Arterial, Venous, and Cerebrospinal Fluid Flow and Pulsatility in Stroke-Related Cerebral Small Vessel Disease: A Longitudinal Analysis.

Background: Cerebral small vessel disease (SVD) causes up to 45% of dementias and 25% of ischemic strokes, but the understanding of vascular pathophysiology is limited. We aimed to investigate the contribution of pulsatility of intracranial arteries, veins, and cerebrospinal fluid (CSF) and cerebral blood flow to long-term imaging and clinical outcomes in SVD.

Methods: We prospectively recruited participants in Edinburgh/Lothian, Scotland, with lacunar or nonlacunar ischemic stroke (modified Rankin Scale score ≤2, as controls) and assessed medical and brain magnetic resonance imaging characteristics at baseline and 1 year (2018-2022).

Rationale and design of the brain magnetic resonance imaging protocol for FutureMS: a longitudinal multi-centre study of newly diagnosed patients with relapsing-remitting multiple sclerosis in Scotland.

Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease. MS prevalence varies geographically and is notably high in Scotland. Disease trajectory varies significantly between individuals and the causes for this are largely unclear.

Rationale and design of a longitudinal study of cerebral small vessel diseases, clinical and imaging outcomes in patients presenting with mild ischaemic stroke: Mild Stroke Study 3.

Background: Cerebral small vessel disease is a major cause of dementia and stroke, visible on brain magnetic resonance imaging. Recent data suggest that small vessel disease lesions may be dynamic, damage extends into normal-appearing brain and microvascular dysfunctions include abnormal blood-brain barrier leakage, vasoreactivity and pulsatility, but much remains unknown regarding underlying pathophysiology, symptoms, clinical features and risk factors of small vessel disease. The Mild Stroke Study 3 is a prospective observational cohort study to identify risk factors for and clinical implications of small vessel disease progression and regression among up to 300 adults with non-disabling stroke.

Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration.

Introduction: Many consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease.

Methods: Surveys, teleconferences, and an in-person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis.

Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing.

Background: Research involving brain imaging is important for understanding common brain diseases. Study endpoints can include features and measures derived from imaging modalities, providing a benchmark against which other phenotypical data can be assessed. In trials, imaging data provide objective evidence of beneficial and adverse outcomes.

Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review.

Introduction: Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear.

Methods: We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy aging from dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries.

Results: Of 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, using AD Neuroimaging Initiative data, support vector machines, and only T1-weighted sequences.

Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group.

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking.

Whole Brain Magnetic Resonance Image Atlases: A Systematic Review of Existing Atlases and Caveats for Use in Population Imaging.

Brain MRI atlases may be used to characterize brain structural changes across the life course. Atlases have important applications in research, e.g.

Computational meta-analysis of statistical parametric maps in major depression.

Objective: Several neuroimaging meta-analyses have summarized structural brain changes in major depression using coordinate-based methods. These methods might be biased toward brain regions where significant differences were found in the original studies. In this study, a novel voxel-based technique is implemented that estimates and meta-analyses between-group differences in grey matter from individual MRI studies, which are then applied to the study of major depression.

A structural and functional magnetic resonance imaging dataset of brain tumour patients.

We collected high resolution structural (T1, T2, DWI) and several functional (BOLD T2*) MRI data in 22 patients with different types of brain tumours. Functional imaging protocols included a motor task, a verb generation task, a word repetition task and resting state. Imaging data are complemented by demographics (age, sex, handedness, and pathology), behavioural results to motor and cognitive tests and direct cortical electrical stimulation data (pictures of stimulation sites with outcomes) performed during surgery.

A brain imaging repository of normal structural MRI across the life course: Brain Images of Normal Subjects (BRAINS).

The Brain Images of Normal Subjects (BRAINS) Imagebank (http://www.brainsimagebank.ac.

Grey matter networks in people at increased familial risk for schizophrenia.

Grey matter brain networks are disrupted in schizophrenia, but it is still unclear at which point during the development of the illness these disruptions arise and whether these can be associated with behavioural predictors of schizophrenia. We investigated if single-subject grey matter networks were disrupted in a sample of people at familial risk of schizophrenia. Single-subject grey matter networks were extracted from structural MRI scans of 144 high risk subjects, 32 recent-onset patients and 36 healthy controls.

Permutation and parametric tests for effect sizes in voxel-based morphometry of gray matter volume in brain structural MRI.

Permutation testing has been widely implemented in voxel-based morphometry (VBM) tools. However, this type of non-parametric inference has yet to be thoroughly compared with traditional parametric inference in VBM studies of brain structure. Here we compare both types of inference and investigate what influence the number of permutations in permutation testing has on results in an exemplar study of how gray matter proportion changes with age in a group of working age adults.

Use of brain MRI atlases to determine boundaries of age-related pathology: the importance of statistical method.

Introduction: Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient's brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g.

Variance in brain volume with advancing age: implications for defining the limits of normality.

Background: Statistical models of normal ageing brain tissue volumes may support earlier diagnosis of increasingly common, yet still fatal, neurodegenerative diseases. For example, the statistically defined distribution of normal ageing brain tissue volumes may be used as a reference to assess patient volumes. To date, such models were often derived from mean values which were assumed to represent the distributions and boundaries, i.

Cortical thickness in first-episode schizophrenia patients and individuals at high familial risk: a cross-sectional comparison.

Background: Schizophrenia is associated with cortical thickness reductions in the brain, but it is unclear whether these are present before illness onset, and to what extent they are driven by genetic factors.

Methods: In the Edinburgh High Risk Study, structural MRI scans of 150 young individuals at high familial risk for schizophrenia, 34 patients with first-episode schizophrenia and 36 matched controls were acquired, and clinical information was collected for the following 10 years for the high-risk and control group. During this time, 17 high-risk individuals developed schizophrenia, on average 2.

Grey matter differences in bipolar disorder: a meta-analysis of voxel-based morphometry studies.

Objective: Several neuroimaging studies have reported structural brain differences in bipolar disorder using automated methods. While these studies have several advantages over those using region of interest techniques, no study has yet estimated a summary effect size or tested for between-study heterogeneity. We sought to address this issue using meta-analytic techniques applied for the first time in bipolar disorder at the level of the individual voxel.

Do brain image databanks support understanding of normal ageing brain structure? A systematic review.

Objective: To document accessible magnetic resonance (MR) brain images, metadata and statistical results from normal older subjects that may be used to improve diagnoses of dementia.

Methods: We systematically reviewed published brain image databanks (print literature and Internet) concerned with normal ageing brain structure.

Results: From nine eligible databanks, there appeared to be 944 normal subjects aged ≥60 years.

Assessment of the impact of the scanner-related factors on brain morphometry analysis with Brainvisa.

Background: Brain morphometry is extensively used in cross-sectional studies. However, the difference in the estimated values of the morphometric measures between patients and healthy subjects may be small and hence overshadowed by the scanner-related variability, especially with multicentre and longitudinal studies. It is important therefore to investigate the variability and reliability of morphometric measurements between different scanners and different sessions of the same scanner.

Impact of cannabis use on thalamic volume in people at familial high risk of schizophrenia.

Background: No longitudinal study has yet examined the association between substance use and brain volume changes in a population at high risk of schizophrenia.

Aims: To examine the effects of cannabis on longitudinal thalamus and amygdala-hippocampal complex volumes within a population at high risk of schizophrenia.

Method: Magnetic resonance imaging scans were obtained from individuals at high genetic risk of schizophrenia at the point of entry to the Edinburgh High-Risk Study (EHRS) and approximately 2 years later.

The Neuro/PsyGRID calibration experiment: identifying sources of variance and bias in multicenter MRI studies.

Calibration experiments precede multicenter trials to identify potential sources of variance and bias. In support of future imaging studies of mental health disorders and their treatment, the Neuro/PsyGRID consortium commissioned a calibration experiment to acquire functional and structural MRI from twelve healthy volunteers attending five centers on two occasions. Measures were derived of task activation from a working memory paradigm, fractal scaling (Hurst exponent) from resting fMRI, and grey matter distributions from T(1) -weighted sequences.

Effects of the BDNF val66met polymorphism on prefrontal brain function in a population at high genetic risk of schizophrenia.

A single nucleotide polymorphism (val66met) in the brain derived neurotrophic factor (BDNF) gene has been shown to be a risk factor for a number of psychiatric disorders, including schizophrenia. This polymorphism has also been shown to have effects on prefrontal brain morphology and function. This study aims to clarify the effects of the val66met polymorphism on prefrontal brain function in a population at high genetic risk for schizophrenia.

Between- and within-scanner variability in the CaliBrain study n-back cognitive task.

Psychiatric neuroimaging techniques are likely to improve understanding of the brain in health and disease, but studies tend to be small, based in one imaging centre and of unclear generalisability. Multicentre studies have great appeal but face problems if functional magnetic resonance imaging (fMRI) data from different centres are to be combined. Fourteen healthy volunteers had two brain scans on different days at three scanners.