to 15 years prior to diagnosis, while ischaemic stroke was characterised by a modest decline for a few years followed by a substantial reduction at the time of diagnosis. By contrast, participants with migraine showed a mild reduction in pre-diagnosis cognition compared to controls which improved following clinical diagnosis. Pre-diagnosis MRI grey matter volume was lower than controls for stroke, Parkinson’s disease and multiple sclerosis (scans performed median 1.7 years before diagnosis), while other conditions had lower volumes post-diagnosis. These cognitive trajectory models reveal disease-specific temporal patterns at a group level, including a long cognitive prodrome associated with focal epilepsy and multiple sclerosis. The findings may help to prioritise risk management of individual diseases and inform clinical decision-making.
Apathy and impulsivity in neurological and psychiatric disorders
Apathy and impulsivity are debilitating syndromes of motivation that are common across neurological and psychiatric disorders. Both are associated with pathology within well described fronto-striatal networks where dopaminergic neurons play an important role in regulating motivated behavior. In this chapter, we investigate whether dopaminergic dysfunction within this network is associated with either apathetic or impulsive behavior. We focus on patients with Parkinson's disease and schizophrenia, investigating a wide range of behavioral and neuroimaging studies. Current findings suggest that both apathy and impulsivity are associated with altered responsiveness to rewards during decision-making and altered function within fronto-striatal networks. While dopaminergic therapy also alters reward sensitivity, there are instances where the effects of apathy and/or impulsivity on this metric are independent of—and extend beyond—dopaminergic tone. This suggests a more nuanced relationship between fronto-striatal dopamine and human motivation syndromes that warrants further investigation.
Automated detection of lacunes in brain MR images using SAM with robust prompts using self-distillation and anatomy-informed priors.
BACKGROUND: Lacunes, which are small fluid-filled cavities in the brain, are signs of cerebral small vessel disease and have been clinically associated with various neurodegenerative and cerebrovascular diseases. Hence, accurate detection of lacunes is crucial and is one of the initial steps for the precise diagnosis of these diseases. However, developing a robust and consistently reliable method for detecting lacunes is challenging because of the heterogeneity in their appearance, contrast, shape, and size. METHOD: In this study, we propose a lacune detection method using the Segment Anything Model (SAM), guided by point prompts from a candidate prompt generator. The prompt generator initially detects potential lacunes with a high sensitivity using a composite loss function. The true lacunes are then selected using SAM by discriminating their characteristics from mimics such as the sulcus and enlarged perivascular spaces, imitating the clinicians' strategy of examining the potential lacunes along all three axes. False positives are further reduced by adaptive thresholds based on the region wise prevalence of lacunes. RESULTS: We evaluated our method on two diverse, multi-centric MRI datasets, VALDO and ISLES, comprising only FLAIR sequences. Despite diverse imaging conditions and significant variations in slice thickness (0.5-6 mm), our method achieved sensitivities of 84% and 92%, with average false positive rates of 0.05 and 0.06 per slice in ISLES and VALDO datasets respectively. CONCLUSIONS: The proposed method demonstrates robust performance across varied imaging conditions and outperformed the state-of-the-art methods, demonstrating its effectiveness in lacune detection and quantification.
AI-driven reclassification of multiple sclerosis progression.
Multiple sclerosis (MS) affects 2.9 million people. Traditional classification of MS into distinct subtypes poorly reflects its pathobiology and has limited value for prognosticating disease evolution and treatment response, thereby hampering drug discovery. Here we report a data-driven classification of MS disease evolution by analyzing a large clinical trial database (approximately 8,000 patients, 118,000 patient visits and more than 35,000 magnetic resonance imaging scans) using probabilistic machine learning. Four dimensions define MS disease states: physical disability, brain damage, relapse and subclinical disease activity. Early/mild/evolving (EME) MS and advanced MS represent two poles of a disease severity spectrum. Patients with EME MS show limited clinical impairment and minor brain damage. Transitions to advanced MS occur via brain damage accumulation through inflammatory states, with or without accompanying symptoms. Advanced MS is characterized by moderate to high disability levels, radiological disease burden and risk of disease progression independent of relapses, with little probability of returning to earlier MS states. We validated these results in an independent clinical trial database and a real-world cohort, totaling more than 4,000 patients with MS. Our findings support viewing MS as a disease continuum. We propose a streamlined disease classification to offer a unifying understanding of the disease, improve patient management and enhance drug discovery efficiency and precision.
Predicting Cognition and Affective Changes in Newly Diagnosed Parkinson’s Disease Through Longitudinal Data-Driven Clustering
Background: Although primarily characterised as a motor disorder, Parkinson’s Disease (PD) also presents with non-motor symptoms, including cognitive decline and affective dysfunction, which are major predictors of quality of life and mortality for individuals. However, factors associated with these non-motor symptom trajectories remain under-characterised. Purpose: This study aimed to investigate predictors of cognitive and affective function over a 5-year follow-up period using data from the Progressive Parkinson’s Marker Initiative. Results: Fuzzy C-means clustering analysis of year-5 cognitive and affective function scores showed two clusters. The second group (n = 96) were older and had worse cognition, affective, and motor functioning at year-5 follow-up compared to the first (n = 213). Predictors of cluster membership was assessed in n = 113 individuals for whom data on all variables of interest were available (cluster 1/2 = 79/34). Cluster membership at 5-year follow-up was significantly predicted by baseline cognitive and affective function, as well as decreased levels of CSF amyloid-beta and increased CSF concentrations of phosphorylated-tau at baseline. Alternative non-linear supervised machine learning model (support vector regressor) using the same predictors improved classification accuracy by 5%. Conclusion: Our analysis highlights that including established biomarkers of other neurocognitive disorders (namely, amyloid-beta and phosphorylated-tau) also has utility for predicting cognitive and affective trajectory in PD. This suggests that assessing a multi-modal panel of prognostic markers, beyond clinical symptom presentation alone, may have utility for informing prognosis of cognitive and affective outcomes in PD. This is significant, potentially allowing for the earlier development of personalised therapeutic interventions for those at highest risk of impairment within these non-motor domains.
TDP-43 pathology is associated with divergent protein profiles in ALS brain and spinal cord.
Neuronal and glial cytoplasmic inclusions positive for TAR DNA-binding protein 43 (TDP-43) are the defining pathological hallmark of 97% of amyotrophic lateral sclerosis (ALS) and 50% of frontotemporal dementia (FTD). The ALS-FTD clinicopathological spectrum variably involves cortical and spinal anterior horn cell pathology. The broader protein composition of these inclusions is of major importance to understanding pathogenesis, clinical heterogeneity and biomarker development. This study examined the proteome associated with TDP-43 inclusions in ALS, using mass spectrometry-based proteomic analysis of spinal cord and cerebral cortex from donors with phosphoTDP-43 positive ALS (n = 16), alpha-synuclein positive Parkinson's disease (PD, n = 8), phosphotau and beta-amyloid positive Alzheimer's disease (AD, n = 8) and age matched non-neurological controls (n = 8), comparing ALS with non-ALS conditions, spinal cord with cerebral cortex samples, and detergent-soluble with -insoluble fractions. Increased abundance of TDP-43 in the detergent-insoluble fraction of ALS cortex and spinal cord tissue confirmed disease-specific protein enrichment by serial fractionation. The most striking alterations between ALS and other conditions were found in the detergent-insoluble fraction of spinal cord, with predominant enrichment of endosomal and extracellular vesicle pathways. In the cortex mitochondrial membrane/envelope and ion transmembrane transport pathways were enriched in the detergent-insoluble fraction. RNA/DNA metabolic processes (in spinal cord) versus mitochondrial and synaptic protein pathways (in cortex) were upregulated in the detergent-soluble fraction of ALS cases and downregulated in the insoluble protein fraction. Whilst motor cortex and spinal cord may not optimally reflect disease-specific pathways in AD, in PD a significant enrichment of alpha-synuclein in the detergent-insoluble fraction of spinal cord was found. Among proteins concordantly elevated in the detergent-insoluble fractions of spinal cord and cortex, there was greater representation of proteins encoded by ALS-associated genes, specifically Cu/Zn superoxide dismutase 1, valosin containing protein and TDP-43 (odds ratio 16.34, p = 0.002). No significant increase in TDP-43 interacting proteins was observed in either detergent-soluble or -insoluble fractions. Together, this study shows a divergence in the composition of proteins associated with TDP-43 positive detergent-insoluble inclusions between spinal cord and cerebral cortex. A common upregulation of proteins encoded by ALS-causing genes implicates their role in the pathogenesis of the ALS-FTD spectrum of diseases beyond TDP-43. Data are available via ProteomeXchange with identifier PXD067060.
Our team
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Sankalp Garud
Postdoctoral Researcher
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Mayra Albayrak
Research Assistant
