Tau and amyloid biomarkers modify the degree to which cognitive reserve and brain reserve predict cognitive decline.

OBJECTIVE: Brain reserve, cognitive reserve, and education are thought to protect against late-life cognitive decline, but these variables have not been directly compared to one another in the same model, using future cognitive and functional decline as outcomes. We sought to determine whether the influence of these protective factors on executive function (EF) and daily function decline was dependent upon Alzheimer's disease (AD) pathology severity, as measured by the total tau to beta-amyloid (T-τ/Aß1-42) ratio in cerebrospinal fluid (CSF). METHOD: Participants were 1201 older adult volunteers in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Brain reserve was defined using a composite index of structural brain volumes (total brain matter, hippocampus, and white matter hyperintensity). Cognitive reserve was defined as the variance in episodic memory performance not explained by brain integrity and demographics. RESULTS: At higher levels of T-τ/Aß1-42, brain and cognitive reserve predicted slower decline in EF. Only brain reserve attenuated decline at lower levels of T-τ/Aß1-42. Education had no independent association with cognitive decline. CONCLUSIONS: These results point to a hierarchy of protection against aging- and disease-associated cognitive decline. When pathology is low, only structural brain integrity predicts rate of future EF decline. The ability of cognitive reserve to predict future EF decline becomes stronger as CSF biomarker evidence of AD increases. Although education is typically thought of as a proxy for cognitive reserve, it did not show any protective effects on cognition after accounting for brain integrity and the residual cognitive reserve index.

Increased cerebral blood flow with increased amyloid burden in the preclinical phase of alzheimer's disease.

BACKGROUND: Arterial spin labeling (ASL) is an emerging MRI technique for noninvasive measurement of cerebral blood flow (CBF) that has been used to show hemodynamic changes in the brains of people with Alzheimer's disease (AD). CBF changes have been measured using positron emission tomography (PET) across the AD spectrum, but ASL showed limited success in measuring CBF variations in the preclinical phase of AD, where amyloid ß (Aß) plaques accumulate in the decades prior to symptom onset. PURPOSE: To investigate the relationship between CBF measured by multiphase-pseudocontinuous-ASL (MP-PCASL) and Aß burden as measured by 11 C-PiB PET imaging in a study of cognitively normal (CN) subjects age over 65. STUDY TYPE: Cross-sectional. POPULATION: Forty-six CN subjects including 33 with low levels of Aß burden and 13 with high levels of Aß. FIELD STRENGTH/SEQUENCE: 3T/3D MP-PCASL. ASSESSMENT: The MP-PCASL method was chosen because it has a high signal-to-noise ratio. Furthermore, the data were analyzed using an efficient processing pipeline consisting of motion correction, ASL motion correction imprecision removal, temporal and spatial filtering, and partial volume effect correction. STATISTICAL TESTS: General Linear Model. RESULTS: In CN subjects positive for Aß burden (n = 13), we observed a positive correlation between CBF and Aß burden in the hippocampus, amygdala, caudate (P < 0.01), frontal, temporal, and insula (P < 0.05). DATA CONCLUSION: To the best of our knowledge, this is the first study using MP-PCASL in the study of AD, and the results suggest a potential compensatory hemodynamic mechanism that protects against pathology in the early stages of AD. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:505-513.

Non-linear realignment improves hippocampus subfield segmentation reliability.

Participant movement can deleteriously affect MR image quality. Further, for the visualization and segmentation of small anatomical structures, there is a need to improve image quality, specifically signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), by acquiring multiple anatomical scans consecutively. We aimed to ameliorate movement artefacts and increase SNR in a high-resolution turbo spin-echo (TSE) sequence acquired thrice using non-linear realignment in order to improve segmentation consistency of the hippocampus subfields. We assessed the method in 29 young healthy participants, 11 Motor Neuron Disease patients, and 11 age matched controls at 7T, and 24 healthy adolescents at 3T. Results show improved image segmentation of the hippocampus subfields when comparing template-based segmentations with individual segmentations with Dice overlaps N = 75; ps < 0.001 (Friedman's test) and higher sharpness ps < 0.001 in non-linearly realigned scans as compared to linearly, and arithmetically averaged scans.

Fibre-specific white matter reductions in Alzheimer's disease and mild cognitive impairment.

Alzheimer's disease is increasingly considered a large-scale network disconnection syndrome, associated with progressive aggregation of pathological proteins, cortical atrophy, and functional disconnections between brain regions. These pathological changes are posited to arise in a stereotypical spatiotemporal manner, targeting intrinsic networks in the brain, most notably the default mode network. While this network-specific disruption has been thoroughly studied with functional neuroimaging, changes to specific white matter fibre pathways within the brain's structural networks have not been closely investigated, largely due to the challenges of modelling complex white matter structure. Here, we applied a novel technique known as 'fixel-based analysis' to comprehensively investigate fibre tract-specific differences at a within-voxel level (called 'fixels') to assess potential axonal loss in subjects with Alzheimer's disease and mild cognitive impairment. We hypothesized that patients with Alzheimer's disease would exhibit extensive degeneration across key fibre pathways connecting default network nodes, while patients with mild cognitive impairment would exhibit selective degeneration within fibre pathways connecting regions previously identified as functionally implicated early in Alzheimer's disease. Diffusion MRI data from Alzheimer's disease (n = 49), mild cognitive impairment (n = 33), and healthy elderly control subjects (n = 95) were obtained from the Australian Imaging, Biomarkers and Lifestyle study of ageing. We assessed microstructural differences in fibre density, and macrostructural differences in fibre bundle morphology using fixel-based analysis. Whole-brain analysis was performed to compare groups across all white matter fixels. Subsequently, we performed a tract of interest analysis comparing fibre density and cross-section across 11 selected white matter tracts, to investigate potentially subtle degeneration within fibre pathways in mild cognitive impairment, initially by clinical diagnosis alone, and then by including amyloid status (i.e. a positive or negative amyloid PET scan). Our whole-brain analysis revealed significant white matter loss manifesting both microstructurally and macrostructurally in Alzheimer's disease patients, evident in specific fibre pathways associated with default mode network nodes. Reductions in fibre density and cross-section in mild cognitive impairment patients were only exhibited within the posterior cingulum when statistical analyses were limited to tracts of interest. Interestingly, these degenerative changes did not appear to be associated with high amyloid accumulation, given that amyloid-negative, but not positive, mild cognitive impairment subjects exhibited subtle focal left posterior cingulum deficits. The findings of this study demonstrated a stereotypical distribution of white matter degeneration in patients with Alzheimer's disease, which was in line with canonical findings from other imaging modalities, and with a network-based conceptualization of the disease.awx355media15726254535001.

Comparison of 18F-florbetaben quantification results using the standard Centiloid, MR-based, and MR-less CapAIBL® approaches: Validation against histopathology.

INTRODUCTION: 18F-florbetaben is currently approved for the visual rule out of ß-amyloid (Aß) pathology. It is also used for recruitment and as an outcome measure in therapeutic trials, requiring accurate and reproducible quantification of Aß burden in the brain. METHODS: Data from eighty-eight subjects (52 male subjects, aged 79.8 ± 10.6 years) who underwent antemortem 18F-florbetaben positron emission tomography scan and magnetic resonance imaging less than a year before neuropathological assessment at autopsy were evaluated. Image analysis was performed using the standard Centiloid (CL) statistical parametric mapping approach and CapAIBL®. Imaging results were compared against autopsy data. RESULTS: Against combined Bielschowsky silver staining and immunohistochemistry histopathological scores, statistical parametric mapping had 96% sensitivity, 96% specificity, and 95% accuracy, whereas magnetic resonance-less CapAIBL standardized uptake value ratioWhole Cerebellum had 94% sensitivity, 96% specificity, and 95% accuracy. Based on the combined histopathological scores, a CL threshold band of 19 ± 7 CL was determined. DISCUSSION: Quantification of 18F-florbetaben positron emission tomography scans using magnetic resonance-based and magnetic resonance-less CapAIBL® approaches showed high agreement, establishing a pathology-based threshold in CL.

Implementing the centiloid transformation for 11C-PiB and ß-amyloid 18F-PET tracers using CapAIBL.

The centiloid scale was recently proposed to provide a standard framework for the quantification of ß-amyloid PET images, so that amyloid burden can be expressed on a standard scale. While the framework prescribes SPM8 as the standard analysis method for PET quantification, non-standard methods can be calibrated to produce centiloid values. We have previously developed a PET-only quantification: CapAIBL. In this study, we show how CapAIBL can be calibrated to the centiloid scale. METHODS: Calibration images for 11C-PiB, 18F-NAV4694, 18F-Florbetaben, 18F-Flutemetamol and 18F- Florbetapir were analysed using the standard method and CapAIBL. Using these images, both methods were calibrated to the centiloid scale. Centiloid values computed using CapAIBL were compared to those computed using standard method. For each tracer, a separate validation was performed using an independent dataset from the AIBL study. RESULTS: Using the calibration images, there was a very strong agreement, and very little bias between the centiloid values computed using CapAIBL and those computed using the standard method with R2 > 0.97 across all tracers. Using images from AIBL, the agreement was also high with R2 > 0.96 across all tracers. In this dataset, there was a small underestimation of the centiloid values computed using CapAIBL of less than 0.8% in PiB, and a small over-estimation of 1.3% in Florbetapir, and 0.8% in Flutemetamol. There was a larger overestimation of 8% in NAV images, and 14% underestimation in Florbetaben images. However, some of these differences could be explained by the use of different scanners between the calibration scans and the ones used in AIBL. CONCLUSION: The PET-only quantification method, CapAIBL, can produce reliable centiloid values. The bias observed in the AIBL dataset for 18F-NAV4694 and 18F-Florbetaben may indicate that using different scanners or reconstruction methods might require scanner-specific adjustments.

Cerebral quantitative susceptibility mapping predicts amyloid-ß-related cognitive decline.

See Derry and Kent (doi:10.1093/awx167) for a scientific commentary on this article.The large variance in cognitive deterioration in subjects who test positive for amyloid-ß by positron emission tomography indicates that convergent pathologies, such as iron accumulation, might combine with amyloid-ß to accelerate Alzheimer's disease progression. Here, we applied quantitative susceptibility mapping, a relatively new magnetic resonance imaging method sensitive to tissue iron, to assess the relationship between iron, amyloid-ß load, and cognitive decline in 117 subjects who underwent baseline magnetic resonance imaging and amyloid-ß positron emission tomography from the Australian Imaging, Biomarkers and Lifestyle study (AIBL). Cognitive function data were collected every 18 months for up to 6 years from 100 volunteers who were either cognitively normal (n = 64) or diagnosed with mild cognitive impairment (n = 17) or Alzheimer's disease (n = 19). Among participants with amyloid pathology (n = 45), higher hippocampal quantitative susceptibility mapping levels predicted accelerated deterioration in composite cognition tests for episodic memory [ß(standard error) = -0.169 (0.034), P = 9.2 × 10-7], executive function [ß(standard error) = -0.139 (0.048), P = 0.004), and attention [ß(standard error) = -0.074 (0.029), P = 0.012]. Deteriorating performance in a composite of language tests was predicted by higher quantitative susceptibility mapping levels in temporal lobe [ß(standard error) = -0.104 (0.05), P = 0.036] and frontal lobe [ß(standard error) = -0.154 (0.055), P = 0.006]. These findings indicate that brain iron might combine with amyloid-ß to accelerate clinical progression and that quantitative susceptibility mapping could be used in combination with amyloid-ß positron emission tomography to stratify individuals at risk of decline.

Trajectories of depressive and anxiety symptoms in older adults: a 6-year prospective cohort study.

OBJECTIVE: Depressive and anxiety symptoms are common in older adults, significantly affect quality of life, and are risk factors for Alzheimer's disease. We sought to identify the determinants of predominant trajectories of depressive and anxiety symptoms in cognitively normal older adults. METHOD: Four hundred twenty-three older adults recruited from the general community underwent Aß positron emission tomography imaging, apolipoprotein and brain-derived neurotrophic factor genotyping, and cognitive testing at baseline and had follow-up assessments. All participants were cognitively normal and free of clinical depression at baseline. Latent growth mixture modeling was used to identify predominant trajectories of subthreshold depressive and anxiety symptoms over 6 years. Binary logistic regression analysis was used to identify baseline predictors of symptomatic depressive and anxiety trajectories. RESULTS: Latent growth mixture modeling revealed two predominant trajectories of depressive and anxiety symptoms: a chronically elevated trajectory and a low, stable symptom trajectory, with almost one in five participants falling into the elevated trajectory groups. Male sex (relative risk ratio (RRR) = 3.23), lower attentional function (RRR = 1.90), and carriage of the brain-derived neurotrophic factor Val66Met allele in women (RRR = 2.70) were associated with increased risk for chronically elevated depressive symptom trajectory. Carriage of the apolipoprotein epsilon 4 allele (RRR = 1.92) and lower executive function in women (RRR = 1.74) were associated with chronically elevated anxiety symptom trajectory. CONCLUSION: Our results indicate distinct and sex-specific risk factors linked to depressive and anxiety trajectories, which may help inform risk stratification and management of these symptoms in older adults at risk for Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd.

Partial volume model for brain MRI scan using MP2RAGE.

MP2RAGE is a T1 weighted MRI sequence that estimates a composite image providing much reduction of the receiver bias, has a high intensity dynamic range, and provides an estimate of T1 mapping. It is, therefore, an appealing option for brain morphometry studies. However, previous studies have reported a difference in cortical thickness computed from MP2RAGE compared with widely used Multi-Echo MPRAGE. In this article, we demonstrated that using standard segmentation and partial volume estimation techniques on MP2RAGE introduces systematic errors, and we proposed a new model to estimate partial volume of the cortical gray matter. We also included in their model a local estimate of tissue intensity to take into account the natural variation of tissue intensity across the brain. A theoretical framework is provided and validated using synthetic and physical phantoms. A repeatability experiment comparing MPRAGE and MP2RAGE confirmed that MP2RAGE using our model could be considered for structural imaging in brain morphology study, with similar cortical thickness estimate than that computed with MPRAGE. Hum Brain Mapp 38:5115-5127, 2017. © 2017 Wiley Periodicals, Inc.

Amyloid burden and incident depressive symptoms in cognitively normal older adults.

OBJECTIVE: Several studies have reported that non-demented older adults with clinical depression show changes in amyloid-ß (Aß) levels in blood, cerebrospinal fluid and on neuroimaging that are consistent with those observed in patients with Alzheimer's disease. These findings suggest that Aß may be one of the mechanisms underlying the relation between the two conditions. We sought to determine the relation between elevated cerebral Aß and the presence of depression across a 54-month prospective observation period. METHODS: Cognitively normal older adults from the Australian Imaging Biomarkers and Lifestyle study who were not depressed and had undergone a positron emission tomography scan to classify them as either high Aß (n = 81) or low Aß (n = 278) participated. Depressive symptoms were assessed using the Geriatric Depression Scale - Short Form at 18-month intervals over 54 months. RESULTS: Whilst there was no difference in probable depression between groups at baseline, incidence was 4.5 (95% confidence interval [CI] 1.3-16.4) times greater within the high Aß group (9%) than the low Aß group (2%) by the 54-month assessment. CONCLUSIONS: Results of this study suggest that elevated Aß levels are associated with a 4.5-fold increased likelihood of developing clinically significant depressive symptoms on follow-up in preclinical Alzheimer's disease. This underscores the importance of assessing, monitoring and treating depressive symptoms in older adults with elevated Aß. Copyright © 2016 John Wiley & Sons, Ltd.

BDNF Val66Met in preclinical Alzheimer's disease is associated with short-term changes in episodic memory and hippocampal volume but not serum mBDNF.

BACKGROUND: The brain-derived neurotrophic factor (BDNF) Val66Met polymorphism Met allele exacerbates amyloid (Aß) related decline in episodic memory (EM) and hippocampal volume (HV) over 36-54 months in preclinical Alzheimer's disease (AD). However, the extent to which Aß+ and BDNF Val66Met is related to circulating markers of BDNF (e.g. serum) is unknown. We aimed to determine the effect of Aß and the BDNF Val66Met polymorphism on levels of serum mBDNF, EM, and HV at baseline and over 18-months. METHODS: Non-demented older adults (n = 446) underwent Aß neuroimaging and BDNF Val66Met genotyping. EM and HV were assessed at baseline and 18 months later. Fasted blood samples were obtained from each participant at baseline and at 18-month follow-up. Aß PET neuroimaging was used to classify participants as Aß- or Aß+. RESULTS: At baseline, Aß+ adults showed worse EM impairment and lower serum mBDNF levels relative to Aß- adults. BDNF Val66Met polymorphism did not affect serum mBDNF, EM, or HV at baseline. When considered over 18-months, compared to Aß- Val homozygotes, Aß+ Val homozygotes showed significant decline in EM and HV but not serum mBDNF. Similarly, compared to Aß+ Val homozygotes, Aß+ Met carriers showed significant decline in EM and HV over 18-months but showed no change in serum mBDNF. CONCLUSION: While allelic variation in BDNF Val66Met may influence Aß+ related neurodegeneration and memory loss over the short term, this is not related to serum mBDNF. Longer follow-up intervals may be required to further determine any relationships between serum mBDNF, EM, and HV in preclinical AD.

Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks.

Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which probe the connectivity of neural networks, show significant promise. We present a machine learning approach to classify TBI participants primarily with mild traumatic brain injury (mTBI) based on altered structural connectivity patterns derived through the network based statistical analysis of structural connectomes generated from TBI and age-matched control groups. In this approach, higher order diffusion models were used to map white matter connections between 116 cortical and subcortical regions. Tracts between these regions were generated using probabilistic tracking and mean fractional anisotropy (FA) measures along these connections were encoded in the connectivity matrices. Network-based statistical analysis of the connectivity matrices was performed to identify the network differences between a representative subset of the two groups. The affected network connections provided the feature vectors for principal component analysis and subsequent classification by random forest. The validity of the approach was tested using data acquired from a total of 179 TBI patients and 146 controls participants. The analysis revealed altered connectivity within a number of intra- and inter-hemispheric white matter pathways associated with DAI, in consensus with existing literature. A mean classification accuracy of 68.16%±1.81% and mean sensitivity of 80.0%±2.36% were achieved in correctly classifying the TBI patients evaluated on the subset of the participants that was not used for the statistical analysis, in a 10-fold cross-validation framework. These results highlight the potential for statistical machine learning approaches applied to structural connectomes to identify patients with diffusive axonal injury.

Heritability and genetic correlation between the cerebral cortex and associated white matter connections.

The aim of this study is to investigate the genetic influence on the cerebral cortex, based on the analyses of heritability and genetic correlation between grey matter (GM) thickness, derived from structural MR images (sMRI), and associated white matter (WM) connections obtained from diffusion MRI (dMRI). We measured on sMRI the cortical thickness (CT) from a large twin imaging cohort using a surface-based approach (N = 308, average age 22.8 ± 2.3 SD). An ACE model was employed to compute the heritability of CT. WM connections were estimated based on probabilistic tractography using fiber orientation distributions (FOD) from dMRI. We then fitted the ACE model to estimate the heritability of CT and FOD peak measures along WM fiber tracts. The WM fiber tracts where genetic influence was detected were mapped onto the cortical surface. Bivariate genetic modeling was performed to estimate the cross-trait genetic correlation between the CT and the FOD-based connectivity of the tracts associated with the cortical regions. We found some cortical regions displaying heritable and genetically correlated GM thickness and WM connectivity, forming networks under stronger genetic influence. Significant heritability and genetic correlations between the CT and WM connectivity were found in regions including the right postcentral gyrus, left posterior cingulate gyrus, right middle temporal gyri, suggesting common genetic factors influencing both GM and WM. Hum Brain Mapp 37:2331-2347, 2016. © 2016 Wiley Periodicals, Inc.

ß-Amyloid, APOE and BDNF Genotype, and Depressive and Anxiety Symptoms in Cognitively Normal Older Women and Men.

OBJECTIVE: To examine how ß-amyloid (Aß), APOE and BDNF genotypes, and cortisol relate to depressive and anxiety symptoms in cognitively normal older women and men. METHODS: Cross-sectional data were analyzed from 423 older adults from the Australian Imaging Biomarkers and Lifestyle study. Analyses of covariance evaluated associations between Aß, APOE and BDNF genotype, and cortisol in relation to severity of depressive and anxiety symptoms. RESULTS: Among Aß+ older adults, APOE ε4 carriage was associated with greater severity of anxiety symptoms (d = 0.55); and in the full sample, APOE ε4 carriage was linked to greater severity of depressive (d = 0.26) and anxiety (d = 0.21) symptoms. Among Aß+ women, ε4 carriers reported greater anxiety symptoms than non-ε4 carriers (d = 0.83), and female BDNF rs6265 Val66 Met allele carriers reported greater depressive symptoms (d = 0.29). CONCLUSION: Sex moderated the relationship between Aß, APOE genotype, and BDNF genotype in predicting severity of anxiety and depressive symptoms in cognitively normal older adults.

Reproducibility of multiphase pseudo-continuous arterial spin labeling and the effect of post-processing analysis methods.

Arterial spin labeling (ASL) is an emerging MRI technique for non-invasive measurement of cerebral blood flow (CBF). Compared to invasive perfusion imaging modalities, ASL suffers from low sensitivity due to poor signal-to-noise ratio (SNR), susceptibility to motion artifacts and low spatial resolution, all of which limit its reliability. In this work, the effects of various state of the art image processing techniques for addressing these ASL limitations are investigated. A processing pipeline consisting of motion correction, ASL motion correction imprecision removal, temporal and spatial filtering, partial volume effect correction, and CBF quantification was developed and assessed. To further improve the SNR for pseudo-continuous ASL (PCASL) by accounting for errors in tagging efficiency, the data from multiphase (MP) acquisitions were analyzed using a novel weighted-averaging scheme. The performances of each step in terms of SNR and reproducibility were evaluated using test-retest ASL data acquired from 12 young healthy subjects. The proposed processing pipeline was shown to improve the within-subject coefficient of variation and regional reproducibility by 17% and 16%, respectively, compared to CBF maps computed following motion correction but without the other processing steps. The CBF measurements of MP-PCASL compared to PCASL had on average 23% and 10% higher SNR and reproducibility, respectively.

Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge.

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated "direct" measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: -1.4% to -2.2% (AD) and -0.35% to -0.67% (control), for ventricles: 4.6% to 10.2% (AD) and 1.2% to 3.4% (control), and for hippocampi: -1.5% to -7.0% (AD) and -0.4% to -1.4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods.

Grey scale texture analysis of endobronchial ultrasound mini probe images for prediction of benign or malignant aetiology.

BACKGROUND AND OBJECTIVE: Expert analysis of endobronchial ultrasound mini probe (EBUS-MP) images has established subjective criteria for discriminating benign and malignant disease. Minimal data are available for objective analysis of these images. The aim of this study was to determine if greyscale texture analysis could differentiate between benign and malignant lung lesions. METHODS: Digital EBUS-MP images with a gain setting of 10/19 and contrast setting of 4/8 from 2007 until 2012 inclusive were included. These images had an expert-defined region of interest (ROI) mapped. ROI were analysed for the following greyscale texture features: mean pixel value, difference between maximum and minimum pixel value, standard deviation of the mean pixel value, entropy, correlation, energy and homogeneity. Significant greyscale texture features differentiating benign from malignant disease were used by two physicians to assess a validation set. RESULTS: A total of 167 images were available. The first 85 lesions were used in the prediction set. Benign lesions had larger differences between maximum and minimum pixel values, larger standard deviations of the mean pixel values and higher entropy than malignant lesions (P < 0.0001 for all values). A total of 82 peripheral lesions were in the validation set. Physician 1 correctly classified 63/82 (76.8%) with a negative predictive value (NPV) for malignancy of 82% and positive predictive value (PPV) of 75%. Physician 2 correctly classified 62/82 (75.6%) with a NPV of 100% and PPV of 71.0%. CONCLUSIONS: Greyscale texture analysis of EBUS-MP images can help establish aetiology with a high NPV for malignancy.

Distance informed Track-Weighted Imaging (diTWI): a framework for sensitising streamline information to neuropathology.

Track-Weighted Imaging (TWI), where voxel intensity is based on image metrics encoded along streamline trajectories, provides a mechanism to study white matter disease. However, with generalised streamline weighting, it is difficult to localise the precise anatomical source and spread of injury or neuropathology. This limitation can be overcome by modulating the voxel weight based on the distance of the voxel from a given anatomical location along the tract, which we term diTWI: distance informed Track-Weighted Imaging. The location of known neuropathology can be delineated on any given imaging modality (e.g. MRI or PET). To demonstrate the clinical utility of this approach, we measured tumour cell infiltration along WM fibre tracts in 13 patients with newly diagnosed glioblastoma and 1 patient with Anaplastic Astrocytoma. TWI and diTWI maps were generated using information obtained from dynamic contrast enhanced MRI (area under the curve, AUC) and diffusivity maps (ADC and FA) with tumour boundaries automatically extracted using a logistic regression classifier. The accuracy of the derived tumour volumes was compared to those generated using 3,4-dihydroxy-6-[(18)F]-fluoro-l-phenylalanine (FDOPA) PET imaging. The accuracy of the tumour volumes generated from the diTWI maps was superior to volumes derived from the TWI, geometric distance or baseline AUC, FA and ADC maps. The relative overlap and relative dissimilarity rates for the diTWI generated tumour volumes after classification were found to be 82.3±15.3% (range 69.1-91.9) and 16.9±8.8% (range 7.9-37.5), respectively. These findings show that diTWI maps provide a useful framework for localising neuropathological processes occurring along WM pathways.

Investigating brain connectivity heritability in a twin study using diffusion imaging data.

Heritability of brain anatomical connectivity has been studied with diffusion-weighted imaging (DWI) mainly by modeling each voxel's diffusion pattern as a tensor (e.g., to compute fractional anisotropy), but this method cannot accurately represent the many crossing connections present in the brain. We hypothesized that different brain networks (i.e., their component fibers) might have different heritability and we investigated brain connectivity using High Angular Resolution Diffusion Imaging (HARDI) in a cohort of twins comprising 328 subjects that included 70 pairs of monozygotic and 91 pairs of dizygotic twins. Water diffusion was modeled in each voxel with a Fiber Orientation Distribution (FOD) function to study heritability for multiple fiber orientations in each voxel. Precision was estimated in a test-retest experiment on a sub-cohort of 39 subjects. This was taken into account when computing heritability of FOD peaks using an ACE model on the monozygotic and dizygotic twins. Our results confirmed the overall heritability of the major white matter tracts but also identified differences in heritability between connectivity networks. Inter-hemispheric connections tended to be more heritable than intra-hemispheric and cortico-spinal connections. The highly heritable tracts were found to connect particular cortical regions, such as medial frontal cortices, postcentral, paracentral gyri, and the right hippocampus.

Lesion segmentation from multimodal MRI using random forest following ischemic stroke.

Understanding structure-function relationships in the brain after stroke is reliant not only on the accurate anatomical delineation of the focal ischemic lesion, but also on previous infarcts, remote changes and the presence of white matter hyperintensities. The robust definition of primary stroke boundaries and secondary brain lesions will have significant impact on investigation of brain-behavior relationships and lesion volume correlations with clinical measures after stroke. Here we present an automated approach to identify chronic ischemic infarcts in addition to other white matter pathologies, that may be used to aid the development of post-stroke management strategies. Our approach uses Bayesian-Markov Random Field (MRF) classification to segment probable lesion volumes present on fluid attenuated inversion recovery (FLAIR) MRI. Thereafter, a random forest classification of the information from multimodal (T1-weighted, T2-weighted, FLAIR, and apparent diffusion coefficient (ADC)) MRI images and other context-aware features (within the probable lesion areas) was used to extract areas with high likelihood of being classified as lesions. The final segmentation of the lesion was obtained by thresholding the random forest probabilistic maps. The accuracy of the automated lesion delineation method was assessed in a total of 36 patients (24 male, 12 female, mean age: 64.57±14.23yrs) at 3months after stroke onset and compared with manually segmented lesion volumes by an expert. Accuracy assessment of the automated lesion identification method was performed using the commonly used evaluation metrics. The mean sensitivity of segmentation was measured to be 0.53±0.13 with a mean positive predictive value of 0.75±0.18. The mean lesion volume difference was observed to be 32.32%±21.643% with a high Pearson's correlation of r=0.76 (p<0.0001). The lesion overlap accuracy was measured in terms of Dice similarity coefficient with a mean of 0.60±0.12, while the contour accuracy was observed with a mean surface distance of 3.06mm±3.17mm. The results signify that our method was successful in identifying most of the lesion areas in FLAIR with a low false positive rate.