Neuroimaging Analysis Research Articles

VertexWiseR: a package for simplified vertex-wise analyses of whole-brain and hippocampal surfaces in R

Abstract Currently, whole-brain vertex-wise analyses on brain surfaces commonly require specially configured operating systems/environments to run and are largely inaccessible to R users. As such, these analyses are inconvenient to execute and inaccessible to many aspiring researchers. To address these limitations, we present VertexWiseR, a user-friendly R package, to run cortical and hippocampal surface vertex-wise analyses, in just about any computer, requiring minimal technical expertise and computational resources. The package allows cohort-wise anatomical surface data to be highly compressed into a single, compact, easy-to-share file. Users can then run a range of vertex-wise statistical analyses with that single file without requiring a special operating system/environment and direct access to the preprocessed file directories. This enables the user to easily take the analyses ‘offline’, which would be highly appropriate and conducive in classroom settings. This R package includes a conventional suite of tools for extracting, manipulating, analysing, and visualizing vertex-wise data, and is designed to be easy for beginners to use. Furthermore, it also contains novel or advanced functionalities such as hippocampal surface analyses, meta-analytic decoding, threshold-free cluster enhancement, and mixed effects models that would appeal to experienced researchers as well. In the current report, we showcase these functionalities in the analyses of two publicly accessible datasets. Overall, our R package opens up new frontiers for the R’s user base/community and makes such neuroimaging analyses accessible to the masses.

Histology-guided MRI segmentation of brainstem nuclei critical to consciousness.

While substantial progress has been made in mapping the connectivity of cortical networks responsible for conscious awareness, neuroimaging analysis of subcortical arousal networks that modulate arousal (i.e., wakefulness) has been limited by a lack of a robust segmentation procedures for brainstem arousal nuclei. Automated segmentation of brainstem arousal nuclei is an essential step toward elucidating the physiology of arousal in human consciousness and the pathophysiology of disorders of consciousness. We created a probabilistic atlas of brainstem arousal nuclei built on diffusion MRI scans of five ex vivo human brain specimens scanned at 750 μm isotropic resolution. Labels of arousal nuclei used to generate the probabilistic atlas were manually annotated with reference to nucleus-specific immunostaining in two of the five brain specimens. We then developed a Bayesian segmentation algorithm that utilizes the probabilistic atlas as a generative model and automatically identifies brainstem arousal nuclei in a resolution- and contrast-agnostic manner. The segmentation method displayed high accuracy in both healthy and lesioned in vivo T1 MRI scans and high test-retest reliability across both T1 and T2 MRI contrasts. Finally, we show that the segmentation algorithm can detect volumetric changes and differences in magnetic susceptibility within brainstem arousal nuclei in Alzheimer's disease and traumatic coma, respectively. We release the probabilistic atlas and Bayesian segmentation tool in FreeSurfer to advance the study of human consciousness and its disorders.

Multi-omics insights into the microbiota-gut-brain axis and cognitive improvement post-bariatric surgery

BackgroundAlthough numerous studies have shown that bariatric surgery results in sustained weight loss and modifications in gut microbiota composition and cognitive function, the exact underlying mechanisms are unclear. This study aimed to investigate the effects of bariatric surgery on cognitive function through the microbiota-gut-brain axis (MGBA).MethodsDemographic data, serum samples, fecal samples, cognitive assessment scales, and resting-state functional connectivity magnetic resonance imaging (rs-fMRI) scans were obtained from 39 obese patients before and after (6 months) laparoscopic sleeve gastrectomy (LSG). PCA analysis, OPLS-DA analysis, and permutation tests were used to conduct fecal 16 S microbiota profiling, serum metabolomics, and neuroimaging analyses, and a bariatric surgery-specific rs-fMRI brain functional connectivity network was constructed. Spearman correlation analysis and Co-inertia analysis were employed to correlate significant alterations in cognitive assessment scales and resting-state functional connectivity difference networks with differential serum metabolites and 16 S microbiota data to identify key gut microbiota and serum metabolic factors.ResultsLSG significantly reduced the weight of obese patients, with reductions of up to 28%. Furthermore, cognitive assessment scale measurements revealed that LSG enhanced cognitive functions, including memory (HVLT, p = 0.000) and executive function (SCWT, p = 0.008). Also, LSG significantly altered gut microbiota composition (p = 0.001), with increased microbial abundance and diversity (p < 0.05). Moreover, serum metabolite levels were significantly altered, revealing intergroup differences in 229 metabolites mapped to 72 metabolic pathways (p < 0.05, VIP > 1). Spearman correlation analysis among cognitive assessment scales, gut microbiota species, and serum metabolites revealed correlations with 68 gut microbiota species and 138 serum metabolites (p < 0.05). Furthermore, pairwise correlations were detected between gut microbiota and serum metabolites (p < 0.05). Functional neuroimaging analysis revealed that LSG increased functional connectivity in cognitive-related frontotemporal networks (FPN, p < 0.01). Additionally, normalization of the default mode network (DMN) and salience network (SN) connectivity was observed after LSG (p < 0.001). Further canonical correlation and correlation analysis suggested that the cognitive-related brain network changes induced by LSG were associated with key gut microbiota species (Akkermansia, Blautia, Collinsella, Phascolarctobacterium, and Ruminococcus, p < 0.05) and neuroactive metabolites (Glycine, L-Serine, DL-Dopa, SM (d18:1/24:1(15Z), p < 0.05).ConclusionThese findings indicate the pathophysiological role of the microbiota-gut-brain axis in enhancing cognitive function after bariatric surgery, and the study provides a basis for clinical dietary adjustments, probiotic supplementation, and guidance for bariatric surgery, but further research is still needed.Trial registrationChinese Clinical Trial Registry, ChiCTR2100049403. Registered 02 August 2021, https://www.chictr.org.cn/.

Humans actively reconfigure neural task states.

The ability to switch between different tasks is a core component of adaptive cognition, but a mechanistic understanding of this capacity has remained elusive. Longstanding questions over whether task switching requires active preparation remain hotly contested, in large part due to the difficulty of inferring preparatory dynamics from behavior or time-locked neuroimaging. We make progress on this debate by quantifying neural task representations using high-dimensional linear dynamical systems fit to human electroencephalographic recordings. We find that these dynamical systems have high predictive accuracy and reveal neural signatures of active preparation that are shared with task-optimized neural networks. These findings inform a classic debate about how we control our cognition, and offer a promising new paradigm for neuroimaging analysis.

Sleep deprivation-induced memory impairment: exploring potential interventions.

Sleep's crucial role in memory consolidation is well-established, with neuroimaging and sleep stage analysis revealing the intricate processes involved. Sleep deprivation significantly impairs memory performance and the ability to form new memories, highlighting the need for effective countermeasures. This article concludes that while sleep deprivation significantly impairs memory, the emerging insights into the gut-brain axis offer a promising frontier for developing novel interventions that can mitigate these effects. The review discusses various interventions, ranging from pharmaceutical compounds like donepezil, memantine, and tolcapone, to innovative physical therapy techniques such as transcranial magnetic stimulation (TMS), deep brain stimulation (DBS), and transcranial direct current stimulation (tDCS). Additionally, the emerging role of the gut-brain axis in sleep deprivation-induced memory impairment is examined, shedding light on the complex interplay between gut microbiota and cognitive functions. This comprehensive review explores the multifaceted relationship between sleep deprivation and memory impairment, delving into the neurobiological mechanisms underlying these processes and examining potential interventions.

Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors.

In regression-based analyses of group-level neuroimage data, researchers typically fit a series of marginal general linear models to image outcomes at each spatially referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, the resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses; however, the number of locations in a typical neuroimage can preclude standard computing methods in this setting. Here, we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process priors. When combined with a simple non-stationary model for the error process, our prior hierarchy can lead to more data-adaptive smoothing than standard methods. We achieve computational tractability through a Vecchia-type approximation of our prior that retains full spatial rank and can be constructed for a wide class of spatial correlation functions. We outline several ways to work with our model in practice and compare performance against standard vertex-wise analyses and several alternatives. Finally, we illustrate our methods in an analysis of cortical surface functional magnetic resonance imaging task contrast data from a large cohort of children enrolled in the adolescent brain cognitive development study.

Reproducible brain PET data analysis: easier said than done.

While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, this article argues that brain positron emission tomography (PET) research stands on even more fragile ground, lagging behind efforts to address MRI reproducibility. We begin by examining the current landscape of factors that contribute to reproducible neuroimaging data analysis, including scientific standards, analytic plan pre-registration, data and code sharing, containerized workflows, and standardized processing pipelines. We then focus on disparities in the current status of these factors between brain MRI and brain PET. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we present a case study that illustrates the many challenges faced by one laboratory that attempted to reproduce a community-standard brain PET processing pipeline. We identified key areas in which the brain PET community could enhance reproducibility, including stricter reporting policies among PET dedicated journals, data repositories, containerized analysis tools, and standardized processing pipelines. Other solutions such as mandatory pre-registration, data sharing, code availability as a condition of grant funding, and online forums and standardized reporting templates, are also discussed. Bolstering these reproducibility factors within the brain PET research community has the potential to unlock the full potential of brain PET research, propelling it toward a higher-impact future.

Alzheimer's Disease and Its a Diagnosis, Symptoms, Pathophysiology, and Treatment

Alzheimer’s disease (AD) is the most prevalent form of dementia, predominantly affecting individuals over 65 years old, though a rare form can manifest in younger people. Globally, AD accounts for approximately 75% of the more than 35 million dementia cases, with projections suggesting the number could rise to 115 million by 2050. The precise etiology of Alzheimer’s remains largely unknown. Historically, dementia has been recognized since ancient times, with significant milestones in understanding and terminology occurring from the 17th century onwards. Alzheimer’s disease is identified by symptoms such as confusion, personality changes, language difficulties, mood swings, and short-term memory disturbance. Pathophysiologically, AD is characterized by amyloid plaques and neurofibrillary tangles (NFTs) which cause brain damage. Diagnosis involves clinical evaluation, neuroimaging, neuropathological findings, and cerebrospinal fluid analysis. Risk factors include genetic predisposition, vascular issues, and psychosocial elements. Current treatments focus on acetylcholinesterase inhibitors to manage symptoms, particularly in the mild to moderate stages of the disease.

The German research consortium for the study of bipolar disorder (BipoLife): a quality assurance protocol for MR neuroimaging data

BackgroundThe German multicenter research consortium BipoLife aims to investigate the mechanisms underlying bipolar disorders. It focuses in particular on people at high risk of developing the disorder and young patients in the early stages of the disease. Functional and structural magnetic resonance imaging (MRI) data was collected in all participating centers. The collection of neuroimaging data in a longitudinal, multicenter study requires the implementation of a comprehensive quality assurance (QA) protocol. Here, we outline this protocol and illustrate its application within the BipoLife consortium.MethodsThe QA protocol consisted of (1) a training of participating research staff, (2) regular phantom measurements to evaluate the MR scanner performance and its temporal stability across the course of the study, and (3) the assessment of the quality of human MRI data by evaluating a variety of image metrics (e.g., signal-to-noise ratio, ghosting level). In this article, we will provide an overview on these QA procedures and show exemplarily the influence of its application on the results of standard neuroimaging analysis pipelines.DiscussionThe QA protocol helped to characterize the various MR scanners, to record their performance over the course of the study and to detect possible malfunctions at an early stage. It also assessed the quality of the human MRI data systematically to characterize its influence on various analyses. Furthermore, by setting up and publishing this protocol, we define standards that must be considered when analyzing data from the BipoLife consortium. It further promotes a systematic evaluation of data quality and a definition of subject inclusion criteria. In the long term, it will help to increase the chance of achieving clinically relevant results.

Unveiling Functional Biomarkers in Schizophrenia: Insights from Region of Interest Analysis Using Machine Learning.

Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. There were many attempts to understand these basic mechanisms behind the disorder, yet we know very little. By employing machine learning techniques with age-matched samples from the auditory oddball task using multi-site functional magnetic resonance imaging (fMRI) data, this study aims to address these challenges. The study employed a three-stage model to gain a better understanding of the neurobiology underlying schizophrenia and techniques that could be applied for diagnosis. At first, we constructed four-level hierarchical sets from each fMRI volume of 34 schizophrenia patients (SZ) and healthy controls (HC) individually in terms of hemisphere, gyrus, lobes, and Brodmann areas. Second, we employed statistical methods, namely, t-tests and Pearson's correlation, to assess the group differences in cortical activation. Finally, we assessed the predictive power of the brain regions for machine learning algorithms using K-nearest Neighbor (KNN), Naive Bayes, Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), and Extreme Learning Machine (ELM). Our investigation depicts promising results, obtaining an accuracy of up to 84% when applying Pearson's correlation-selected features at lobes and Brodmann region level (81% for Gyrus), as well as Hemispheres involving different stages. Thus, the results of our study were consistent with previous studies that have revealed some functional abnormalities in several brain regions. We also discovered the involvement of other brain regions which were never sufficiently studied in previous literature, such as the posterior lobe (posterior cerebellum), Pyramis, and Brodmann Area 34. We present a unique and comprehensive approach to investigating the neurological basis of schizophrenia in this study. By bridging the gap between neuroimaging and computable analysis, we aim to improve diagnostic accuracy in patients with schizophrenia and identify potential prognostic markers for disease progression.

Neuronal Migration Disorders in Children; A Case Series highlighting importance of Neuroimaging in diagnosing Epilepsy

Background: Neuronal migration disorders comprise a heterogeneous group of neurological conditions caused by abnormal neuronal positioning during brain development. We aimed to study the clinical profile, neuroimaging features, genetics, and treatment of neuronal migration disorders. Methods: This was a retrospective case series of 15 pediatric patients diagnosed with neuronal migration disorders at a tertiary care hospital over a 5 year period. Detailed clinical evaluation, neuroimaging, EEG, and genetic analysis were performed. Results: Global developmental delay and epilepsy were the most common presenting features. Neuroimaging revealed spectrum of cortical dysgenesis including pachygyria, polymicrogyria, lissencephaly, heterotopia, and schizencephaly, predominantly involving fronto-parietal regions. MRI coupled with clinical findings enabled definitive diagnosis. Genetic analysis identified mutations in PAFAH1B1, ADGRG1, DHDDS, TMTC3, and other genes. Anti-seizure medications like valproate, levetiracetam, and clobazam were the mainstay of therapy. Non-pharmacological interventions including physiotherapy, occupational therapy, and speech therapy were employed. Conclusion: This study demonstrates the diverse clinical, genetic, and radiological spectrum of neuronal migration disorders. Comprehensive evaluation along with neuroimaging and genetic analysis enables accurate diagnosis and guides management. A multimodal approach is required focusing on seizures, neurodevelopmental disabilities, and improving quality of life.

Enabling Advanced Multi-Modal Neuroimaging Analysis within a Trusted Research Environment

IntroductionGlobally, 55 million individuals have dementia, with an increasing annual incident of 10 million. Enabling the development of new multi-modal models can improve the current diagnostic pathways and potentially contribute to early diagnosis and treatment of dementia. Here, we report how multi-modal resources is achieved within the successful Trusted Research Environment (TRE). ObjectivesWe aimed to identify the challenges of the storage, distribution and analysis of neuroimaging data and how we could implement a comprehensive infrastructure to deal with these. The problems we specifically aimed to address were how to: anonymise scans, store large amounts of data, standardise datasets to a common format, extract metadata, provision the data, and allow for analysis. MethodsWe document various stages and capacities required for multi-modal neuroimaging analysis for dementia and conclude that achieving research ready assets to enable neuroimaging analysis for dementia from existing resources requires an engineered process to facilitate multiple aspects of curation, provisioning and large-scale analysis. ResultsWe developed an ingest pipeline for neuroimaging data to meet the requirements set out in the objectives. This involved standardising all datasets to the Brain Imaging Data Structure, defacing scans and anonymising data, using MinIO for data storage and extracting metadata from header information for data discovery and provisioning. ConclusionThe neuroimaging ingest pipeline developed has allowed for the distribution of imaging datasets within DPUK which has facilitated multi-modal research on anonymised and standardised data and enabled linkage with phenotypic and genomic data.

Addressing inconsistency in functional neuroimaging: A replicable data-driven multi-scale functional atlas for canonical brain networks.

The advent of various neuroimaging methodologies has greatly aided in the conceptualization of large-scale brain networks in the field of cognitive neuroscience. However, there is inconsistency across studies in both nomenclature and the functional entities being described. There is a need for a unifying framework which standardizes terminology across studies while also bringing analyses and results into the same reference space. Here we present a functional whole-brain atlas of canonical brain networks derived from more than 100k resting-state fMRI datasets. These data-driven networks are highly replicable across datasets as well as multiple spatial scales. We have organized, labeled, and described them with terms familiar to the fields of cognitive and affective neuroscience in order to optimize their utility in future neuroimaging analyses and enhance the accessibility of new findings. The benefits of this atlas are not limited to future template-based or reference-guided analyses, but also extend to other data-driven neuroimaging approaches across modalities, such as those using blind independent component analysis (ICA). Future studies utilizing this atlas will contribute to greater harmonization and standardization in functional neuroimaging research.

A Randomization-Based, Model-Free Approach to Functional Neuroimaging: A Proof of Concept.

Functional neuroimaging analysis takes noisy multidimensional measurements as input and produces statistical inferences regarding the functional properties of brain regions as output. Such inferences are most commonly model-based, in that they assume a model of how neural activity translates to the measured signal (blood oxygenation level-dependent signal in the case of functional MRI). The use of models increases statistical sensitivity and makes it possible to ask fine-grained theoretical questions. However, this comes at the cost of making theoretical assumptions about the underlying data-generating process. An advantage of model-free approaches is that they can be used in cases where model assumptions are known not to hold. To this end, we introduce a randomization-based, model-free approach to functional neuroimaging. TWISTER randomization makes it possible to infer functional selectivity from correlations between experimental runs. We provide a proof of concept in the form of a visuomotor mapping experiment and discuss the possible strengths and limitations of this new approach in light of our empirical results.

Abstract P343: Cerebral injury and cognitive performances are correlated with immunoinflammatory markers in hypertensive patients

Immunity and inflammation play a pivotal role in hypertension onset and cardiovascular organ damage. This role has been thoroughly characterized for classical hypertension targets as the kidneys, the heart and the vasculature. Many studies have characterized this in the experimental context, however immunoinflammatory challenges links with brain injury in the clinical context of hypertension are still lacking. In this study we will characterize how immunoinflammatory mediators can be predictors of cerebral injury characterized by advanced neuroimaging in hypertensive patients. Hypertensive patients underwent cerebral MRI and by advanced neuroimaging we characterized the microstructural injury by DTI fiber tracking. We administered the Montreal Cognitive Assessment to evaluate cognitive function, measured circulating C-reactive protein (hs-CRP) and counted circulating white blood cells (WBC). We found a large cluster of white matter tracts whose integrity parameters were associated with immunoinflammatory biomarkers (Figure A). In particular, a cluster of tracts of the limbic system and encompassing the corpus callosum show a correlation between their loss of integrity and hs-CRP levels. Another cluster of associative tracts spanning the temporal lobe show association between their loss of integrity and the WBC. Finally, the Frontal Aslant and the Uncinate Fasciculus show association with both immunoinflammatory markers (Figure B). In this study we performed advanced neuroimaging analyses to characterize the interplay between immunoinflammatory risk and cerebral alterations in hypertensives. Furthermore, our previous work identified that white matter damage in the Forceps Minor and Superior Longitudinal Fasciculus was a typical trait of hypertensive patients and associated to loss of cognitive functions. Our data further show the inflammatory risk association with worse cognitive functions, suggesting a clinical relevance in terms of damage evidenced by advanced MRI in hypertensive patients.

Abstract 22: Evaluation of the effects of CD8 knock-out on cognitive decline in the TAC-induced hypertension mouse model

Hypertension is one of the main risk factors for cognitive impairment and dementia. We and others demonstrated the role of immunoinflammatory challenges in the establishment of cerebral injury following hypertension, highlighting a major role of inflammatory cytokines as interferon-gamma (IFN-γ) and finding increased parenchymal infiltration of CD8+ T cells. To establish the mechanistic role of these cells in cognitive impairment we subjected mice lacking CD8 cells (CD8 KO) and matched controls to aortic coarctation (TAC) and performed advanced neuroimaging analyses with a dedicated 7T small animal MRI, evaluating perfusion, brain volumes, and white matter microstructural integrity. Cognitive performance was evaluated at 4 weeks using the Morris Water Maze (MWM) test. Brain perfusion analyses shown protection from cerebral hypoperfusion in TAC CD8 KO mice compared to TAC WT mice (TAC WT vs TAC KO: 108 vs 148 ml/100g/min, p=0.0298) (Figure A). CD8 KO show preserved microstructural structure at DTI MRI, a typical trait of cerebral injury in clinical and experimental hypertension (Fimbria Fractional Anisotropy, TAC WT vs TAC KO: 0.45 vs 0.54, p=0.0210) , and show no a preserved cognition at MWM (Figure B), with a preserved learning curve at acquisition trial and a preserved memory retention capability evidenced at the probe trial (TAC WT vs TAC KO: 16,4 vs 32.2 seconds spent in platform quadrant, p=0.0014). The rescue from cerebral injury evidenced at MRI and the absence of cognitive impairment in CD8 KO mice demonstrate the pivotal role of CD8+ lymphocytes in the pathogenesis of hypertension-induced cognitive impairment.

Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder

Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information. Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study investigates whether multi-forms of magnetic resonance imaging (MRI) contrast can be used individually and in combination to produce a categorical classification of young individuals with ASD. MRI data were accessed from the Autism Brain Imaging Data Exchange (ABIDE). Young participants (ages 2–30) were selected, and two group cohorts consisted of 702 participants: 351 ASD and 351 controls. Image-based classification was performed using one-channel and two-channel inputs to 3D-DenseNet deep learning networks. The models were trained and tested using tenfold cross-validation. Two-channel models were twinned with combinations of structural MRI (sMRI) maps and amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) maps from resting-state functional MRI (rs-fMRI). All models produced classification accuracy that exceeded 65.1%. The two-channel ALFF-sMRI model achieved the highest mean accuracy of 76.9% ± 2.34. The one-channel ALFF-based model alone had mean accuracy of 72% ± 3.1. This study leveraged the ABIDE dataset to produce ASD classification results that are comparable and/or exceed literature values. The deep learning approach was conducive to diverse neuroimaging inputs. Findings reveal that the ALFF-sMRI two-channel model outperformed all others.

Neuroimaging of autobiographical memory in dementia with Lewy bodies: a story of insula.

Although deficits in learning and retrieving new information are well characterized in dementia with Lewy bodies, autobiographical memory has never been explored in this disease. Yet, autobiographical memory impairments are a pervasive feature of dementia, well characterized in other neurodegenerative diseases. Moreover, autobiographical memory corresponds to an extension over time of the self, which we hypothesize is altered in dementia with Lewy bodies and impairment of which could be linked to the insular atrophy occurring from an early stage of the disease. In this study, we sought to characterize autobiographical memory impairments and explore their neural correlates in dementia with Lewy bodies, on the assumption that insular damage could impact the self, including its most elaborate components, such as autobiographical memory. Twenty patients with prodromal to mild dementia with Lewy bodies were selected to participate in this exploratory study along with 20 healthy control subjects. The Autobiographical Interview was used to assess autobiographical memory. Performances were compared between patients and control subjects, and an analysis across life periods and recall conditions was performed. 3D magnetic resonance images were acquired for all participants, and correlational analyses were performed in the patient group using voxel-based morphometry. The behavioural results of the Autobiographical Interview showed that autobiographical memory performances were significantly impaired in dementia with Lewy body patients compared to control subjects in a temporally ungraded manner, for both the free recall and the specific probe conditions (P < 0.0001), though with greater improvement after probing in the patient group. Furthermore, autobiographical memory impairments were correlated with grey matter volume within right insular cortex, temporoparietal junction, precuneus, putamen, left temporal cortex, bilateral parahippocampus and cerebellum, using a threshold of P = 0.005 uncorrected. The behavioural results confirm the existence of temporally ungraded autobiographical memory impairments in dementia with Lewy bodies, from the early stage of the disease. As we expected, neuroimaging analysis revealed a role for the insula and the precuneus in autobiographical memory retrieval, two regions associated with elementary aspects of the self, among other brain regions classically associated with autobiographical memory, such as medial temporal lobe and temporoparietal junction. Our findings provide important insights regarding the involvement of the insula in the self and suggest that insular damage could lead to a global collapse of the self, including its more elaborated components, such as autobiographical memory.

A child with recurrent headache, fever and diffuse meningeal enhancement on MRI.

Juvenile neurolupus presents primarily with neuropsychiatric manifestations which may also be the initial presentation. Such primary neuropsychiatric SLE (NPSLE) events are a consequence either of microvasculopathy and thrombosis, or of autoantibodies and inflammatory mediators. Diagnosis of NPSLE requires the exclusion of other causes, and clinical assessment directs the selection of appropriate investigations. These investigations include measurement of autoantibodies, analysis of cerebrospinal fluid, electrophysiological studies, neuropsychological assessment and neuroimaging to evaluate brain structure and function. In our patient, the disease presented with chronic headache initially diagnosed as migraine, followed by fever and paraparesis. Fundoscopy showed retinal haemorrhages. Investigations revealed anaemia, neutrophilic leucocytosis, thrombocytopenia and raised inflammatory markers (ESR 119mm/h CRP 58mg/L) and high globulin. MRI brain showed diffuse meningeal enhancement resembling meningitis but CSF analysis was normal. ANA and dsDNA were positive with low C3, C4. All diffuse meningeal enhancements may not be meningitis and one needs to corroborate all the clinical, biochemical and imaging analyses to come to a diagnosis.

An integration of neuroimaging and serum proteomics analysis suggests immune and inflammation are associated with white matter microstructure changes in cerebral small vessel disease with depressive symptoms

IntroductionDepressive symptoms are a common concomitant of cerebral small vessel disease (CSVD), of which pathogenesis requires more study. White matter microstructural abnormalities and proteomic alternation have been widely reported regarding depression in the elderly with CSVD. Exploring the relationship between cerebral white matter microstructural alterations and serum proteins may complete the explanation of molecular mechanisms for the findings from neuroimaging research of CSVD combined with depressive symptoms. MethodsAn untargeted proteomics approach based on mass spectrometry was used to obtain serum proteomic profiles, which were clustered into co-expression protein modules. White matter microstructural integrity was measured using the FMRIB Software Library (FSL) and MATLAB to analyze diffusion tensor imaging (DTI) data and calculate the differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) for 50 regions of interest (ROI). Integrating the proteome with the DTI results, weighted gene co-expression analysis (WGCNA) was used to identify protein modules related to white matter microstructural alterations, and the proteins of the corresponding modules were analyzed for functional enrichment through bioinformatics techniques. ResultsDTI measurements were analCerebral small vessel disease (CSVD); Depression; Diffusion tensor imaging (DTI); Proteomics; Inflammationyzed between individuals with CSVD and depressive symptoms (CSVD+D) (n = 24) and those without depressive symptoms (CSVD-D) (n = 35). Results showed an overall increase in MD, AD, and RD within the left hemisphere of the CSVD+D group, suggesting widespread loss of white matter integrity and axonal demyelination, including left superior longitudinal fasciculus (SLF), left posterior corona radiata (PCR) and right external capsule (EC). We identified two protein modules associated with DTI diffusivity, and functional enrichment analyses revealed that complement and coagulation cascades and immune responses participate in the alternation of white matter microstructure in the CSVD+D group. ConclusionThe results suggested immune- and inflammation-related mechanism was associated with white matter microstructure changes in CSVD with depressive symptoms