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OBJECTIVE—To analyse the natural history and survival of corticobasal degeneration by investigating the clinical features of 14 cases confirmed by postmortem examination. METHODS—Patients with definite corticobasal degeneration were selected from the research and clinical files of seven tertiary medical centres in Austria, the United Kingdom, and the United States. Clinical features were analysed in detail. RESULTS—The sample consisted of eight female and six male patients; mean age at symptom onset was 63 (SD 7.7) years, and mean disease duration was 7.9 (SD 2.6) years. The most commonly reported symptom at onset included asymmetric limb clumsiness with or without rigidity (50%) or tremor (21%). At the first neurological visit, on average 3.0 (SD 1.9) years after symptom onset, the most often encountered extrapyramidal features included unilateral limb rigidity (79%) or bradykinesia (71%), postural imbalance (45%), and unilateral limb dystonia (43%). Ideomotor apraxia (64%), and to a lesser extent cortical dementia (36%), were the most common cortical signs present at the first visit. During the course of the disease, virtually all patients developed asymmetric or unilateral akinetic rigid parkinsonism and a gait disorder. No patient had a dramatic response to levodopa therapy. Median survival time after onset of symptoms was 7.9 (SD 0.7) (range, 2.5-12.5) years, and, after the first clinic visit, 4.9 (SD 0.7) (range, 0.8-10) years. Early bilateral bradykinesia, frontal syndrome, or two out of tremor, rigidity, and bradykinesia, predicted a shorter survival. CONCLUSION—The results confirm that unilateral parkinsonism unresponsive to levodopa and limb ideomotor apraxia are the clinical hallmarks of corticobasal degeneration, and only a minority of patients with corticobasal degeneration present with dementia. The study also suggests that a focal cognitive and extrapyramidal motor syndrome is indicative of corticobasal degeneration. Survival in corticobasal degeneration was shortened by the early presence of (more) widespread parkinsonian features or frontal lobe syndrome.

Background: The presentation of symptoms for multiple system atrophy (MSA) varies. Because there are no specific markers for its clinical diagnosis, the diagnosis rests on the results of the neuropathologic examination. Despite several clinicopathologic studies, the diagnostic accuracy for MSA is unknown. Objectives: To determine the accuracy for the clinical diagnosis of MSA and to identify, as early as possible, those features that would best predict MSA. Design: One hundred five autopsy-confirmed cases of MSA and related disorders (MSA [n=16], non-MSA [n=89]) were presented as clinical vignettes to 6 neurologists (raters) who were unaware of the study design. Raters identified the main clinical features and provided a diagnosis based on descriptions of the patients' first and last clinic visits. Methods: Interrater reliability was evaluated with the use of k statistics. Raters' diagnoses and those of the primary neurologists (who followed up the patients) were compared with the autopsy-confirmed diagnoses to estimate the sensitivity and positive predictive values at the patients' first and last visits. Logistic regression analysis was used to determine the best predictors to diagnose MSA. Results: For the first visit (median, 42 months after the onset of symptoms), the raters' sensitivity (median, 56%; range, 50%-69%) and positive predictive values (median, 76%; range, 61%-91%) for the clinical diagnosis of MSA were not optimal. For the last visit (74 months after the onset of symptoms), the raters' sensitivity (median, 69%; range, 56%-94%) and positive predictive values (median, 80%; range, 77%-92%) improved. Primary neurologists correctly identified 25% and 50% of the patients with MSA at the first and last visits, respectively. False-negative and -positive misdiagnoses frequently occurred in patients with Parkinson disease and progressive supranuclear palsy. Early severe autonomic failure, absence of cognitive impairment, early cerebellar symptoms, and early gait disturbances were identified as the best predictive features to diagnose MSA. Conclusions: The low sensitivity for the clinical diagnosis of MSA, particularly among neurologists who followed up these patients in the tertiary centers, suggests that this disorder is underdiagnosed. The misdiagnosis of MSA is usually due to its confusion with Parkinson disease or progressive supranuclear palsy, thus compromising the research on all 3 disorders.

The accuracy of the clinical diagnosis of corticobasal degeneration (CBD) is unknown. To determine its diagnostic accuracy, we presented 105 cases with known neuropathologic diagnoses, including CBD (n = 10), progressive supranuclear palsy (PSP, n = 24), Parkinson's disease (n = 15), diffuse Lewy body disease (n = 14), multiple system atrophy (n = 16), postencephalitic parkinsonism (n = 7), Pick's disease (n = 7), Creutzfeldt-Jakob disease (n = 4), Alzheimer's disease (n = 4), vascular parkinsonism (n = 3), and Whipple's disease (n = 1), as clinical vignettes to six neurologists unaware of the autopsy findings. Reliability was measured with the kappa statistics. The neurologists' clinical diagnoses were compared with clinicopathologic diagnoses for sensitivity, specificity, and positive predictive values at first and last clinic visits. The group reliability for the diagnosis of CBD significantly improved from moderate for the first visit (mean = 34 months after onset) to substantial for the last (68 months after onset). For the first visit, mean sensitivity for CBD was low (35%), but specificity was near-perfect (99.6%). For the last visit, mean sensitivity minimally increased (48.3%), and specificity remained stable. False-negative misdiagnoses mainly occurred with PSP. False-positive diagnoses were rare. The extremely low sensitivity of the clinical diagnosis of CBD suggests that this disorder is markedly underdiagnosed. Although the validity of the clinical diagnosis might have been improved if neurologists could have examined these patients, more important is that this disorder was misdiagnosed by the primary neurologists. In our data set, the best predictors for the diagnosis of CBD included limb dystonia, ideomotor apraxia, myoclonus, and asymmetric akinetic-rigid syndrome with late onset of gait or balance disturbances.

Concussions are more serious than previously believed, and awareness of this fact has been increasing in both military and sports settings. Also known as mild traumatic brain injury (mTBI), concussions often occur multiple times in the same service member or athlete. These brain injuries can seriously and negatively affect patients, leading to changes in personality, sleep problems, and cognitive impairments. They can also increase the risk for suicide, posttraumatic stress disorder, depression, and anxiety. In some people, repetitive mTBI can lead to chronic traumatic encephalopathy (CTE), a neurodegenerative disorder. Evidence-based treatments are needed for both mTBI and CTE. Currently, symptom management and patient education are the best strategies to help those who have received multiple concussions. Education about preventing concussions and the use of return-to-play guidelines are especially important for young athletes. Language: en

Summary Artificial neural networks (ANNs), computer paradigms that can learn, excel in pattern recognition tasks such as disease diagnosis. Artificial neural networks operate in two different learning modes: supervised, in which a known diagnostic outcome is presented to the ANN, and unsupervised, in which the diagnostic outcome is not presented. A supervised learning ANN could emulate human expert diagnostic performance and identify relevant predictive markers in the diagnostic task, while an unsupervised learning ANN could suggest reasonable alternative diagnostic classification criteria. In the present study, we used ANN methodology to try to overcome the neuropathological difficulties in differentiating the subtypes of progressive supranuclear palsy (PSP), and in differentiati ng PSP from postencephalitic parkinsonism (PEP) and corticobasal degeneration, or Pick's disease from corticobasal degeneration. First, we applied supervised learning ANN to classify 62 cases of these disorders and to identify diagnostic markers that distinguish them. In a second experiment, we used unsupervised learning ANN to investigate possible alternative nosological classifications. Artificial neural networks input data for each case consisted of values representing histological features, including neurofibrillary tangles, neuronal loss and gliosis found in multiple brain sampling areas. The supervised learning ANN achieved excellent accuracy in classifying PSP but had difficulty classifying the other disorders. This method identified a few features that might help to differentiate PEP, supported currently proposed criteria for Pick's disease, corticobasal degeneration and typical PSP, but detected no features to characterize the atypical subtype of PSP. In general, unsupervised learning ANN supported the present nosological classification for PSP, PEP, Pick's disease and corticobasal degeneration, although it overlapped some groups. Artificial neural networks methodology appears promising for studying neurodegenerative disorders.

We assessed the validity and interrater reliability of neurologists who, using four different sets of previously published criteria for the clinical diagnosis of progressive supranuclear palsy (PSP), also called Steele-Richardson-Olszewski syndrome, rated 105 autopsy-proven cases of PSP (n = 24), Lewy body disease (n = 29), corticobasal ganglionic degeneration (n = 10), postencephalitic parkinsonism (n = 7), multiple system atrophy (n = 16), Pick's disease (n = 7), and other parkinsonian or dementia disorders (n = 12). Cases were presented in random order to six neurologists. Information from each patient's first and last visits to the medical center supplying the case was presented sequentially to the rater, and the rater's diagnosis was compared with the neuropathologic diagnosis of each case. Interrater agreement for the diagnosis of PSP varied from substantial to near perfect, but none of the criteria had both high sensitivity and high predictive value. Because of these limitations, we used a logistic regression analysis to identify the variables from the data set that would best predict the diagnosis. This analysis identified vertical supranuclear palsy with downward gaze abnormalities and postural instability with unexplained falls as the best features for predicting the diagnosis. From the results of the regression analysis and the addition of exclusionary features, we propose optimal criteria for the clinical diagnosis of PSP.

Multiple lines of evidence suggest that specific subtypes of age-related cataract (ARC) and Alzheimer disease (AD) are related etiologically. To identify shared genetic factors for ARC and AD, we estimated co-heritability of quantitative measures of cataract subtypes with AD-related brain MRI traits among 1,249 members of the Framingham Eye Study who had a brain MRI scan approximately ten years after the eye exam. Cortical cataract (CC) was found to be co-heritable with future development of AD and with several MRI traits, especially temporal horn volume (THV, ρ = 0.24, P

We present the preliminary neuropathologic criteria for progressive supranuclear palsy (PSP) as proposed at a workshop held at the National Institutes of Health, Bethesda, MD, April 24 and 25, 1993. The criteria distinguish typical, atypical, and combined PSP. A semiquantitative distribution of neurofibrillary tangles is the basis for the diagnosis of PSP. A high density of neurofibrillary tangles and neuropil threads in the basal ganglia and brainstem is crucial for the diagnosis of typical PSP. Tau-positive astrocytes or their processes in areas of involvement help to confirm the diagnosis. Atypical cases of PSP are variants in which the severity or distribution of abnormalities deviates from the typical pattern. Criteria excluding the diagnosis of typical and atypical PSP are large or numerous infarcts, marked diffuse or focal atrophy, Lewy bodies, changes diagnostic of Alzheimer's disease, oligodendroglial argyrophilic inclusions, Pick bodies, diffuse spongiosis, and prion protein-positive amyloid plaques. The diagnosis of combined PSP is proposed when other neurologic disorders exist concomitantly with PSP.

We examined patterns of neuronal degeneration in the motor cortex of amyotrophic lateral selerosis (ALS) patients using traditional cell stains and several histochemical markers including neurofilament, parvalbumin, NADPH-diaphorase, ubiquitin, Alz-50 and tau. Three grades of ALS (mild, moderate, severe) were defined based on the extent of Betz cell depletion. Non-phosphorylated neurofilament immunoreactive cortical pyramidal neurons and non-pyramidal parvalbumin local circuit neurons were significantly depleted in all grades of ALS. In contrast, NADPH-diaphorase neurons and Alz-50-positive neurons were quantitatively preserved despite reduced NADPH-diaphorase cellular staining and dendritic pruning. The density of ubiquitin-positive structures in the middle and deep layers of the motor cortex was increased in all cases. Axonal tau immunoreactivity was not altered. These histochemical results suggest that cortical degeneration in ALS is distinctive from other neurodegenerative diseases affecting cerebral cortex. Unlike Huntington's disease, both pyramidal and local cortical neurons are affected in ALS; unlike Alzheimer's disease, alteration of the neuronal cytoskeleton is not prominent. The unique pattern of neuronal degeneration found in ALS motor cortex is consistent with non-N-methyl-Dxxx-aspartate glutamate receptor-mediated cytotoxicity.

Recently, it has become clear that head trauma can lead to a progressive neurodegeneration known as chronic traumatic encephalopathy. Although the medical literature also implicates head trauma as a risk factor for Alzheimer's disease, these findings are predominantly based on clinical diagnostic criteria that lack specificity. The dementia that follows head injuries or repetitive mild trauma may be caused by chronic traumatic encephalopathy, alone or in conjunction with other neurodegenerations (for example, Alzheimer's disease). Prospective longitudinal studies of head-injured individuals, with neuropathological verification, will not only improve understanding of head trauma as a risk factor for dementia but will also enhance treatment and prevention of a variety of neurodegenerative diseases.

We examined the histological changes produced by injections of beta-amyloid [beta(1-40)], and control peptides in rat and monkey cerebral cortex. beta(25-35) injections were also studied in rat cortex. Standard immunoperoxidase procedures were used to detect the distribution of tau, MAP2, beta(1-40) and ALZ 50 immunoreactivity. All injections produced localized necrosis at the injection site surrounded by a zone of neuronal loss and gliosis. In rat cortex, lesions produced by solubilized beta(1-40) and beta(25-35) in water were generally larger than those produced by control peptides. Tau and ALZ 50 antibodies labeled neurites and diffusely positive perikarya around beta(1-40) injections, whereas MAP2 staining was reduced, paralleling the distribution of neuronal loss and gliosis. In aged primate cortex, beta(1-40) lesion size was dose dependent. Hyalinized, ALZ 50 positive neurons, and abnormal neurites were prominent around the injection site. Although beta-amyloid is acutely neurotoxic in both rat and monkey cerebral cortex, neuronal degeneration in the primate more closely resembles that found in AD.

CONVERGING LINES OF EVIdence have implicated 1 member of the vacuolar proteinsorting 10 family—the sortilin-related receptor, low-density lipoprotein receptor class A repeat-containing protein (SORL1) (GenBank 6653)—in the pathogenesis of Alzheimerdisease (AD).1-3 Pathological studies4,5 have documented reduced SORL1 expression in the brains of some patients with AD. Association between the risk of AD and multiple SORL1 single-nucleotide polymorphisms (SNPs) has been demonstrated in several studies3,6-10 that included populations of diverse ethnic background, used various study designs(ie, clinic case-control, family-based, or population-based), and relied on clinical or pathological criteria for AD, whereas the evidence of the association was equivocal in another study.11 One hypothesis for the role of SORL1 in the pathogenesis of AD is that SORL1 modulates subcellular trafficking of the amyloid precursor protein (APP). Reduced expression of SORL1 may lead to routing of APP into compartments where it is cleaved by the presenilin 1 complex to generate the amyloid β peptide.3 The role of SORL1 in APP processing is strongly supported by the fact that SORL1 interacts with endogenous APP holoproteins3,12 but does not bind to APP C-terminal fragments or to other type I membrane proteins.3 The SORL1 protein also belongs to a superfamily of low-density lipoprotein receptors (SorLA/LR11) that bind apolipoprotein E (APOE) and are implicated in cholesterol metabolism13 and atherogenesis.14 Human macrophages exposed to SORL1 have elevated lipid levels, and mice genetically deficient for SORL1 show signs of thickening arteries and macrophage infiltration relative to SORL1 knockout controls. These observations suggest a role for SORL1 in the development of atherosclerosis14 and raise the intriguing possibility that, like APOE and other cholesterol metabolism genes (eg, paraoxonase15), SORL1 may also increase dementia risk through effects on cerebrovascular abnormalities. Indeed, cerebrovascular disease increases the odds of dementia16 and potentially interacts with AD pathologic mechanisms to alter memory.17 Neurodegenerative and cerebrovascular processes can be linked to the known protein structure of SORL1 (Figure). Figure Inferred biochemical basis for the role of the sortilin-related receptor SORL1 in Alzheimer disease and cerebrovascular disease. The SORL1 protein contains several functional domains. The N-terminus region of the protein contains the vacuolar protein ... The possibility of multiple mechanisms of action associated with distinct SORL1 polymorphisms is supported by previous association studies. The SNPs associated with AD are located in 2 regions in SORL1 separated by a mean (SD) of 100 (15) kilobases that contain areas of tight linkage disequilibrium in white and African American subjects.3,18 Associations with SNPs in the region near the 5′ end have been reported in white European subjects, Hispanic subjects from the Dominican Republic, and Israeli-Arab subjects,3,6 whereas SNPs in the region closer to the 3′ end show association in African American, Han Chinese, and some white European and Hispanic subjects.3,6,8,10 Furthermore, associations with a specific 3-SNP haplotype in each of these locations have been replicated in multiple data sets representing several ethnic groups.3,6 These results suggest the existence of AD predisposing variants in different functional domains of SORL1 and thus raise the possibility for variable effects of intragenic polymorphisms on the biochemical properties of SORL1, on its cell-type–specific patterns of expression, or on both. To further elucidate how SORL1 gene variants influence processes leading to AD, we evaluated the association of SORL1 SNPs with brain magnetic resonance imaging (MRI) findings in a multiethnic group of families containing at least 1 sibpair discordant for AD. In addition, we compared SORL1 haplotypes with analogous measures of severity of AD and vascular abnormalities in a separate group of deceased individuals with pathologically confirmed AD.

We report the clinicopathological study of an 83-year-old man who abruptly developed complex visual hallucinations, disordered sleep, and mild cognitive impairment that persisted until his death 6 months later. Postmortem neuropathological examination including serial sections through the diencephalon, midbrain, and pons disclosed isolated bilateral infarcts confined to the medial substantia nigra pars reticulata. The findings suggest that destruction of the pars reticulata may be the essential feature to the development of peduncular hallucinosis.

A rat model of cerebral hemorrhage using stereotaxic injection of blood into the right basal ganglia was developed to investigate the influence of iron metabolism on the appearance of cerebral hemorrhage on MR images. Images of in vitro fixed brain sections stained specifically for different iron-storage substances, ferritin and hemosiderin, created by digitization of the pathology sections using an Eikonix CCD camera, were compared with the in vivo MR images of late-phase hematomas. Areas of the pathologic and MR features of the lesions were quantitatively correlated. The single-slice MR images were obtained with the use of T1- and T2-weighted spin-echo pulse sequences, as well as T2-weighted spin-echo pulse sequences in which the 180 degrees refocusing pulse was offset from the center of the echo time; this was termed an asymmetric spin-echo pulse sequence. The symmetric and asymmetric T2-weighted images allowed the calculation of line-width images, which emphasize line broadening from intravoxel magnetic field inhomogeneities that arise from the presence of iron-containing substances. From biochemical and histochemical staining, we conclude that at least two iron-storage substances are present in the late phase of resolving cerebral hematomas. Ferritin has a wider distribution than hemosiderin, showing a similar distribution to the MR signal changes of the calculated line-width images. Line-width mapping is a sensitive means of detecting magnetic field inhomogeneities caused by the magnetic susceptibility differences introduced by the aggregation of these iron-storage substances.

Background Caregiver exhaustion is a frequent consequence of sleep disturbance and rest-activity rhythm disruption that occurs in dementia. This exhaustion is the causal factor most frequently cited by caregivers in making the decision to institutionalize patients with dementia. Recent studies have implicated dysfunction of the circadian pacemaker in the etiology of these disturbances in dementia. Methods We studied the activity and core-body temperature rhythms in a cohort of 38 male patients with a clinical diagnosis of probable Alzheimer disease (AD) approximately 2 years before death. These patients were later given a confirmed diagnosis of AD (n = 23), frontotemporal degeneration (FTD) (n = 9), or diffuse Lewy body disease (DLB) with mixed AD or FTD pathologies (n = 6) after autopsy and neuropathological examination. Physiological rhythms of patients with AD and FTD were then compared with a group of normal, elderly men (n = 8) from the community. Results Alzheimer patients showed increased nocturnal activity and a significant phase-delay in their rhythms of core-body temperature and activity compared with patients with FTD and controls. The activity rhythm of FTD patients was highly fragmented and phase-advanced in comparison with controls and apparently uncoupled from the rhythm of core-body temperature. Conclusions Patients with AD and patients with FTD show different disturbances in their rhythms of activity and temperature compared with each other and with normal elderly patients.

Dysarthria and dysphagia are known to occur in parkinsonian syndromes such as Parkinson disease (PD), dementia with Lewy bodies (DLB), corticobasal degeneration (CBD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). Differences in the evolution of these symptoms have not been studied systematically in postmortem-confirmed cases.To study differences in the evolution of dysarthria and dysphagia in postmortem-confirmed parkinsonian disorders.Eighty-three pathologically confirmed cases (PD, n = 17; MSA, n = 15; DLB, n = 14; PSP, n = 24; and CBD, n = 13) formed the basis for a multicenter clinicopathological study organized by the National Institute of Neurological Disorders and Stroke, Bethesda, Md. Cases with enough clinicopathological documentation for the purpose of the study were selected from research and neuropathological files of 7 medical centers in 4 countries (Austria, France, England, and the United States).Median dysarthria latencies were short in PSP and MSA (24 months each), intermediate in CBD and DLB (40 and 42 months), and long in PD (84 months). Median dysphagia latencies were intermediate in PSP (42 months), DLB (43 months), CBD (64 months), and MSA (67 months), and long in PD (130 months). Dysarthria or dysphagia within 1 year of disease onset was a distinguishing feature for atypical parkinsonian disorders (APDs) (specificity, 100%) but failed to further distinguish among the APDs. Survival time after onset of a complaint of dysphagia was similar in PD, MSA, and PSP (15 to 24 months, P =.7) and latency to a complaint of dysphagia was highly correlated with total survival time (rho = 0.88; P.001) in all disorders.Latency to onset of dysarthria and dysphagia clearly differentiated PD from the APDs, but did not help distinguish different APDs. Survival after onset of dysphagia was similarly poor among all parkinsonian disorders. Evaluation and adequate treatment of patients with PD who complain of dysphagia might prevent or delay complications such as aspiration pneumonia, which in turn may improve quality of life and increase survival time.

Reply from the Authors: We thank Dr. Kertesz for his interest in our recent article in which we indicated the obstacles for an accurate clinical diagnosis of Pick's disease.1 We recognize that the relative frequency of Pick's disease presented to the six raters for evaluation was low. However, a low number of cases of this disorder was purposefully chosen because this disorder is rare not only in clinical practice but also in autopsy studies. Moreover, we believe that the underdiagnosis of Pick's disease is not fully explained by …

Presentation of Case A 76-year-old man was admitted to the hospital because of confusion and agitation. He was well and active until 14 weeks earlier, when his wife noticed that he became forgetful...

Chronic traumatic encephalopathy (CTE) is a neurodegeneration characterized by the abnormal accumulation of hyperphosphorylated tau protein within the brain. Like many other neurodegenerative conditions, at present, CTE can only be definitively diagnosed by post-mortem examination of brain tissue. As the first part of a series of consensus panels funded by the NINDS/NIBIB to define the neuropathological criteria for CTE, preliminary neuropathological criteria were used by 7 neuropathologists to blindly evaluate 25 cases of various tauopathies, including CTE, Alzheimer’s disease, progressive supranuclear palsy, argyrophilic grain disease, corticobasal degeneration, primary age-related tauopathy, and parkinsonism dementia complex of Guam. The results demonstrated that there was good agreement among the neuropathologists who reviewed the cases (Cohen’s kappa, 0.67) and even better agreement between reviewers and the diagnosis of CTE (Cohen’s kappa, 0.78). Based on these results, the panel defined the pathognomonic lesion of CTE as an accumulation of abnormal hyperphosphorylated tau (p-tau) in neurons and astroglia distributed around small blood vessels at the depths of cortical sulci and in an irregular pattern. The group also defined supportive but non-specific p-tau-immunoreactive features of CTE as: pretangles and NFTs affecting superficial layers (layers II–III) of cerebral cortex; pretangles, NFTs or extracellular tangles in CA2 and pretangles and proximal dendritic swellings in CA4 of the hippocampus; neuronal and astrocytic aggregates in subcortical nuclei; thorn-shaped astrocytes at the glial limitans of the subpial and periventricular regions; and large grain-like and dot-like structures. Supportive non-p-tau pathologies include TDP-43 immunoreactive neuronal cytoplasmic inclusions and dot-like structures in the hippocampus, anteromedial temporal cortex and amygdala. The panel also recommended a minimum blocking and staining scheme for pathological evaluation and made recommendations for future study. This study provides the first step towards the development of validated neuropathological criteria for CTE and will pave the way towards future clinical and mechanistic studies. Electronic supplementary material The online version of this article (doi:10.1007/s00401-015-1515-z) contains supplementary material, which is available to authorized users.

Automated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0.13 mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e.g., amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1 mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6.0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1 mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and classification based on whole hippocampal volume (82% accuracy)., National Center for Research Resources (U.S.) (P41EB015896), National Center for Research Resources (U.S.) (BIRN002 U24 RR021382), National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01EB013565), National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01EB006758), National Institute on Aging (AG022381), National Institute on Aging (5R01AG008122-22), National Institute on Aging (K01AG028521), National Institute on Aging (P30AG13846), National Institute on Aging (R01AG1649), National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01), National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01), National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01), National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963), National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534), United States. Dept. of Health and Human Services. Shared Instrumentation Grant Program (1S10RR023401), United States. Dept. of Health and Human Services. Shared Instrumentation Grant Program (1S10RR019307), United States. Dept. of Health and Human Services. Shared Instrumentation Grant Program (1S10RR023043), Ellison Medical Foundation, National Institutes of Health (U.S.). Blueprint for Neuroscience Research (5U01-MH093765), United States. National Institutes of Health (P30-AG010129), United States. National Institutes of Health (K01-AG030514)

The rapid correction or over-correction of hyponatremia is believed by many to be the crucial factor in the causation of central pontine myelinolysis (CPM). Over a 17-year period we found CPM in 10 (7%) of the 139 burn patients examined postmortem but in only 10 (0.28%) of the 3,528 patients in the general autopsy population (p less than 0.001). Each of the burn patients with CPM had suffered a prolonged, nonterminal episode of extreme serum hyperosmolality, whereas most burn patients without CPM had not suffered such an episode. The histologic age of the lesions correlated with the duration of time between the hyperosmolar episode and death. Hypernatremia, hyperglycemia, and azotemia, alone or combined, accounted for the hyperosmolality. No single electrolyte or metabolic derangement was essential, as in at least one burn patient with CPM the serum sodium, glucose, or blood urea nitrogen was normal during the hyperosmolar episode. Hyponatremia was not present in any burn patient with CPM. We conclude that severely burned patients, like alcoholics, are especially susceptible to CPM, and that in burn patients with CPM there is a striking association with serum hyperosmolality. The data also suggest that the rapid correction of hyponatremia exerts its effects by causing an osmotic shift and not because of any specific property of the sodium ion.

Cytoskeletal disruption is a key pathological feature of Alzheimer's disease (AD). We used refined immunocytochemical techniques to define the range of abnormalities affecting the microtubule system in AD hippocampus. Minimal tau and tubulin immunoreactivity was granular and accumulated in otherwise normal neuronal perikarya. As tau-reactive neurofibrillary tangles formed, granular tau and tubulin staining diminished, and ubiquitin reactivity developed. In regions of high neurofibrillary tangle density, microtubule-associated protein 2 (MAP2) histochemical features of remaining nontangled neurons included apical dendritic degeneration with proliferation of basal dendrites. In addition to perisomatic dendritic proliferation, there was massive sprouting of tau-immunoreactive distal dystrophic neurites. Sprouting proximal dendrites and dystrophic neurites often demonstrated growth-cone-like lamellipodia and filopodia. Degeneration of the perisomatic proliferating dendrites was characterized by the accumulation of fibrillar tau immunoreactivity. The colocalization of MAP2 and tau in growth structures recapitulated their codistribution in developing neurites. The data suggest that extensive plasticity and growth response occur in tandem with neuronal degeneration in AD, and that reorganization of the cytoskeletal microtubule system may underlie these proliferative changes.

Two patients with head injury and pulmonary emboli of brain tissue are described. Both patients developed persistent bleeding with elevations of prothrombin time, partial thromboplastin time, and decreased platelet counts. Postmortem examination identified multiple skull fractures, subdural and subarachnoid blood, cortical tears, and intraparenchymal hemorrhages. Lacerations of dural venous sinuses were documented in each case. Multiple pulmonary arteries contained plugs of cerebral tissue, including fragments of cerebral and cerebellar cortex, and white matter. The literature concerning brain tissue emboli is reviewed. In neonates, three patients who survived longer than 1 hour were found to have evidence of persistent hemorrhage. Among both children and adults, our two cases are the only ones reported with associated clotting abnormalities. Brain tissue embolism in neonates occurs after difficult vaginal deliveries, often in conjunction with the use of forceps. Tears of the tentorium cerebelli or falx cerebri have been documented at postmortem examination in the majority of these cases. By contrast, brain tissue embolism in children and adults occurs in association with severe closed or penetrating head injury. In several cases, as in the two reported here, postmortem examination has demonstrated a large cerebral venous defect as the probable site of entry of brain tissue into the systemic circulation. Language: en

We report our ongoing work to characterize the molecular nature of neurofibrillary tangles (NFT). An epitope map of tau protein using monoclonal antibodies that crossreact with NFT reveals the presence of epitopes that span the entire tau molecule from the amino terminus to the carboxy terminus. Several antibodies that recognize tau protein including Alz50 do not recognize primary amino acid sequence but are directed either against a post-translational modification or a complex higher order structure. The importance of tau protein in the development of the pathology is underscored by the extent of the tau-reactive neuritic lesions. These dystrophic neurites or "curly fibers" extend well beyond the classical distributions of the senile plaques and NFT. Furthermore, the neuropil lesion is considerably more extensive than either the senile plaques or neurofibrillary tangles. One of the features of the dystrophy in Alzheimer's disease is widespread neuronal sprouting characteristic of dystrophic neurites and tangle-bearing cells.

Progressive supranuclear palsy (PSP) and cortocobasal degeneration (CBD) are often clinically confused with each other because they share a rapid disease progression, parkinsonism that responds poorly or transiently to levodopa therapy, and associated signs (e.g., ocular abnormalities, pyramidal signs and cognitive involvement). To improve the accuracy in diagnosing these disorders, this study examined the clinical features of 51 patients pathologically diagnosed with PSP and CBD. Logistic regression analysis identified two sets of predictors (models) for CBD patients, one consisting of asymmetric parkinsonism, cognitive disturbances at onset and instability and falls at first clinic visit, and the other one of asymmetric parkinsonism, cognitive disturbances at symptom onset and speech disturbances. While PSP patients often had severe postural instability at onset, symmetric parkinsonism, vertical supranuclear gaze palsy, speech and frontal lobe-type features, CBD patients presented with lateralized motor (e.g., parkinsonism, dystonia or myoclonus) and cognitive signs (e.g., ideomotor apraxia, aphasia or alien limb). On the other hand, CBD patients presenting with an alternate phenotype characterized by early severe frontal dementia and bilateral parkinsonism were generally misdiagnosed. PSP patients without vertical supranuclear gaze palsy were misdiagnosed. Recognizing the features which differentiate these disorders and the less obvious disease presentations as well as developing an increased index of suspicion will improve the diagnostic accuracy of these disorders.

The chronic effects of repetitive head impacts (RHI) on the development of neuroinflammation and its relationship to chronic traumatic encephalopathy (CTE) are unknown. Here we set out to determine the relationship between RHI exposure, neuroinflammation, and the development of hyperphosphorylated tau (ptau) pathology and dementia risk in CTE. We studied a cohort of 66 deceased American football athletes from the Boston University-Veteran’s Affairs-Concussion Legacy Foundation Brain Bank as well as 16 non-athlete controls. Subjects with a neurodegenerative disease other than CTE were excluded. Counts of total and activated microglia, astrocytes, and ptau pathology were performed in the dorsolateral frontal cortex (DLF). Binary logistic and simultaneous equation regression models were used to test associations between RHI exposure, microglia, ptau pathology, and dementia. Duration of RHI exposure and the development and severity of CTE were associated with reactive microglial morphology and increased numbers of CD68 immunoreactive microglia in the DLF. A simultaneous equation regression model demonstrated that RHI exposure had a significant direct effect on CD68 cell density (p

Accepted September 14, 1998. From the Neuropharmacology Unit, Defense and Veterans Head Injury Program, Henry M. Jackson Foundation, and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland; Veterans Administration Medical Center GRECC, Bedford, Massachusetts; and Departments of Neurology and Pathology, Boston University Medical School, Boston, Massachusetts. Address correspondence to Dr. Litvan, Neuropharmacology Unit, Defense and Veterans Head Injury Program, Henry M. Jackson Foundation, NINDS, NIH, Federal Building, Room 714, 7550 Wisconsin Avenue, Bethesda, MD 20892-9130; e-mail: litvan1@helix.nih.gov Copyright q 1999 American Psychiatric Press, Inc. Clinicopathologic Case Report

Publisher's version (útgefin grein), Objective: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. Methods We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n=20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p[BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p[BI]= 4.4 × 10-10; p [SSBI] = 1.2 × 10 -4), diabetes (p[BI] = 1.7 × 10 -8; p [SSBI] = 2.8 × 10 -3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10 -24), and MRI-defined white matter hyperintensity burden (p [BI]=1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. Conclusion: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI., CHAP: R01-AG-11101, R01-AG-030146, NIRP-14-302587. SMART: This study was supported by a grant from the Netherlands Organization for Scientific Research–Medical Sciences (project no. 904-65–095). LBC: The authors thank the LBC1936 participants and the members of the LBC1936 research team who collected and collated the phenotypic and genotypic data. The LBC1936 is supported by Age UK (Disconnected Mind Programme grant). The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1). The brain imaging was performed in the Brain Research Imaging Centre (https://www.ed.ac.uk/clinical-sciences/edinburgh-imaging), a center in the SINAPSE Collaboration (sinapse.ac.uk) supported by the Scottish Funding Council and Chief Scientist Office. Funding from the UK Biotechnology and Biological Sciences Research Council (BBSRC) and the UK Medical Research Council is acknowledged. Genotyping was supported by a grant from the BBSRC (ref. BB/F019394/1). PROSPER: The PROSPER study was supported by an investigator-initiated grant obtained from Bristol-Myers Squibb. Prof. Dr. J.W. Jukema is an Established Clinical Investigator of the Netherlands Heart Foundation (grant 2001 D 032). Support for genotyping was provided by the seventh framework program of the European commission (grant 223004) and by the Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging grant 050-060-810). SCES and SiMES: National Medical Research Council Singapore Centre Grant NMRC/CG/013/2013. C.-Y.C. is supported by the National Medical Research Council, Singapore (CSA/033/2012), Singapore Translational Research Award (STaR) 2013. Dr. Kamran Ikram received additional funding from the Singapore Ministry of Health's National Medical Research Council (NMRC/CSA/038/2013). SHIP: SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs, as well as the Social Ministry of the Federal State of Mecklenburg–West Pomerania, and the network “Greifswald Approach to Individualized Medicine (GANI_MED)” funded by the Federal Ministry of Education and Research (grant 03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers, Erlangen, Germany, and the Federal State of Mecklenburg–West Pomerania. Whole-body MRI was supported by a joint grant from Siemens Healthineers, Erlangen, Germany, and the Federal State of Mecklenburg–West Pomerania. The University of Greifswald is a member of the Caché Campus program of the InterSystems GmbH. OATS (Older Australian Twins Study): OATS was supported by an Australian National Health and Medical Research Council (NHRMC)/Australian Research Council (ARC) Strategic Award (ID401162) and by a NHMRC grant (ID1045325). OATS was facilitated via access to the Australian Twin Registry, which is supported by the NHMRC Enabling Grant 310667. The OATS genotyping was partly supported by a Commonwealth Scientific and Industrial Research Organisation Flagship Collaboration Fund Grant. NOMAS: The Northern Manhattan Study is funded by the NIH grant “Stroke Incidence and Risk Factors in a Tri-Ethnic Region” (NINDS R01NS 29993). TASCOG: NHMRC and Heart Foundation. AGES: The study was funded by the National Institute on Aging (NIA) (N01-AG-12100), Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament), with contributions from the Intramural Research Programs at the NIA, the National Heart, Lung, and Blood Institute (NHLBI), and the National Institute of Neurological Disorders and Stroke (NINDS) (Z01 HL004607-08 CE). ERF: The ERF study as a part of European Special Populations Research Network (EUROSPAN) was supported by European Commission FP6 STRP grant no. 018947 (LSHG-CT-2006-01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007–2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the programme “Quality of Life and Management of the Living Resources” of 5th Framework Programme (no. QLG2-CT-2002-01254). High-throughput analysis of the ERF data was supported by a joint grant from Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). Exome sequencing analysis in ERF was supported by the ZonMw grant (project 91111025). Najaf Amin is supported by the Netherlands Brain Foundation (project no. F2013[1]-28). ARIC: The Atherosclerosis Risk in Communities study was performed as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HSN268201100006C, HSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL70825, R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and NIH contract HHSN268200625226C. Infrastructure was partly supported by grant no. UL1RR025005, a component of the NIH and NIH Roadmap for Medical Research. This project was also supported by NIH R01 grant NS087541 to M.F. FHS: This work was supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (contracts no. N01-HC-25195 and no. HHSN268201500001I), and its contract with Affymetrix, Inc. for genotyping services (contract no. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. This study was also supported by grants from the NIA (R01s AG033040, AG033193, AG054076, AG049607, AG008122, and U01-AG049505) and the NINDS (R01-NS017950, UH2 NS100605). Dr. DeCarli is supported by the Alzheimer's Disease Center (P30 AG 010129). ASPS: The research reported in this article was funded by the Austrian Science Fund (FWF) grant nos. P20545-P05, P13180, and P20545-B05, by the Austrian National Bank Anniversary Fund, P15435, and the Austrian Ministry of Science under the aegis of the EU Joint Programme–Neurodegenerative Disease Research (JPND) (jpnd.eu). LLS: The Leiden Longevity Study has received funding from the European Union's Seventh Framework Programme (FP7/2007–2011) under grant agreement no. 259679. This study was supported by a grant from the Innovation-Oriented Research Program on Genomics (SenterNovem IGE05007), the Centre for Medical Systems Biology, and the Netherlands Consortium for Healthy Ageing (grant 050-060-810), all in the framework of the Netherlands Genomics Initiative, Netherlands Organization for Scientific Research (NWO), UnileverColworth, and by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007). CHS: This CHS research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC15103, and HHSN268200960009C and grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, R01HL085251, and R01HL130114 from the NHLBI with additional contribution from NINDS. Additional support was provided through R01AG023629 from the NIA. A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Rotterdam Study: The generation and management of GWAS genotype data for the Rotterdam Study is supported by the Netherlands Organisation of Scientific Research (NWO) Investments (no. 175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/NWO project no. 050-060-810. The Rotterdam Study is funded by Erasmus MC Medical Center and Erasmus MC University, Rotterdam, Netherlands Organization for Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. M.A.I. is supported by an NWO Veni grant (916.13.054). The 3-City Study: The 3-City Study is conducted under a partnership agreement among the Institut National de la Santé et de la Recherche Médicale (INSERM), the University of Bordeaux, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, Mutuelle Générale de l’Education Nationale (MGEN), Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Programme “Cohortes et collections de données biologiques.” C.T. and S.D. have received investigator-initiated research funding from the French National Research Agency (ANR) and from the Fondation Leducq. S.D. is supported by a starting grant from the European Research Council (SEGWAY), a grant from the Joint Programme of Neurodegenerative Disease research (BRIDGET), from the European Union's Horizon 2020 research and innovation programme under grant agreements No 643417 & No 640643, and by the Initiative of Excellence of Bordeaux University. Part of the computations were performed at the Bordeaux Bioinformatics Center (CBiB), University of Bordeaux. This work was supported by the National Foundation for Alzheimer's Disease and Related Disorders, the Institut Pasteur de Lille, the Labex DISTALZ, and the Centre National de Génotypage. ADGC: The Alzheimer Disease Genetics Consortium is supported by NIH. NIH-NIA supported this work through the following grants: ADGC, U01 AG032984, RC2 AG036528; NACC, U01 AG016976; NCRAD, U24 AG021886; NIA LOAD, U24 AG026395, U24 AG026390; Banner Sun Health Research Institute, P30 AG019610; Boston University, P30 AG013846, U01 AG10483, R01 CA129769, R01 MH080295, R01 AG017173, R01 AG025259, R01AG33193; Columbia University, P50 AG008702, R37 AG015473; Duke University, P30 AG028377, AG05128; Emory University, AG025688; Group Health Research Institute, UO1 AG06781, UO1 HG004610; Indiana University, P30 AG10133; Johns Hopkins University, P50 AG005146, R01 AG020688; Massachusetts General Hospital, P50 AG005134; Mayo Clinic, P50 AG016574; Mount Sinai School of Medicine, P50 AG005138, P01 AG002219; New York University, P30 AG08051, MO1RR00096, UL1 RR029893, 5R01AG012101, 5R01AG022374, 5R01AG013616, 1RC2AG036502, 1R01AG035137; Northwestern University, P30 AG013854; Oregon Health & Science University, P30 AG008017, R01 AG026916; Rush University, P30 AG010161, R01 AG019085, R01 AG15819, R01 AG17917, R01 AG30146; TGen, R01 NS059873; University of Alabama at Birmingham, P50 AG016582, UL1RR02777; University of Arizona, R01 AG031581; University of California, Davis, P30 AG010129; University of California, Irvine, P50 AG016573, P50, P50 AG016575, P50 AG016576, P50 AG016577; University of California, Los Angeles, P50 AG016570; University of California, San Diego, P50 AG005131; University of California, San Francisco, P50 AG023501, P01 AG019724; University of Kentucky, P30 AG028383, AG05144; University of Michigan, P50 AG008671; University of Pennsylvania, P30 AG010124; University of Pittsburgh, P50 AG005133, AG030653; University of Southern California, P50 AG005142; University of Texas Southwestern, P30 AG012300; University of Miami, R01 AG027944, AG010491, AG027944, AG021547, AG019757; University of Washington, P50 AG005136; Vanderbilt University, R01 AG019085; and Washington University, P50 AG005681, P01 AG03991. The Kathleen Price Bryan Brain Bank at Duke University Medical Center is funded by NINDS grant NS39764, NIMH MH60451, and by GlaxoSmithKline. Genotyping of the TGEN2 cohort was supported by Kronos Science. The TGen series was also funded by NIA grant AG041232, the Banner Alzheimer's Foundation, The Johnnie B. Byrd Sr. Alzheimer's Institute, the Medical Research Council, and the state of Arizona and also includes samples from the following sites: Newcastle Brain Tissue Resource (funding via the Medical Research Council [MRC], local NHS trusts, and Newcastle University), MRC London Brain Bank for Neurodegenerative Diseases (funding via the Medical Research Council), South West Dementia Brain Bank (funding via numerous sources including the Higher Education Funding Council for England [HEFCE], Alzheimer's Research Trust [ART], BRACE, as well as North Bristol NHS Trust Research and Innovation Department and DeNDRoN), The Netherlands Brain Bank (funding via numerous sources including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, and Universitat de Barcelona). ADNI: Funding for ADNI is through the Northern California Institute for Research and Education by grants from Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson & Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., Alzheimer's Association, Alzheimer's Drug Discovery Foundation, the Dana Foundation, and the National Institute of Biomedical Imaging and Bioengineering and NIA grants U01 AG024904, RC2 AG036535, and K01 AG030514. Support was also provided by the Alzheimer's Association (LAF, IIRG-08-89720; MAP-V, IIRG-05-14147) and the US Department of Veterans Affairs Administration, Office of Research and Development, Biomedical Laboratory Research Program. SiGN: Stroke Genetic Network (SiGN) was supported in part by award nos. U01NS069208 and R01NS100178 from NINDS. Genetics of Early-Onset Stroke (GEOS) Study was supported by the NIH Genes, Environment and Health Initiative (GEI) grant U01 HG004436, as part of the GENEVA consortium under GEI, with additional support provided by the Mid-Atlantic Nutrition and Obesity Research Center (P30 DK072488); and the Office of Research and Development, Medical Research Service, and the Baltimore Geriatrics Research, Education, and Clinical Center of the Department of Veterans Affairs. Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to Johns Hopkins University (contract no. HHSN268200782096C). Assistance with data cleaning was provided by the GENEVA Coordinating Center (U01 HG 004446; PI Bruce S. Weir). Study recruitment and assembly of datasets were supported by a Cooperative Agreement with the Division of Adult and Community Health, Centers for Disease Control and Prevention, and by grants from NINDS and the NIH Office of Research on Women's Health (R01 NS45012, U01 NS069208-01). METASTROKE: ASGC: Australian population control data were derived from the Hunter Community Study. This research was funded by grants from the Australian National and Medical Health Research Council (NHMRC Project Grant ID: 569257), the Australian National Heart Foundation (NHF Project Grant ID: G 04S 1623), the University of Newcastle, the Gladys M Brawn Fellowship scheme, and the Vincent Fairfax Family Foundation in Australia. E.G.H. was supported by a Fellowship from the NHF and National Stroke Foundation of Australia (ID: 100071). J.M. was supported by an Australian Postgraduate Award. BRAINS: Bio-Repository of DNA in Stroke (BRAINS) is partly funded by a Senior Fellowship from the Department of Health (UK) to P.S., the Henry Smith Charity, and the UK-India Education Research Institutive (UKIERI) from the British Council. GEOS: Genetics of Early Onset Stroke (GEOS) Study, Baltimore, was supported by GEI Grant U01 HG004436, as part of the GENEVA consortium under GEI, with additional support provided by the Mid-Atlantic Nutrition and Obesity Research Center (P30 DK072488), and the Office of Research and Development, Medical Research Service, and the Baltimore Geriatrics Research, Education, and Clinical Center of the Department of Veterans Affairs. Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to the Johns Hopkins University (contract no. HHSN268200782096C). Assistance with data cleaning was provided by the GENEVA Coordinating Center (U01 HG 004446; PI Bruce S. Weir). Study recruitment and assembly of datasets were supported by a Cooperative Agreement with the Division of Adult and Community Health, Centers for Disease Control and Prevention, and by grants from NINDS and the NIH Office of Research on Women's Health (R01 NS45012, U01 NS069208-01). HPS: Heart Protection Study (HPS) (ISRCTN48489393) was supported by the UK MRC, British Heart Foundation, Merck and Co. (manufacturers of simvastatin), and Roche Vitamins Ltd. (manufacturers of vitamins). Genotyping was supported by a grant to Oxford University and CNG from Merck and Co. J.C.H. acknowledges support from the British Heart Foundation (FS/14/55/30806). ISGS: Ischemic Stroke Genetics Study (ISGS)/Siblings With Ischemic Stroke Study (SWISS) was supported in part by the Intramural Research Program of the NIA, NIH project Z01 AG-000954-06. ISGS/SWISS used samples and clinical data from the NIH-NINDS Human Genetics Resource Center DNA and Cell Line Repository (ccr.coriell.org/ninds), human subjects protocol nos. 2003-081 and 2004-147. ISGS/SWISS used stroke-free participants from the Baltimore Longitudinal Study of Aging (BLSA) as controls. The inclusion of BLSA samples was supported in part by the Intramural Research Program of the NIA, NIH project Z01 AG-000015-50, human subjects protocol no. 2003-078. The ISGS study was funded by NIH-NINDS Grant R01 NS-42733 (J.F.M.). The SWISS study was funded by NIH-NINDS Grant R01 NS-39987 (J.F.M.). This study used the high-performance computational capabilities of the Biowulf Linux cluster at the NIH (biowulf.nih.gov). MGH-GASROS: MGH Genes Affecting Stroke Risk and Outcome Study (MGH-GASROS) was supported by NINDS (U01 NS069208), the American Heart Association/Bugher Foundation Centers for Stroke Prevention Research 0775010N, the NIH and NHLBI's STAMPEED genomics research program (R01 HL087676), and a grant from the National Center for Research Resources. The Broad Institute Center for Genotyping and Analysis is supported by grant U54 RR020278 from the National Center for Research resources. Milan: Milano–Besta Stroke Register Collection and genotyping of the Milan cases within CEDIR were supported by the Italian Ministry of Health (grant nos.: RC 2007/LR6, RC 2008/LR6; RC 2009/LR8; RC 2010/LR8; GR-2011-02347041), FP6 LSHM-CT-2007-037273 for the PROCARDIS control samples. WTCCC2: Wellcome Trust Case-Control Consortium 2 (WTCCC2) was principally funded by the Wellcome Trust, as part of the Wellcome Trust Case Control Consortium 2 project (085475/B/08/Z and 085475/Z/08/Z and WT084724MA). The Stroke Association provided additional support for collection of some of the St George's, London cases. The Oxford cases were collected as part of the Oxford Vascular Study, which is funded by the MRC, Stroke Association, Dunhill Medical Trust, National Institute of Health Research (NIHR), and the NIHR Biomedical Research Centre, Oxford. The Edinburgh Stroke Study was supported by the Wellcome Trust (clinician scientist award to C.L.M.S.) and the Binks Trust. Sample processing occurred in the Genetics Core Laboratory of the Wellcome Trust Clinical Research Facility, Western General Hospital, Edinburgh. Much of the neuroimaging occurred in the Scottish Funding Council Brain Imaging Research Centre (https://www.ed.ac.uk/clinical-sciences/edinburgh-imaging), Division of Clinical Neurosciences, University of Edinburgh, a core area of the Wellcome Trust Clinical Research Facility, and part of the SINAPSE (Scottish Imaging Network: A Platform for Scientific Excellence) collaboration (sinapse.ac.uk), funded by the Scottish Funding Council and the Chief Scientist Office. Collection of the Munich cases and data analysis was supported by the Vascular Dementia Research Foundation. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements no. 666881, SVDs@target (to M.D.) and no. 667375, CoSTREAM (to M.D.); the DFG as part of the Munich Cluster for Systems Neurology (EXC 1010 SyNergy) and the CRC 1123 (B3) (to M.D.); the Corona Foundation (to M.D.); the Fondation Leducq (Transatlantic Network of Excellence on the Pathogenesis of Small Vessel Disease of the Brain) (to M.D.); the e:Med program (e:AtheroSysMed) (to M.D.) and the FP7/2007-2103 European Union project CVgenes@target (grant agreement no. Health-F2-2013-601456) (to M.D.). M.F. and A.H. acknowledge support from the BHF Centre of Research Excellence in Oxford and the Wellcome Trust core award (090532/Z/09/Z). VISP: The GWAS component of the Vitamin Intervention for Stroke Prevention (VISP) study was supported by the US National Human Genome Research Institute (NHGRI), grant U01 HG005160 (PI Michèle Sale and Bradford Worrall), as part of the Genomics and Randomized Trials Network (GARNET). Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to Johns Hopkins University. Assistance with data cleaning was provided by the GARNET Coordinating Center (U01 HG005157; PI Bruce S. Weir). Study recruitment and collection of datasets for the VISP clinical trial were supported by an investigator-initiated research grant (R01 NS34447; PI James Toole) from the US Public Health Service, NINDS, Bethesda, MD. Control data obtained through the database of genotypes and phenotypes (dbGAP) maintained and supported by the United States National Center for Biotechnology Information, US National Library of Medicine. WHI: Funding support for WHI-GARNET was provided through the NHGRI GARNET (grant no. U01 HG005152). Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GARNET Coordinating Center (U01 HG005157). Funding support for genotyping, which was performed at the Broad Institute of MIT and Harvard, was provided by the GEI (U01 HG004424). R.L. is a senior clinical investigator of FWO Flanders. F.W.A. is supported by a Dekker scholarship-Junior Staff Member 2014T001–Netherlands Heart Foundation and UCL Hospitals NIHR Biomedical Research Centre.