Capillary dysfunction in healthy elderly APOE ε4 carriers with raised brain Aß deposition.

INTRODUCTION: Capillary dysfunction, characterized by disturbances in capillary blood flow distribution, might be an overlooked factor in the development of Alzheimer's disease (AD). This study investigated microvascular blood flow in preclinical and prodromal AD individuals. METHODS: Using dynamic susceptibility contrast magnetic resonance imaging and positron emission tomography, we examined alterations in microvascular circulation and levels of Aß deposition in two independent cohorts of APOE ε4 carriers. RESULTS: Capillary dysfunction was elevated in both prodromal and preclinical AD individuals compared to age-matched controls. Additionally, the prodromal group exhibited higher levels of capillary dysfunction compared to the preclinical group. DISCUSSION: These findings suggest that capillary dysfunction can be detected at the preclinical stage of AD and indicates a worsening of capillary dysfunction throughout the AD continuum. Understanding the interaction between capillary dysfunction and Aß could provide insights into the relationship between cardiovascular risk factors and the development of AD. HIGHLIGHTS: Alzheimer's disease (AD) is associated with disturbances in microvascular circulation. Capillary dysfunction can be detected in preclinical AD. As cognitive symptoms progress in prodromal AD, capillary dysfunction worsens. Capillary dysfunction may impede the clearance of beta-amyloid (Aß). Capillary dysfunction might contribute to the development of AD.

Impaired cerebral microcirculation in isolated REM sleep behaviour disorder.

During the prodromal period of Parkinson's disease and other α-synucleinopathy-related parkinsonisms, neurodegeneration is thought to progressively affect deep brain nuclei, such as the locus coeruleus, caudal raphe nucleus, substantia nigra, and the forebrain nucleus basalis of Meynert. Besides their involvement in the regulation of mood, sleep, behaviour, and memory functions, these nuclei also innervate parenchymal arterioles and capillaries throughout the cortex, possibly to ensure that oxygen supplies are adjusted according to the needs of neural activity. The aim of this study was to examine whether patients with isolated REM sleep behaviour disorder, a parasomnia considered to be a prodromal phenotype of α-synucleinopathies, reveal microvascular flow disturbances consistent with disrupted central blood flow control. We applied dynamic susceptibility contrast MRI to characterize the microscopic distribution of cerebral blood flow in the cortex of 20 polysomnographic-confirmed patients with isolated REM sleep behaviour disorder (17 males, age range: 54-77 years) and 25 healthy matched controls (25 males, age range: 58-76 years). Patients and controls were cognitively tested by Montreal Cognitive Assessment and Mini Mental State Examination. Results revealed profound hypoperfusion and microvascular flow disturbances throughout the cortex in patients compared to controls. In patients, the microvascular flow disturbances were seen in cortical areas associated with language comprehension, visual processing and recognition and were associated with impaired cognitive performance. We conclude that cortical blood flow abnormalities, possibly related to impaired neurogenic control, are present in patients with isolated REM sleep behaviour disorder and associated with cognitive dysfunction. We hypothesize that pharmacological restoration of perivascular neurotransmitter levels could help maintain cognitive function in patients with this prodromal phenotype of parkinsonism.

On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data.

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.

Validation of structural brain connectivity networks: The impact of scanning parameters.

Evaluation of the structural connectivity (SC) of the brain based on tractography has mainly focused on the choice of diffusion model, tractography algorithm, and their respective parameter settings. Here, we systematically validate SC derived from a post mortem monkey brain, while varying key acquisition parameters such as the b-value, gradient angular resolution and image resolution. As gold standard we use the connectivity matrix obtained invasively with histological tracers by Markov et al. (2014). As performance metric, we use cross entropy as a measure that enables comparison of the relative tracer labeled neuron counts to the streamline counts from tractography. We find that high angular resolution and high signal-to-noise ratio are important to estimate SC, and that SC derived from low image resolution (1.03 mm3) are in better agreement with the tracer network, than those derived from high image resolution (0.53 mm3) or at an even lower image resolution (2.03 mm3). In contradiction, sensitivity and specificity analyses suggest that if the angular resolution is sufficient, the balanced compromise in which sensitivity and specificity are identical remains 60-64% regardless of the other scanning parameters. Interestingly, the tracer graph is assumed to be the gold standard but by thresholding, the balanced compromise increases to 70-75%. Hence, by using performance metrics based on binarized tracer graphs, one risks losing important information, changing the performance of SC graphs derived by tractography and their dependence of different scanning parameters.

The relationships between neuroinflammation, beta-amyloid and tau deposition in Alzheimer's disease: a longitudinal PET study.

BACKGROUND: The aim of this longitudinal study was to assess with positron emission tomography (PET) the relationship between levels of inflammation and the loads of aggregated ß-amyloid and tau at baseline and again after 2 years in prodromal Alzheimer's disease. METHODS: Forty-three subjects with mild cognitive impairment (MCI) had serial 11C-PK11195 PET over 2 years to measure inflammation changes, and 11C-PiB PET to determine ß-amyloid fibril load; 22 also had serial 18F-Flortaucipir PET to determine tau tangle load. Cortical surface statistical mapping was used to localise areas showing significant changes in tracer binding over time and to interrogate correlations between tracer binding of the tracers at baseline and after 2 years. RESULTS: Those MCI subjects with high 11C-PiB uptake at baseline (classified as prodromal Alzheimer's disease) had raised inflammation levels which significantly declined across cortical regions over 2 years although their ß-amyloid levels continued to rise. Those MCI cases who had low/normal 11C-PiB uptake at baseline but their levels then rose over 2 years were classified as prodromal AD with low Thal phase 1-2 amyloid deposition at baseline. They showed levels of cortical inflammation which correlated with their rising ß-amyloid load. Those MCI cases with baseline low 11C-PiB uptake that remained stable were classified as non-AD, and they showed no correlated inflammation levels. Finally, MCI cases which showed both high 11C-PiB and 18F-Flortaucipir uptake at baseline (MCI due to AD) showed a further rise in their tau tangle load over 2 years with a correlated rise in levels of inflammation. CONCLUSIONS: Our baseline and 2-year imaging findings are compatible with a biphasic trajectory of inflammation in Alzheimer's disease: MCI cases with low baseline but subsequently rising ß-amyloid load show correlated levels of microglial activation which then later decline when the ß-amyloid load approaches AD levels. Later, as tau tangles form in ß-amyloid positive MCI cases with prodromal AD, the rising tau load is associated with higher levels of inflammation.

Does inflammation precede tau aggregation in early Alzheimer's disease? A PET study.

OBJECTIVE: Our aim was to assess with positron emission tomography (PET) the temporal and spatial inter-relationships between levels of cortical microglial activation and the aggregated amyloid-ß and tau load in mild cognitive impairment (MCI) and early Alzheimer's disease (AD). METHODS: Six clinically probable AD and 20 MCI subjects had inflammation (11C-(R)-PK11195), amyloid (11C-PiB) and tau (18F-flortaucipir) PET, magnetic resonance imaging (MRI) and a neuropsychological assessment. Parametric images of tracer binding were interrogated at a voxel level and by region of interest analyses. RESULTS: 55% of MCI and 83% of AD subjects had a high amyloid-ß load. We have previously reported that clusters of correlated amyloid and inflammation levels are present in cortex. Here we found no correlation between levels of inflammation (11C-(R)-PK11195 BPND) and tau (18F-flortaucipir SUVR) or MMSE scores in high amyloid-ß cases. INTERPRETATION: While correlated levels of amyloid-ß and inflammation can be seen in MCI, we did not detect an association between levels of cortical tau tangles and inflammation in our series of high amyloid-ß cases. High levels of inflammation could be seen in amyloid-ß positive MCI cases where 18F-flortaucipir signals were low suggesting microglial activation precedes tau tangle formation. Inflammation levels were higher in high amyloid-ß MCI than in early AD cases, compatible with it initially playing a protective role.

Brain inflammation accompanies amyloid in the majority of mild cognitive impairment cases due to Alzheimer's disease.

See Kreisl (doi:10.1093/awx151) for a scientific commentary on this article.Subjects with mild cognitive impairment associated with cortical amyloid-ß have a greatly increased risk of progressing to Alzheimer's disease. We hypothesized that neuroinflammation occurs early in Alzheimer's disease and would be present in most amyloid-positive mild cognitive impairment cases. 11C-Pittsburgh compound B and 11C-(R)-PK11195 positron emission tomography was used to determine the amyloid load and detect the extent of neuroinflammation (microglial activation) in 42 mild cognitive impairment cases. Twelve age-matched healthy control subjects had 11C-Pittsburgh compound B and 10 healthy control subjects had 11C-(R)-PK11195 positron emission tomography for comparison. Amyloid-positivity was defined as 11C-Pittsburgh compound B target-to-cerebellar ratio above 1.5 within a composite cortical volume of interest. Supervised cluster analysis was used to generate parametric maps of 11C-(R)-PK11195 binding potential. Levels of 11C-(R)-PK11195 binding potential were measured in a selection of cortical volumes of interest and at a voxel level. Twenty-six (62%) of 42 mild cognitive impairment cases showed a raised cortical amyloid load compared to healthy controls. Twenty-two (85%) of the 26 amyloid-positive mild cognitive impairment cases showed clusters of increased cortical microglial activation accompanying the amyloid. There was a positive correlation between levels of amyloid load and 11C-(R)-PK11195 binding potentials at a voxel level within subregions of frontal, parietal and temporal cortices. 11C-(R)-PK11195 positron emission tomography reveals increased inflammation in a majority of amyloid positive mild cognitive impairment cases, its cortical distribution overlapping that of amyloid deposition.

White matter biomarkers from fast protocols using axially symmetric diffusion kurtosis imaging.

White matter tract integrity (WMTI) can characterize brain microstructure in areas with highly aligned fiber bundles. Several WMTI biomarkers have now been validated against microscopy and provided promising results in studies of brain development and aging, as well as in a number of brain disorders. Currently, WMTI is mostly used in dedicated animal studies and clinical studies of slowly progressing diseases, and has not yet emerged as a routine clinical tool. To this end, a less data intensive experimental method would be beneficial by enabling high resolution validation studies, and ease clinical applications by speeding up data acquisition compared with typical diffusion kurtosis imaging (DKI) protocols utilized as part of WMTI imaging. Here, we evaluate WMTI based on recently introduced axially symmetric DKI, which has lower data demand than conventional DKI. We compare WMTI parameters derived from conventional DKI with those calculated analytically from axially symmetric DKI. We employ numerical simulations, as well as data from fixed rat spinal cord (one sample) and in vivo human (three subjects) and rat brain (four animals). Our analysis shows that analytical WMTI based on axially symmetric DKI with sparse data sets (19 images) produces WMTI metrics that correlate strongly with estimates based on traditional DKI data sets (60 images or more). We demonstrate the preclinical potential of the proposed WMTI technique in in vivo rat brain (300 µm isotropic resolution with whole brain coverage in a 1 h acquisition). WMTI parameter estimates are subject to a duality leading to two solution branches dependent on a sign choice, which is currently debated. Results from both of these branches are presented and discussed throughout our analysis. The proposed fast WMTI approach may be useful for preclinical research and e.g. clinical evaluation of patients with traumatic white matter injuries or symptoms of neurovascular or neuroinflammatory disorders.

Capillary dysfunction is associated with symptom severity and neurodegeneration in Alzheimer's disease.

INTRODUCTION: We examined whether cortical microvascular blood volume and hemodynamics in Alzheimer's disease (AD) are consistent with tissue hypoxia and whether they correlate with cognitive performance and the degree of cortical thinning. METHODS: Thirty-two AD patients underwent cognitive testing, structural magnetic resonance imaging (MRI), and perfusion MRI at baseline and after 6 months. We measured cortical thickness, microvascular cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and capillary transit time heterogeneity (CTH) and estimated tissue oxygen tension (PtO2). RESULTS: At baseline, poor cognitive performance and regional cortical thinning correlated with lower CBF and CBV, with higher MTT and CTH and with low PtO2 across the cortex. Cognitive decline over time was associated with increasing whole brain relative transit time heterogeneity (RTH = CTH/MTT). DISCUSSION: Our results confirm the importance of microvascular pathology in AD. Deteriorating microvascular hemodynamics may cause hypoxia, which is known to precipitate amyloid retention.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge.

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis.

Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effective at the early stages of the pathology. Our objective is to investigate the efficiency of a new MR-based hippocampal grading score to detect incident dementia in cognitively intact patients. This new score is based on a pattern recognition strategy, providing a grading measure that reflects the similarity of the anatomical patterns of the subject under study with dataset composed of healthy subjects and patients with AD. Hippocampal grading was evaluated on subjects from the Three-City cohort, with a followup period of 12 years. Experiments demonstrate that hippocampal grading yields prediction accuracy up to 72.5% (P < 0.0001) 7 years before conversion to AD, better than both hippocampal volume (58.1%, P = 0.04) and MMSE score (56.9%, P = 0.08). The area under the ROC curve (AUC) supports the efficiency of imaging biomarkers with a gain of 8.4 percentage points for hippocampal grade (73.0%) over hippocampal volume (64.6%). Adaptation of the proposed framework to clinical score estimation is also presented. Compared with previous studies investigating new biomarkers for AD prediction over much shorter periods, the very long followup of the Three-City cohort demonstrates the important clinical potential of the proposed imaging biomarker. The high accuracy obtained with this new imaging biomarker paves the way for computer-based prognostic aides to help the clinician identify cognitively intact subjects that are at high risk to develop AD.

Effects of power training in older patients with multiple sclerosis on neurodegeneration, neuromuscular function, and physical function. A study protocol for the "power training in older multiple sclerosis patients (PoTOMS) randomized control trial.

Introduction: Approximately one-third of all persons with multiple sclerosis (pwMS) are older, i.e., having an age ≥60 years. Whilst ageing and MS separately elicit deteriorating effects on brain morphology, neuromuscular function, and physical function, the combination of ageing and MS may pose a particular challenge. To counteract such detrimental changes, power training (i.e., a type of resistance exercise focusing on moderate-to-high loading at maximal intended movement velocity) presents itself as a viable and highly effective solution. Power training is known to positively impact physical function, neuromuscular function, as well as brain morphology. Existing evidence is promising but limited to young and middle-aged pwMS, with the effects of power training remaining to be elucidated in older pwMS. Methods: The presented 'Power Training in Older MS patients (PoTOMS)' trial is a national, multi-center, parallel-group, randomized controlled trial. The trial compares 24 weeks of usual care(n = 30) to 24 weeks of usual care and power training (n = 30). The primary outcome is whole brain atrophy rate. The secondary outcomes include changes in brain micro and macro structures, neuromuscular function, physical function, cognitive function, bone health, and patient-reported outcomes. Ethics and dissemination: The presented study is approved by The Regional Ethics Committee (reference number 1-10-72-222-20) and registered at the Danish Data Protection Agency (reference number 2016-051-000001). All study findings will be published in scientific peer-reviewed journals and presented at relevant scientific conferences independent of the results. The www.clinicaltrials.gov identifier is NCT04762342.

Amyloid-ß aggregates activate peripheral monocytes in mild cognitive impairment.

The peripheral immune system is important in neurodegenerative diseases, both in protecting and inflaming the brain, but the underlying mechanisms remain elusive. Alzheimer's Disease is commonly preceded by a prodromal period. Here, we report the presence of large Aß aggregates in plasma from patients with mild cognitive impairment (n = 38). The aggregates are associated with low level Alzheimer's Disease-like brain pathology as observed by 11C-PiB PET and 18F-FTP PET and lowered CD18-rich monocytes. We characterize complement receptor 4 as a strong binder of amyloids and show Aß aggregates are preferentially phagocytosed and stimulate lysosomal activity through this receptor in stem cell-derived microglia. KIM127 integrin activation in monocytes promotes size selective phagocytosis of Aß. Hydrodynamic calculations suggest Aß aggregates associate with vessel walls of the cortical capillaries. In turn, we hypothesize aggregates may provide an adhesion substrate for recruiting CD18-rich monocytes into the cortex. Our results support a role for complement receptor 4 in regulating amyloid homeostasis.

Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning.

Predicting Alzheimer's disease (AD) in individuals with some symptoms of cognitive decline may have great influence on treatment choice and disease progression. Structural magnetic resonance imaging (MRI) has the potential of revealing early signs of neurodegeneration in the human brain and may thus aid in predicting and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated high sensitivity to cortical gray matter changes. In this study we investigated the possibility for using patterns of cortical thickness measurements for predicting AD in subjects with mild cognitive impairment (MCI). We used a novel technique for identifying cortical regions potentially discriminative for separating individuals with MCI who progress to probable AD, from individuals with MCI who do not progress to probable AD. Specific patterns of atrophy were identified at four time periods before diagnosis of probable AD and features were selected as regions of interest within these patterns. The selected regions were used for cortical thickness measurements and applied in a classifier for testing the ability to predict AD at the four stages. In the validation, the test subjects were excluded from the feature selection to obtain unbiased results. The accuracy of the prediction improved as the time to conversion from MCI to AD decreased, from 70% at 3 years before the clinical criteria for AD was met, to 76% at 6 months before AD. By inclusion of test subjects in the feature selection process, the prediction accuracies were artificially inflated to a range of 73% to 81%. Two important results emerge from this study. First, prediction accuracies of conversion from MCI to AD can be improved by learning the atrophy patterns that are specific to the different stages of disease progression. This has the potential to guide the further development of imaging biomarkers in AD. Second, the results show that one needs to be careful when designing training, testing and validation schemes to ensure that datasets used to build the predictive models are not used in testing and validation.

Capillary dysfunction correlates with cortical amyloid load in early Alzheimer's disease.

Alterations in cerebral perfusion is increasingly considered to play a crucial role in Alzheimer's disease (AD) and together with accumulated amyloid-ß, deficiencies in the brain microvascular circulation may result in local hypoxia. Here, we studied alterations in cerebral circulation and the correlation between amyloid-ß load and cerebral perfusion in prodromal AD (pAD). Using dynamic susceptibility contrast MRI and PET, we evaluated cerebral perfusion and amyloid-ß levels in 19 individuals with mild cognitive impairment (MCI) and high amyloid-ß load (pAD-MCI), 13 MCI individuals without AD pathology and 21 healthy controls. The pAD-MCI group showed significantly lower microvascular blood flow and significantly higher heterogeneity of microvascular blood transit times (p < 0.01) compared with the other 2 groups. Additionally, in the pAD-MCI group raised amyloid-ß levels correlated with decreased microvascular blood flow and increased heterogeneity of microvascular blood flow in frontal and temporal areas (p < 0.01). These results indicate a close connection between levels of amyloid-ß deposition and brain microvascular perfusion in pAD. A vicious cycle may be established where amyloid-ß load and deficiencies in brain perfusion may reinforce each other.

Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's disease.

In this paper, we propose an innovative approach to robustly and accurately detect Alzheimer's disease (AD) based on the distinction of specific atrophic patterns of anatomical structures such as hippocampus (HC) and entorhinal cortex (EC). The proposed method simultaneously performs segmentation and grading of structures to efficiently capture the anatomical alterations caused by AD. Known as SNIPE (Scoring by Non-local Image Patch Estimator), the novel proposed grading measure is based on a nonlocal patch-based frame-work and estimates the similarity of the patch surrounding the voxel under study with all the patches present in different training populations. In this study, the training library was composed of two populations: 50 cognitively normal subjects (CN) and 50 patients with AD, randomly selected from the ADNI database. During our experiments, the classification accuracy of patients (CN vs. AD) using several biomarkers was compared: HC and EC volumes, the grade of these structures and finally the combination of their volume and their grade. Tests were completed in a leave-one-out framework using discriminant analysis. First, we showed that biomarkers based on HC provide better classification accuracy than biomarkers based on EC. Second, we demonstrated that structure grading is a more powerful measure than structure volume to distinguish both populations with a classification accuracy of 90%. Finally, by adding the ages of subjects in order to better separate age-related structural changes from disease-related anatomical alterations, SNIPE obtained a classification accuracy of 93%.

BEaST: brain extraction based on nonlocal segmentation technique.

Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.

Reduced cortical thickness of brain areas involved in pain processing in patients with chronic pancreatitis.

BACKGROUND & AIMS: Patients with painful chronic pancreatitis (CP) might have abnormal brain function. We assessed cortical thickness in brain areas involved in visceral pain processing. METHODS: We analyzed brain morphologies of 19 patients with painful CP and compared them with 15 healthy individuals (controls) by using a 3T magnetic resonance scanner. By using an automated method with surface-based cortical segmentation, we assessed cortical thickness of the primary (SI) and secondary (SII) somatosensory cortex; prefrontal cortex (PFC); frontal cortex (FC); anterior (ACC), mid (MCC), and posterior (PCC) cingulate cortex; and insula. The occipital middle sulcus was used as a control area. The pain score was determined on the basis of the average daily amount of pain during 1 week. RESULTS: Compared with controls, patients with CP had reduced overall cortical thickness (P = .0012), without effects of modification for diabetes, alcoholic etiologies, or opioid treatment (all P values >.05). In patients with CP, the cortical thickness was decreased in SII (P = .002, compared with controls), PFC (P = .046), FC (P = .0003), MCC (P = .001), and insula (P = .002). There were no differences in cortical thickness between CP patients and controls in the control area (P = .20), SI (P = .06), ACC (P = .95), or PCC (P = .42). Cortical thickness in the affected areas correlated with pain score (r = 0.47, P = .003). CONCLUSIONS: In patients with CP, brain areas involved in pain processing have reduced cortical thickness. As a result of long-term, ongoing pain input to the neuromatrix, cortical thickness might serve as a measure for overall pain system dysfunction, as observed in other diseases characterized by chronic pain.

Capillary function progressively deteriorates in prodromal Alzheimer's disease: A longitudinal MRI perfusion study.

Cardiovascular risk factors are associated with the development of Alzheimer's disease (AD), and increasing evidence suggests that cerebral microvascular dysfunction plays a vital role in the disease progression. Using magnetic resonance imaging, we investigated the two-year changes of the cerebral microvascular blood flow in 11 mild cognitively impaired (MCI) patients with prodromal AD compared to 12 MCI patients without evidence of AD and 10 cognitively intact age-matched controls. The pAD-MCI patients displayed widespread deterioration in microvascular cerebral perfusion associated with capillary dysfunction. No such changes were observed in the other two groups, suggesting that the dysfunction in capillary perfusion is linked to the AD pathophysiology. The observed capillary dysfunction may limit local oxygenation in AD leading to downstream ß-amyloid aggregation, tau hyperphosphorylation, neuroinflammation and neuronal dysfunction. The findings are in agreement with the capillary dysfunction hypothesis of AD, suggesting that increasing heterogeneity of capillary blood flow is a primary pathological event in AD.

Longitudinal MRI study of cortical thickness, perfusion, and metabolite levels in major depressive disorder.

OBJECTIVE: To determine whether patients with major depressive disorder (MDD) display morphologic, functional, and metabolic brain abnormalities in limbic-cortical regions at a baseline magnetic resonance (MR) scan and whether these changes are normalized in MDD patients in remission at a follow-up scan. METHOD: A longitudinal 3.0-Tesla (T) magnetic resonance imaging (MRI) study was carried out with cortical thickness measurements with a surface-based approach, perfusion measurements with three-dimensional (3D) pseudo-continuous arterial spin labeling (pCASL), and spectroscopy (1H-MRS) measurements in the anterior cingulate cortex (ACC) with water as an internal reference adjusted for cerebrospinal fluid content. We examined 23 MDD patients and 26 healthy controls. MDD patients underwent a baseline MRI at inclusion and were invited to a follow-up scan when they were in remission or after a 6-month follow-up period. RESULTS: Major findings were a significantly thinner posterior cingulate cortex in non-remitters than in remitters, a significant decrease in perfusion in the frontal lobes and the ACC in non-remitters compared with healthy controls at baseline and significantly reduced N-acetylaspartate, myo-inositol, and glutamate levels in MDD patients compared with healthy controls at baseline. CONCLUSION: Using novel MRI techniques, we have found abnormalities in cerebral regions related to cortical-limbic pathways in MDD patients.