In vivo cortical diffusion imaging relates to Alzheimer's disease neuropathology.

BACKGROUND: There has been increasing interest in cortical microstructure as a complementary and earlier measure of neurodegeneration than macrostructural atrophy, but few papers have related cortical diffusion imaging to post-mortem neuropathology. This study aimed to characterise the associations between the main Alzheimer's disease (AD) neuropathological hallmarks and multiple cortical microstructural measures from in vivo diffusion MRI. Comorbidities and co-pathologies were also investigated. METHODS: Forty-three autopsy cases (8 cognitively normal, 9 mild cognitive impairment, 26 AD) from the National Alzheimer's Coordinating Center and Alzheimer's Disease Neuroimaging Initiative databases were included. Structural and diffusion MRI scans were analysed to calculate cortical minicolumn-related measures (AngleR, PerpPD+, and ParlPD) and mean diffusivity (MD). Neuropathological hallmarks comprised Thal phase, Braak stage, neuritic plaques, and combined AD neuropathological changes (ADNC-the "ABC score" from NIA-AA recommendations). Regarding comorbidities, relationships between cortical microstructure and severity of white matter rarefaction (WMr), cerebral amyloid angiopathy (CAA), atherosclerosis of the circle of Willis (ACW), and locus coeruleus hypopigmentation (LCh) were investigated. Finally, the effect of coexistent pathologies-Lewy body disease and TAR DNA-binding protein 43 (TDP-43)-on cortical microstructure was assessed. RESULTS: Cortical diffusivity measures were significantly associated with Thal phase, Braak stage, ADNC, and LCh. Thal phase was associated with AngleR in temporal areas, while Braak stage was associated with PerpPD+ in a wide cortical pattern, involving mainly temporal and limbic areas. A similar association was found between ADNC (ABC score) and PerpPD+. LCh was associated with PerpPD+, ParlPD, and MD. Co-existent neuropathologies of Lewy body disease and TDP-43 exhibited significantly reduced AngleR and MD compared to ADNC cases without co-pathology. CONCLUSIONS: Cortical microstructural diffusion MRI is sensitive to AD neuropathology. The associations with the LCh suggest that cortical diffusion measures may indirectly reflect the severity of locus coeruleus neuron loss, perhaps mediated by the severity of microglial activation and tau spreading across the brain. Recognizing the impact of co-pathologies is important for diagnostic and therapeutic decision-making. Microstructural markers of neurodegeneration, sensitive to the range of histopathological features of amyloid, tau, and monoamine pathology, offer a more complete picture of cortical changes across AD than conventional structural atrophy.

Pancreas MRI Segmentation Into Head, Body, and Tail Enables Regional Quantitative Analysis of Heterogeneous Disease.

BACKGROUND: Quantitative imaging studies of the pancreas have often targeted the three main anatomical segments, head, body, and tail, using manual region of interest strategies to assess geographic heterogeneity. Existing automated analyses have implemented whole-organ segmentation, providing overall quantification but failing to address spatial heterogeneity. PURPOSE: To develop and validate an automated method for pancreas segmentation into head, body, and tail subregions in abdominal MRI. STUDY TYPE: Retrospective. SUBJECTS: One hundred and fifty nominally healthy subjects from UK Biobank (100 subjects for method development and 50 subjects for validation). A separate 390 UK Biobank triples of subjects including type 2 diabetes mellitus (T2DM) subjects and matched nondiabetics. FIELD STRENGTH/SEQUENCE: A 1.5 T, three-dimensional two-point Dixon sequence (for segmentation and volume assessment) and a two-dimensional axial multiecho gradient-recalled echo sequence. ASSESSMENT: Pancreas segments were annotated by four raters on the validation cohort. Intrarater agreement and interrater agreement were reported using Dice overlap (Dice similarity coefficient [DSC]). A segmentation method based on template registration was developed and evaluated against annotations. Results on regional pancreatic fat assessment are also presented, by intersecting the three-dimensional parts segmentation with one available proton density fat fraction (PDFF) image. STATISTICAL TEST: Wilcoxon signed rank test and Mann-Whitney U-test for comparisons. DSC and volume differences for evaluation. A P value < 0.05 was considered statistically significant. RESULTS: Good intrarater (DSC mean, head: 0.982, body: 0.940, tail: 0.961) agreement and interrater (DSC mean, head: 0.968, body: 0.905, tail: 0.943) agreement were observed. No differences (DSC, head: P = 0.4358, body: P = 0.0992, tail: P = 0.1080) were observed between the manual annotations and our method's segmentations (DSC mean, head: 0.965, body: 0.893, tail: 0.934). Pancreatic body PDFF was different between T2DM and nondiabetics matched by body mass index. DATA CONCLUSION: The developed segmentation's performance was no different from manual annotations. Application on type 2 diabetes subjects showed potential for assessing pancreatic disease heterogeneity. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.

Evaluation of quantitative MRCP (MRCP+) for risk stratification of primary sclerosing cholangitis: comparison with morphological MRCP, MR elastography, and biochemical risk scores.

OBJECTIVES: To study the association of MRCP+ parameters with biochemical scoring systems and MR elastography (MRE) in primary sclerosing cholangitis (PSC). To evaluate the incremental value of combining MRCP+ with morphological scores in associating with biochemical scores. METHODS AND MATERIALS: MRI images, liver stiffness measurements by MRE, and biochemical testing of 65 patients with PSC that were retrospectively enrolled between January 2014 and December 2015 were obtained. MRCP+ was used to post-process MRCP images to obtain quantitative measurements of the bile ducts and biliary tree. Linear regression analysis was used to test the associations. Bootstrapping was used as a validation method. RESULTS: The total number of segmental strictures had the strongest association with Mayo Risk Score (R2 = 0.14), minimum stricture diameter had the highest association with Amsterdam Oxford Prognostic Index (R2 = 0.12), and the percentage of duct nodes with width 0-3 mm had the strongest association with PSC Risk Estimate Tool (R2 = 0.09). The presence of Ducts with medians > 9 mm had the highest association with MRE (R2= 0.21). The strength of association of MRCP+ to Mayo Risk Score was similar to ANALI2 and weaker than MRE (R2 = 0.23, 0.24, 0.38 respectively). MRCP+ enhanced the association of ANALI 2 and MRE with the Mayo Risk Score. CONCLUSIONS: MRCP+ demonstrated a significant association with biochemical scores and MRE. The association of MRCP+ with the biochemical scores was generally comparable to ANALI scores. MRCP+ enhanced the association of ANALI2 and MRE with the Mayo Risk Score. KEY POINTS: • MRCP+ has the potential to act as a risk stratfier in PSC. • MRE outperformed MRCP+ for risk stratifcation. • Combination of MRCP+ with MRE and ANALI scores improved overall performace as risk stratifiers.

A Quantitative Magnetic Resonance Cholangiopancreatography Metric of Intrahepatic Biliary Dilatation Severity Detects High-Risk Primary Sclerosing Cholangitis.

Magnetic resonance imaging with magnetic resonance cholangiopancreatography (MRI-MRCP) in primary sclerosing cholangitis (PSC) is currently based on qualitative assessment and has high interobserver variability. We investigated the utility and performance of quantitative metrics derived from a three-dimensional biliary analysis tool in adult patients with PSC. MRI-MRCP, blood-based biomarkers, and FibroScan were prospectively performed in 80 participants with large-duct PSC and 20 healthy participants. Quantitative analysis was performed using MRCP+ (Perspectum Ltd., United Kingdom), and qualitative reads were performed by radiologists. Inter-reader agreements were compared. Patients were classified into high risk or low risk for disease progression, using Mayo risk score (MRS), Amsterdam-Oxford model (AOM), upper limit of normal (ULN) alkaline phosphatase (ALP), disease distribution, and presence of dominant stricture. Performance of noninvasive tools was assessed using binomial logistic regressions and receiver operating characteristic curve analyses. Quantitative biliary metrics performed well to distinguish abnormal from normal bile ducts (P < 0.0001). Interobserver agreements for MRCP+ dilatation metrics (intraclass correlation coefficient, 0.90-0.96) were superior to modified Amsterdam intrahepatic stricture severity score (κ = 0.74) and Anali score (κ = 0.38). MRCP+ intrahepatic dilatation severity showed excellent performance to classify patients into high-risk and low-risk groups, using predictors of disease severity as the reference (MRS, P < 0.0001; AOM, P = 0.0017; 2.2 × ULN ALP, P = 0.0007; 1.5 × ULN ALP, P = 0.0225; extrahepatic disease, P = 0.0331; dominant stricture, P = 0.0019). MRCP+ intrahepatic dilatation severity was an independent predictor of MRS >0 (odds ratio, 31.3; P = 0.035) in the multivariate analysis. Conclusion: Intrahepatic biliary dilatation severity calculated using MRCP+ is elevated in patients with high-risk PSC and may be used as an adjunct for risk stratification in PSC. This exploratory study has provided the groundwork for examining the utility of novel quantitative biliary metrics in multicenter studies.

Intracortical diffusion tensor imaging signature of microstructural changes in frontotemporal lobar degeneration.

BACKGROUND: Frontotemporal lobar degeneration (FTLD) is a neuropathological construct with multiple clinical presentations, including the behavioural variant of frontotemporal dementia (bvFTD), primary progressive aphasia-both non-fluent variant (nfvPPA) and semantic variant (svPPA)-progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), characterised by the deposition of abnormal tau protein in the brain. A major challenge for treating FTLD is early diagnosis and accurate discrimination among different syndromes. The main goal here was to investigate the cortical architecture of FTLD syndromes using cortical diffusion tensor imaging (DTI) analysis and to test its power to discriminate between different clinical presentations. METHODS: A total of 271 individuals were included in the study: 87 healthy subjects (HS), 31 semantic variant primary progressive aphasia (svPPA), 37 behavioural variant (bvFTD), 30 non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), 47 PSP Richardson's syndrome (PSP-RS) and 39 CBS cases. 3T MRI T1-weighted images and DTI scans were analysed to extract three cortical DTI derived measures (AngleR, PerpPD and ParlPD) and mean diffusivity (MD), as well as standard volumetric measurements. Whole brain and regional data were extracted. Linear discriminant analysis was used to assess the group discrimination capability of volumetric and DTI measures to differentiate the FTLD syndromes. In addition, in order to further investigate differential diagnosis in CBS and PSP-RS, a subgroup of subjects with autopsy confirmation in the training cohort was used to select features which were then tested in the test cohort. Three different challenges were explored: a binary classification (controls vs all patients), a multiclass classification (HS vs bvFTD vs svPPA vs nfvPPA vs CBS vs PSP-RS) and an additional binary classification to differentiate CBS and PSP-RS using features selected in an autopsy confirmed subcohort. RESULTS: Linear discriminant analysis revealed that PerpPD was the best feature to distinguish between controls and all patients (ACC 86%). PerpPD regional values were able to classify correctly the different FTLD syndromes with an accuracy of 85.6%. The PerpPD and volumetric values selected to differentiate CBS and PSP-RS patients showed a classification accuracy of 85.2%. CONCLUSIONS: (I) PerpPD achieved the highest classification power for differentiating healthy controls and FTLD syndromes and FTLD syndromes among themselves. (II) PerpPD regional values could provide an additional marker to differentiate FTD, PSP-RS and CBS.

Quantitative MRCP Imaging: Accuracy, Repeatability, Reproducibility, and Cohort-Derived Normative Ranges.

BACKGROUND: Magnetic resonance cholangiopancreatography (MRCP) is an important tool for noninvasive imaging of biliary disease, however, its assessment is currently subjective, resulting in the need for objective biomarkers. PURPOSE: To investigate the accuracy, scan/rescan repeatability, and cross-scanner reproducibility of a novel quantitative MRCP tool on phantoms and in vivo. Additionally, to report normative ranges derived from the healthy cohort for duct measurements and tree-level summary metrics. STUDY TYPE: Prospective. PHANTOMS/SUBJECTS: Phantoms: two bespoke designs, one with varying tube-width, curvature, and orientation, and one exhibiting a complex structure based on a real biliary tree. Subjects Twenty healthy volunteers, 10 patients with biliary disease, and 10 with nonbiliary liver disease. SEQUENCE/FIELD STRENGTH: MRCP data were acquired using heavily T2 -weighted 3D multishot fast/turbo spin echo acquisitions at 1.5T and 3T. ASSESSMENT: Digital instances of the phantoms were synthesized with varying resolution and signal-to-noise ratio. Physical 3D-printed phantoms were scanned across six scanners (two field strengths for each of three manufacturers). Human subjects were imaged on four scanners (two fieldstrengths for each of two manufacturers). STATISTICAL TESTS: Bland-Altman analysis and repeatability coefficient (RC). RESULTS: Accuracy of the diameter measurement approximated the scanning resolution, with 95% limits of agreement (LoA) from -1.1 to 1.0 mm. Excellent phantom repeatability was observed, with LoA from -0.4 to 0.4 mm. Good reproducibility was observed across the six scanners for both phantoms, with a range of LoA from -1.1 to 0.5 mm. Inter- and intraobserver agreement was high. Quantitative MRCP detected strictures and dilatations in the phantom with 76.6% and 85.9% sensitivity and 100% specificity in both. Patients and healthy volunteers exhibited significant differences in metrics including common bile duct (CBD) maximum diameter (7.6 mm vs. 5.2 mm P = 0.002), and overall biliary tree volume 12.36 mL vs. 4.61 mL, P = 0.0026). DATA CONCLUSION: The results indicate that quantitative MRCP provides accurate, repeatable, and reproducible measurements capable of objectively assessing cholangiopathic change. Evidence Level: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:807-820.

Volitional modulation of higher-order visual cortex alters human perception.

Can we change our perception by controlling our brain activation? Awareness during binocular rivalry is shaped by the alternating perception of different stimuli presented separately to each monocular view. We tested the possibility of causally influencing the likelihood of a stimulus entering awareness. To do this, participants were trained with neurofeedback, using realtime functional magnetic resonance imaging (rt-fMRI), to differentially modulate activation in stimulus-selective visual cortex representing each of the monocular images. Neurofeedback training led to altered bistable perception associated with activity changes in the trained regions. The degree to which training influenced perception predicted changes in grey and white matter volumes of these regions. Short-term intensive neurofeedback training therefore sculpted the dynamics of visual awareness, with associated plasticity in the human brain.

Patterns of progressive atrophy vary with age in Alzheimer's disease patients.

Age is not only the greatest risk factor for Alzheimer's disease (AD) but also a key modifier of disease presentation and progression. Here, we investigate how longitudinal atrophy patterns vary with age in mild cognitive impairment (MCI) and AD. Data comprised serial longitudinal 1.5-T magnetic resonance imaging scans from 153 AD, 339 MCI, and 191 control subjects. Voxel-wise maps of longitudinal volume change were obtained and aligned across subjects. Local volume change was then modeled in terms of diagnostic group and an interaction between group and age, adjusted for total intracranial volume, white-matter hyperintensity volume, and apolipoprotein E genotype. Results were significant at p < 0.05 with family-wise error correction for multiple comparisons. An age-by-group interaction revealed that younger AD patients had significantly faster atrophy rates in the bilateral precuneus, parietal, and superior temporal lobes. These results suggest younger AD patients have predominantly posterior progressive atrophy, unexplained by white-matter hyperintensity, apolipoprotein E, or total intracranial volume. Clinical trials may benefit from adapting outcome measures for patient groups with lower average ages, to capture progressive atrophy in posterior cortices.

Patterns of atrophy in pathologically confirmed dementias: a voxelwise analysis.

OBJECTIVE: Imaging is recommended to support the clinical diagnoses of dementias, yet imaging research studies rarely have pathological confirmation of disease. This study aims to characterise patterns of brain volume loss in six primary pathologies compared with controls and to each other. METHODS: One hundred and eighty-six patients with a clinical diagnosis of dementia and histopathological confirmation of underlying pathology, and 73 healthy controls were included in this study. Voxel-based morphometry, based on ante-mortem T1-weighted MRI, was used to identify cross-sectional group differences in brain volume. RESULTS: Early-onset and late-onset Alzheimer's disease exhibited different patterns of grey matter volume loss, with more extensive temporoparietal involvement in the early-onset group, and more focal medial temporal lobe loss in the late-onset group. The Presenilin-1 group had similar parietal involvement to the early-onset group with localised volume loss in the thalamus, medial temporal lobe and temporal neocortex. Lewy body pathology was associated with less extensive volume loss than the other pathologies, although precentral/postcentral gyri volume was reduced in comparison with other pathological groups. Tau and TDP43A pathologies demonstrated similar patterns of frontotemporal volume loss, although less extensive on the right in the 4-repeat-tau group, with greater parietal involvement in the TDP43A group. The TDP43C group demonstrated greater left anterior-temporal involvement. CONCLUSIONS: Pathologically distinct dementias exhibit characteristic patterns of regional volume loss compared with controls and other dementias. Voxelwise differences identified in these cohorts highlight imaging signatures that may aid in the differentiation of dementia subtypes during life. The results of this study are available for further examination via NeuroVault (http://neurovault.org/collections/ADHMHOPN/).

White matter hyperintensities are associated with disproportionate progressive hippocampal atrophy.

This study investigates relationships between white matter hyperintensity (WMH) volume, cerebrospinal fluid (CSF) Alzheimer's disease (AD) pathology markers, and brain and hippocampal volume loss. Subjects included 198 controls, 345 mild cognitive impairment (MCI), and 154 AD subjects with serial volumetric 1.5-T MRI. CSF Aß42 and total tau were measured (n = 353). Brain and hippocampal loss were quantified from serial MRI using the boundary shift integral (BSI). Multiple linear regression models assessed the relationships between WMHs and hippocampal and brain atrophy rates. Models were refitted adjusting for (a) concurrent brain/hippocampal atrophy rates and (b) CSF Aß42 and tau in subjects with CSF data. WMH burden was positively associated with hippocampal atrophy rate in controls (P = 0.002) and MCI subjects (P = 0.03), and with brain atrophy rate in controls (P = 0.03). The associations with hippocampal atrophy rate remained following adjustment for concurrent brain atrophy rate in controls and MCIs, and for CSF biomarkers in controls (P = 0.007). These novel results suggest that vascular damage alongside AD pathology is associated with disproportionately greater hippocampal atrophy in nondemented older adults. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc.

Neuroanatomical correlates of prion disease progression - a 3T longitudinal voxel-based morphometry study.

PURPOSE: MRI has become an essential tool for prion disease diagnosis. However there exist only a few serial MRI studies of prion patients, and these mostly used whole brain summary measures or region of interest based approaches. We present here the first longitudinal voxel-based morphometry (VBM) study in prion disease. The aim of this study was to systematically characterise progressive atrophy in patients with prion disease and identify whether atrophy in specific brain structures correlates with clinical assessment. METHODS: Twenty-four prion disease patients with early stage disease (3 sporadic, 2 iatrogenic, 1 variant and 18 inherited CJD) and 25 controls were examined at 3T with a T1-weighted 3D MPRAGE sequence at multiple time-points (2-6 examinations per subject, interval range 0.1-3.2 years). Longitudinal VBM provided intra-subject and inter-subject image alignment, allowing voxel-wise comparison of progressive structural change. Clinical disease progression was assessed using the MRC Prion Disease Rating Scale. Firstly, in patients, we determined the brain regions where grey and white matter volume change between baseline and final examination correlated with the corresponding change in MRC Scale score. Secondly, in the 21/24 patients with interscan interval longer than 3 months, we identified regions where annualised rates of regional volume change in patients were different from rates in age-matched controls. Given the heterogeneity of the cohort, the regions identified reflect the common features of the different prion sub-types studied. RESULTS: In the patients there were multiple regions where volume loss significantly correlated with decreased MRC scale, partially overlapping with anatomical regions where yearly rates of volume loss were significantly greater than controls. The key anatomical areas involved included: the basal ganglia and thalamus, pons and medulla, the hippocampal formation and the superior parietal lobules. There were no areas demonstrating volume loss significantly higher in controls than patients or negative correlation between volume and MRC Scale score. CONCLUSIONS: Using 3T MRI and longitudinal VBM we have identified key anatomical regions of progressive volume loss which correlate with an established clinical disease severity index and are relevant to clinical deterioration. Localisation of the regions of progressive brain atrophy correlating most strongly with clinical decline may help to provide more targeted imaging endpoints for future clinical trials.

Multivariate dynamical modelling of structural change during development.

Here we introduce a multivariate framework for characterising longitudinal changes in structural MRI using dynamical systems. The general approach enables modelling changes of states in multiple imaging biomarkers typically observed during brain development, plasticity, ageing and degeneration, e.g. regional gray matter volume of multiple regions of interest (ROIs). Structural brain states follow intrinsic dynamics according to a linear system with additional inputs accounting for potential driving forces of brain development. In particular, the inputs to the system are specified to account for known or latent developmental growth/decline factors, e.g. due to effects of growth hormones, puberty, or sudden behavioural changes etc. Because effects of developmental factors might be region-specific, the sensitivity of each ROI to contributions of each factor is explicitly modelled. In addition to the external effects of developmental factors on regional change, the framework enables modelling and inference about directed (potentially reciprocal) interactions between brain regions, due to competition for space, or structural connectivity, and suchlike. This approach accounts for repeated measures in typical MRI studies of development and aging. Model inversion and posterior distributions are obtained using earlier established variational methods enabling Bayesian evidence-based comparisons between various models of structural change. Using this approach we demonstrate dynamic cortical changes during brain maturation between 6 and 22 years of age using a large openly available longitudinal paediatric dataset with 637 scans from 289 individuals. In particular, we model volumetric changes in 26 bilateral ROIs, which cover large portions of cortical and subcortical gray matter. We account for (1) puberty-related effects on gray matter regions; (2) effects of an early transient growth process with additional time-lag parameter; (3) sexual dimorphism by modelling parameter differences between boys and girls. There is evidence that the regional pattern of sensitivity to dynamic hidden growth factors in late childhood is similar across genders and shows a consistent anterior-posterior gradient with strongest impact to prefrontal cortex (PFC) brain changes. Finally, we demonstrate the potential of the framework to explore the coupling of structural changes across a priori defined subnetworks using an example of previously established resting state functional connectivity.

Investigating white matter fibre density and morphology using fixel-based analysis.

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.

Faster permutation inference in brain imaging.

Permutation tests are increasingly being used as a reliable method for inference in neuroimaging analysis. However, they are computationally intensive. For small, non-imaging datasets, recomputing a model thousands of times is seldom a problem, but for large, complex models this can be prohibitively slow, even with the availability of inexpensive computing power. Here we exploit properties of statistics used with the general linear model (GLM) and their distributions to obtain accelerations irrespective of generic software or hardware improvements. We compare the following approaches: (i) performing a small number of permutations; (ii) estimating the p-value as a parameter of a negative binomial distribution; (iii) fitting a generalised Pareto distribution to the tail of the permutation distribution; (iv) computing p-values based on the expected moments of the permutation distribution, approximated from a gamma distribution; (v) direct fitting of a gamma distribution to the empirical permutation distribution; and (vi) permuting a reduced number of voxels, with completion of the remainder using low rank matrix theory. Using synthetic data we assessed the different methods in terms of their error rates, power, agreement with a reference result, and the risk of taking a different decision regarding the rejection of the null hypotheses (known as the resampling risk). We also conducted a re-analysis of a voxel-based morphometry study as a real-data example. All methods yielded exact error rates. Likewise, power was similar across methods. Resampling risk was higher for methods (i), (iii) and (v). For comparable resampling risks, the method in which no permutations are done (iv) was the absolute fastest. All methods produced visually similar maps for the real data, with stronger effects being detected in the family-wise error rate corrected maps by (iii) and (v), and generally similar to the results seen in the reference set. Overall, for uncorrected p-values, method (iv) was found the best as long as symmetric errors can be assumed. In all other settings, including for familywise error corrected p-values, we recommend the tail approximation (iii). The methods considered are freely available in the tool PALM - Permutation Analysis of Linear Models.

MRI visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases.

Accurately distinguishing between different degenerative dementias during life is challenging but increasingly important with the prospect of disease-modifying therapies. Molecular biomarkers of dementia pathology are becoming available, but are not widely used in clinical practice. Conversely, structural neuroimaging is recommended in the evaluation of cognitive impairment. Visual assessment remains the primary method of scan interpretation, but in the absence of a structured approach, diagnostically relevant information may be under-utilized. This definitive, multi-centre study uses post-mortem confirmed cases as the gold standard to: (i) assess the reliability of six visual rating scales; (ii) determine their associated pattern of atrophy; (iii) compare their diagnostic value with expert scan assessment; and (iv) assess the accuracy of a machine learning approach based on multiple rating scales to predict underlying pathology. The study includes T1-weighted images acquired in three European centres from 184 individuals with histopathologically confirmed dementia (101 patients with Alzheimer's disease, 28 patients with dementia with Lewy bodies, 55 patients with frontotemporal lobar degeneration), and scans from 73 healthy controls. Six visual rating scales (medial temporal, posterior, anterior temporal, orbito-frontal, anterior cingulate and fronto-insula) were applied to 257 scans (two raters), and to a subset of 80 scans (three raters). Six experts also provided a diagnosis based on unstructured assessment of the 80-scan subset. The reliability and time taken to apply each scale was evaluated. Voxel-based morphometry was used to explore the relationship between each rating scale and the pattern of grey matter volume loss. Additionally, the performance of each scale to predict dementia pathology both individually and in combination was evaluated using a support vector classifier, which was compared with expert scan assessment to estimate clinical value. Reliability of scan assessment was generally good (intraclass correlation coefficient > 0.7), and average time to apply all six scales was <3 min. There was a very close association between the pattern of grey matter loss and the regions of interest each scale was designed to assess. Using automated classification based on all six rating scales, the accuracy (estimated using the area under the receiver-operator curves) for distinguishing each pathological group from controls ranged from 0.86-0.97; and from one another, 0.75-0.92. These results were substantially better than the accuracy of any single scale, at least as good as expert reads, and comparable to previous studies using molecular biomarkers. Visual rating scores from magnetic resonance images routinely acquired as part of the investigation of dementias, offer a practical, inexpensive means of improving diagnostic accuracy.

DIR-visible grey matter lesions and atrophy in multiple sclerosis: partners in crime?

BACKGROUND: The extent and clinical relevance of grey matter (GM) pathology in multiple sclerosis (MS) are increasingly recognised. GM pathology may present as focal lesions, which can be visualised using double inversion recovery (DIR) MRI, or as diffuse pathology, which can manifest as atrophy. It is, however, unclear whether the diffuse atrophy centres on focal lesions. This study aimed to determine if GM lesions and GM atrophy colocalise, and to assess their independent relationship with motor and cognitive deficits in MS. METHODS: Eighty people with MS and 30 healthy controls underwent brain volumetric T1-weighted and DIR MRI at 3 T, and had a comprehensive neurological and cognitive assessment. Probability mapping of GM lesions marked on the DIR scans and voxel- based morphometry (assessing GM atrophy) were carried out. The associations of GM lesion load and GM volume with clinical scores were tested. RESULTS: DIR-visible GM lesions were most commonly found in the right cerebellum and most apparent in patients with primary progressive MS. Deep GM structures appeared largely free from lesions, but showed considerable atrophy, particularly in the thalamus, caudate, pallidum and putamen, and this was most apparent in secondary progressive patients with MS. Very little co-localisation of GM atrophy and lesions was seen, and this was generally confined to the cerebellum and postcentral gyrus. In both regions, GM lesions and volume independently correlated with physical disability and cognitive performance. CONCLUSIONS: DIR-detectable GM lesions and GM atrophy do not significantly overlap in the brain but, when they do, they independently contribute to clinical disability.

Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres.

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.

Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance.

Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R(2)=0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4±35.4ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p<0.001) than for either SPM8 (R(2)=0.577 CI (0.500, 0.644)) or FreeSurfer (R(2)=0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV.

Dysconnectivity within the default mode in first-episode schizophrenia: a stochastic dynamic causal modeling study with functional magnetic resonance imaging.

We report the first stochastic dynamic causal modeling (sDCM) study of effective connectivity within the default mode network (DMN) in schizophrenia. Thirty-three patients (9 women, mean age = 25.0 years, SD = 5) with a first episode of psychosis and diagnosis of schizophrenia--according to the Diagnostic and Statistic Manual of Mental Disorders, 4th edition, revised criteria--were studied. Fifteen healthy control subjects (4 women, mean age = 24.6 years, SD = 4) were included for comparison. All subjects underwent resting state functional magnetic resonance imaging (fMRI) interspersed with 2 periods of continuous picture viewing. The anterior frontal (AF), posterior cingulate (PC), and the left and right parietal nodes of the DMN were localized in an unbiased fashion using data from 16 independent healthy volunteers (using an identical fMRI protocol). We used sDCM to estimate directed connections between and within nodes of the DMN, which were subsequently compared with t tests at the between subject level. The excitatory effect of the PC node on the AF node and the inhibitory self-connection of the AF node were significantly weaker in patients (mean values = 0.013 and -0.048 Hz, SD = 0.09 and 0.05, respectively) relative to healthy subjects (mean values = 0.084 and -0.088 Hz, SD = 0.15 and 0.77, respectively; P < .05). In summary, sDCM revealed reduced effective connectivity to the AF node of the DMN--reflecting a reduced postsynaptic efficacy of prefrontal afferents--in patients with first-episode schizophrenia.