A meta-analysis of structural MRI studies of the brain in systemic lupus erythematosus (SLE)

A comprehensive search of published literature in brain volumetry was conducted in three autoimmune diseases — systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and ulcerative colitis (UC) — with the intention of performing a meta-analysis of published data. Due to the lack of data in RA and UC, the reported meta-analysis was limited to SLE. The MEDLINE database was searched for studies from 1988 to March 2022. A total of 175 papers met the initial inclusion criteria, and 16 were included in a random-effects meta-analysis. The reduction in the number of papers included in the final analysis is primarily due to the lack of overlap in measured and reported brain regions. A significantly lower volume was seen in patients with SLE in the hippocampus, corpus callosum, and total gray matter volume measurements as compared to age- and sex-matched controls. There were not enough studies to perform a meta-analysis for RA and UC; instead, we include a summary of published volumetric studies. The meta-analyses revealed structural brain abnormalities in patients with SLE, suggesting that lower global brain volumes are associated with disease status. This volumetric difference was seen in both the hippocampus and corpus callosum and total gray matter volume measurements. These results indicate both gray and white matter involvements in SLE and suggest there may be both localized and global reductions in brain volume

The influence of stimulus onset asynchrony, task order, sex and hormonal contraception on prepulse inhibition and prepulse facilitation: Methodological considerations for drug and imaging research

Data availability statement: The data that support the findings of this study are available from the corresponding author [LN], upon reasonable request.Footnotes: Rights retention strategy: For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.Copyright © The Author(s) 2022. Background: Prepulse-induced startle modulation occurs when a weak sensory stimulus (‘prepulse’) is presented before a startling sensory stimulus (‘pulse’), producing an inhibited (Prepulse Inhibition, PPI) or facilitated (Prepulse Facilitation, PPF) startle response. We recently identified several gaps and outlined future lines of enquiry to enable a fuller understanding of the neurobiology of PPI and PPF in healthy and clinical populations. However, before embarking on these studies, it is important to consider how task and population characteristics affect these phenomena in healthy humans. Methods: We examined PPI and PPF in separate tasks, with counterbalanced task order across participants in one session, using a range of stimulus onset asynchronies (SOAs), in 48 healthy adults (23 men, 25 women; 10 hormonal contraceptive users) to determine which SOAs produce the strongest PPI and PPF and also explored how sex and hormonal contraception might influence PPI and PPF under these experimental conditions. Results: Both PPI and PPF were affected by SOA, with greatest PPI observed at 60 and 120 ms, and greatest PPF at 4500 and 6000 ms. PPI was influenced by sex (more PPI in men than women) and hormonal contraception, whereas PPF was affected by task order (greater PPF when the PPF task followed, rather than preceded, the PPI task). Conclusions: Our findings indicate that studies of PPI and PPF need to consider, not only sex and hormonal status of study participants, but also task characteristics and presentation order to reduce variance and increase replicability across studies.Lido CTP Unilever; Biotechnology and Biological Sciences Research Council (BBSRC)

Beyond Patient Reported Pain: Perfusion Magnetic Resonance Imaging Demonstrates Reproducible Cerebral Representation of Ongoing Post-Surgical Pain

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Multivariate decoding of brain images using ordinal regression.

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection

Neural mapping of prepulse-induced startle reflex modulation as indices of sensory information processing in healthy and clinical populations: A systematic review

© 2021 The Authors. Startle reflex is modulated when a weaker sensory stimulus (“prepulse”) precedes a startling stimulus (“pulse”). Prepulse Inhibition (PPI) is the attenuation of the startle reflex (prepulse precedes pulse by 30–500 ms), whereas Prepulse Facilitation (PPF) is the enhancement of the startle reflex (prepulse precedes pulse by 500–6000 ms). Here, we critically appraise human studies using functional neuroimaging to establish brain regions associated with PPI and PPF. Of 10 studies, nine studies revealed thalamic, striatal and frontal lobe activation during PPI in healthy groups, and activation deficits in the cortico-striato-pallido-thalamic circuitry in schizophrenia (three studies) and Tourette Syndrome (two studies). One study revealed a shared network for PPI and PPF in frontal regions and cerebellum, with PPF networks recruiting superior medial gyrus and cingulate cortex. The main gaps in the literature are (i) limited PPF research and whether PPI and PPF operate on separate/shared networks, (ii) no data on sex differences in neural underpinnings of PPI and PPF, and (iii) no data on neural underpinnings of PPI and PPF in other clinical disorders.Laura F. Naysmith is funded by Lido CTP Unilever, Biotechnology and Biological Sciences Research Council (BBSRC)

The autonomic brain: multi-dimensional generative hierarchical modelling of the autonomic connectome

The autonomic nervous system governs the body's multifaceted internal adaptation to diverse changes in the external environment, a role more complex than is accessible to the methods — and data scales — hitherto used to illuminate its operation. Here we apply generative graphical modelling to large-scale multimodal neuroimaging data encompassing normal and abnormal states to derive a comprehensive hierarchical representation of the autonomic brain. We demonstrate that whereas conventional structural and functional maps identify regions jointly modulated by parasympathetic and sympathetic systems, only graphical analysis discriminates between them, revealing the cardinal roles of the autonomic system to be mediated by high-level distributed interactions. We provide a novel representation of the autonomic system — a multidimensional, generative network — that renders its richness tractable within future models of its function in health and disease

The autonomic brain: Multi-dimensional generative hierarchical modelling of the autonomic connectome.

The autonomic nervous system governs the body's multifaceted internal adaptation to diverse changes in the external environment, a role more complex than is accessible to the methods-and data scales-hitherto used to illuminate its operation. Here we apply generative graphical modelling to large-scale multimodal neuroimaging data encompassing normal and abnormal states to derive a comprehensive hierarchical representation of the autonomic brain. We demonstrate that whereas conventional structural and functional maps identify regions jointly modulated by parasympathetic and sympathetic systems, only graphical analysis discriminates between them, revealing the cardinal roles of the autonomic system to be mediated by high-level distributed interactions. We provide a novel representation of the autonomic system-a multidimensional, generative network-that renders its richness tractable within future models of its function in health and disease

Delineation between different components of chronic pain using dimension reduction - an ASL fMRI study in hand osteoarthritis

DK was supported by grants from GENIEUR COST action and the ‘Sint Annadal’ Foundation Maastricht. MAH and SW are supported by a Medical Research Council Experimental Medicine Challenge Grant award (MR/ N026969/1) and the NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Trust. The data collected for this study were part of an academic–industrial collaboration between King’s College London and the study sponsor, Pfizer Global Research and Development, UK. All data collection was performed by King’s College London scientists only

Voxel-based magnetic resonance imaging investigation of poor and preserved clinical insight in people with schizophrenia

AIM To define regional grey-matter abnormalities in schizophrenia patients with poor insight (Insight-), relative to patients with preserved clinical insight (Insight+), and healthy controls. METHODS Forty stable schizophrenia outpatients (20 Insight- and 20 Insight+) and 20 healthy controls underwent whole brain magnetic resonance imaging (MRI). Insight in all patients was assessed using the Birchwood Insight Scale (BIS; a self-report measure). The two patient groups were pre-selected to match on most clinical and demographic parameters but, by design, they had markedly distinct BIS scores. Voxel-based morphometry employed in SPM8 was used to examine group differences in grey matter volumes across the whole brain. RESULTS The three participant groups were comparable in age [F(2,57) = 0.34, P = 0.71] and the patient groups did not differ in age at illness onset [t(38) = 0.87, P = 0.39]. Insight- and Insight+ patient groups also did not differ in symptoms on the Positive and Negative Syndromes scale (PANSS): Positive symptoms [t(38) = 0.58, P = 0.57], negative symptoms [t(38) = 0.61, P = 0.55], general psychopathology [t(38) = 1.30, P = 0.20] and total PANSS scores [t(38) = 0.21, P = 0.84]. The two patient groups, as expected, varied significantly in the level of BIS-assessed insight [t(38) = 12.11, P 0.20) from each other. CONCLUSION Our findings demonstrate a clear association between poor clinical insight and smaller fronto-temporal, occipital and cerebellar grey matter volumes in stable long-term schizophrenia patients

Preliminary report: parasympathetic tone links to functional brain networks during the anticipation and experience of visceral pain

Medical Research Council project grant - Medical Research Council Grant Number - MGAB1A1