The striatal dopamine transporter in first-episode, drug-naive schizophrenic patients: evaluation by the new SPECT-ligand[99mTc]TRODAT-1

Following the current hypothesis that acute schizophrenic psychotic illness is associated with a triatal ‘hyperdopaminergic state’, presynaptic integrity and dopamine transporter (DAT) density in first-episode, neuroleptic-naive schizophrenic patients was measured by single-photonemission- tomography (SPECT) and compared with that in healthy control subjects. A new SPECT-ligand for assessment of the striatal DAT, the Technetium-99m-labelled tropane TRODAT-1 ([99mTc]TRODAT-1), was used. Ten inpatients suffering from a first acute schizophrenic episode and 10 age- and sex-matched healthy control subjects underwent SPECT with [99mTc]TRODAT-1. On the day of SPECT, psychopathological ratings were performed with the Brief Psychiatric Rating Scale (BPRS), the Positive and Negative Syndrome Scale (PANSS) and Schedule for Assessment of Negative Symptoms (SANS). Patients had not previously received any neuroleptic or antidepressant medication. Mean specific TRODAT-1 binding in the striatum did not differ significantly between the patient and the age- and sex-matched control group (1.25 vs. 1.28). Variance was significantly higher in the patient group. The data obtained with the new ligand in first-episode, drug-naive schizophrenic patients are in line with the PET results from the group of Laakso et al. in a comparable patient sample. [99mTc]TRODAT-1 seems to be a valuable new SPECTligand in the evaluation of the presynaptic site of the striatal dopaminergic synapse in schizophrenia

T1 and FLAIR signal intensities are related to tau pathology in dominantly inherited Alzheimer disease

Carriers of mutations responsible for dominantly inherited Alzheimer disease provide a unique opportunity to study potential imaging biomarkers. Biomarkers based on routinely acquired clinical MR images, could supplement the extant invasive or logistically challenging) biomarker studies. We used 1104 longitudinal MR, 324 amyloid beta, and 87 tau positron emission tomography imaging sessions from 525 participants enrolled in the Dominantly Inherited Alzheimer Network Observational Study to extract novel imaging metrics representing the mean (μ) and standard deviation (σ) of standardized image intensities of T1-weighted and Fluid attenuated inversion recovery (FLAIR) MR scans. There was an exponential decrease in FLAIR-μ in mutation carriers and an increase in FLAIR and T1 signal heterogeneity (T1-σ and FLAIR-σ) as participants approached the symptom onset in both supramarginal, the right postcentral and right superior temporal gyri as well as both caudate nuclei, putamina, thalami, and amygdalae. After controlling for the effect of regional atrophy, FLAIR-μ decreased and T1-σ and FLAIR-σ increased with increasing amyloid beta and tau deposition in numerous cortical regions. In symptomatic mutation carriers and independent of the effect of regional atrophy, tau pathology demonstrated a stronger relationship with image intensity metrics, compared with amyloid pathology. We propose novel MR imaging intensity-based metrics using standard clinical T1 and FLAIR images which strongly associates with the progression of pathology in dominantly inherited Alzheimer disease. We suggest that tau pathology may be a key driver of the observed changes in this cohort of patients

A robust coregistration method for in vivo studies using a first generation simultaneous PET/MR scanner

Purpose: Hybrid positron emission tomography (PET)/magnetic resonance (MR) imaging systems have recently been built that allow functional and anatomical information obtained from PET and MR to be acquired simultaneously. The authors have developed a robust coregistration scheme for a first generation small animal PET/MR imaging system and illustrated the potential of this system to study intratumoral heterogeneity in a mouse model. Methods: An alignment strategy to fuse simultaneously acquired PET and MR data, using the MR imaging gradient coordinate system as the reference basis, was developed. The fidelity of the alignment was evaluated over multiple study sessions. In order to explore its robustness in vivo, the alignment strategy was applied to explore the heterogeneity of glucose metabolism in a xenograft tumor model, using ^(18)F-FDG-PET to guide the acquisition of localized ^1H MR spectra within a single imaging session. Results: The alignment method consistently fused the PET/MR data sets with subvoxel accuracy (registration error mean=0.55 voxels, <0.28 mm); this was independent of location within the field of view. When the system was used to study intratumoral heterogeneity within xenograft tumors, a correlation of high ^(18)F-FDG-PET signal with high choline/creatine ratio was observed. Conclusions: The authors present an implementation of an efficient and robust coregistration scheme for multimodal noninvasive imaging using PET and MR. This setup allows time-sensitive, multimodal studies of physiology to be conducted in an efficient manner

Comparing cortical signatures of atrophy between late-onset and autosomal dominant Alzheimer disease

Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals. We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (nADRC = 381; nDIAN = 145), preclinical (nADRC = 153; nDIAN = 76), and cognitively impaired (nADRC = 54; nDIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either 11C-Pittsburgh compound B or 18F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.Fil: Dincer, Aylin. Washington University in St. Louis; Estados UnidosFil: Gordon, Brian A.. Washington University in St. Louis; Estados UnidosFil: Hari-Raj, Amrita. Ohio State University; Estados UnidosFil: Keefe, Sarah J.. Washington University in St. Louis; Estados UnidosFil: Flores, Shaney. Washington University in St. Louis; Estados UnidosFil: McKay, Nicole S.. Washington University in St. Louis; Estados UnidosFil: Paulick, Angela M.. Washington University in St. Louis; Estados UnidosFil: Shady Lewis, Kristine E.. University of Kentucky; Estados UnidosFil: Feldman, Rebecca L.. Washington University in St. Louis; Estados UnidosFil: Hornbeck, Russ C.. Washington University in St. Louis; Estados UnidosFil: Allegri, Ricardo Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Ances, Beau M.. Washington University in St. Louis; Estados UnidosFil: Berman, Sarah B.. University of Pittsburgh; Estados UnidosFil: Brickman, Adam M.. Columbia University; Estados UnidosFil: Brooks, William S.. Neuroscience Research Australia; Australia. University of New South Wales; AustraliaFil: Cash, David M.. UCL Queen Square Institute of Neurology; Reino UnidoFil: Chhatwal, Jasmeer P.. Harvard Medical School; Estados UnidosFil: Farlow, Martin R.. Indiana University; Estados UnidosFil: Fougère, Christian la. German Center for Neurodegenerative Diseases; Alemania. University Hospital of Tübingen; AlemaniaFil: Fox, Nick C.. UCL Queen Square Institute of Neurology; Reino UnidoFil: Fulham, Michael J.. Royal Prince Alfred Hospital; Australia. University of Sydney; AustraliaFil: Jack, Clifford R.. Mayo Clinic; Estados UnidosFil: Joseph-Mathurin, Nelly. Washington University in St. Louis; Estados UnidosFil: Karch, Celeste M.. Washington University in St. Louis; Estados UnidosFil: Lee, Athene. University Brown; Estados UnidosFil: Levin, Johannes. German Center for Neurodegenerative Diseases; Alemania. Ludwig Maximilians Universitat; Alemania. Munich Cluster for Systems Neurology; AlemaniaFil: Masters, Colin L.. University of Melbourne; AustraliaFil: McDade, Eric M.. Washington University in St. Louis; Estados UnidosFil: Oh, Hwamee. University Brown; Estados UnidosFil: Perrin, Richard J.. Washington University in St. Louis; Estados Unido

Amino-acid PET versus MRI guided re-irradiation in patients with recurrent glioblastoma multiforme (GLIAA) – protocol of a randomized phase II trial (NOA 10/ARO 2013-1)

Background: The higher specificity of amino-acid positron emission tomography (AA-PET) in the diagnosis of gliomas, as well as in the differentiation between recurrence and treatment-related alterations, in comparison to contrast enhancement in T1-weighted MRI was demonstrated in many studies and is the rationale for their implementation into radiation oncology treatment planning. Several clinical trials have demonstrated the significant differences between AA-PET and standard MRI concerning the definition of the gross tumor volume (GTV). A small single-center non-randomized prospective study in patients with recurrent high grade gliomas treated with stereotactic fractionated radiotherapy (SFRT) showed a significant improvement in survival when AA-PET was integrated in target volume delineation, in comparison to patients treated based on CT/MRI alone. Methods: This protocol describes a prospective, open label, randomized, multi-center phase II trial designed to test if radiotherapy target volume delineation based on FET-PET leads to improvement in progression free survival (PFS) in patients with recurrent glioblastoma (GBM) treated with re-irradiation, compared to target volume delineation based on T1Gd-MRI. The target sample size is 200 randomized patients with a 1:1 allocation ratio to both arms. The primary endpoint (PFS) is determined by serial MRI scans, supplemented by AA-PET-scans and/or biopsy/surgery if suspicious of progression. Secondary endpoints include overall survival (OS), locally controlled survival (time to local progression or death), volumetric assessment of GTV delineated by either method, topography of progression in relation to MRIor PET-derived target volumes, rate of long term survivors (> 1 year), localization of necrosis after re-irradiation, quality of life (QoL) assessed by the EORTC QLQ-C15 PAL questionnaire, evaluation of safety of FET-application in AA-PET imaging and toxicity of re-irradiation. Discussion: This is a protocol of a randomized phase II trial designed to test a new strategy of radiotherapy target volume delineation for improving the outcome of patients with recurrent GBM. Moreover, the trial will help to develop a standardized methodology for the integration of AA-PET and other imaging biomarkers in radiation treatment planning. Trial registration: The GLIAA trial is registered with ClinicalTrials.gov (NCT01252459, registration date 02.12.2010), German Clinical Trials Registry (DRKS00000634, registration date 10.10.2014), and European Clinical Trials Database (EudraCT-No. 2012-001121-27, registration date 27.02.2012)

Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer's disease

Introduction: Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment. Methods: We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally. Results: Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%–98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion: Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials.Fil: Keret, Ophir. University of California; Estados UnidosFil: Staffaroni, Adam M.. University of California; Estados UnidosFil: Ringman, John M.. University of Southern California; Estados UnidosFil: Cobigo, Yann. University of California; Estados UnidosFil: Goh, Sheng Yang M.. University of California; Estados UnidosFil: Wolf, Amy. University of California; Estados UnidosFil: Allen, Isabel Elaine. University of California; Estados UnidosFil: Salloway, Stephen. Brown University; Estados UnidosFil: Chhatwal, Jasmeer. Harvard Medical School; Estados UnidosFil: Brickman, Adam M.. Columbia University; Estados UnidosFil: Reyes Dumeyer, Dolly. Columbia University; Estados UnidosFil: Bateman, Randal J.. University of Washington; Estados UnidosFil: Benzinger, Tammie L.S.. University of Washington; Estados UnidosFil: Morris, John C.. University of Washington; Estados UnidosFil: Ances, Beau M.. University of Washington; Estados UnidosFil: Joseph Mathurin, Nelly. University of Washington; Estados UnidosFil: Perrin, Richard J.. University of Washington; Estados UnidosFil: Gordon, Brian A.. University of Washington; Estados UnidosFil: Levin, Johannes. German Center for Neurodegenerative Diseases; Alemania. Ludwig Maximilians Universitat; AlemaniaFil: Vöglein, Jonathan. Ludwig Maximilians Universitat; Alemania. German Center for Neurodegenerative Diseases; AlemaniaFil: Jucker, Mathias. German Center for Neurodegenerative Diseases; Alemania. Eberhard Karls Universität Tübingen; AlemaniaFil: la Fougère, Christian. Eberhard Karls Universität Tübingen; Alemania. German Center for Neurodegenerative Diseases; AlemaniaFil: Martins, Ralph N.. Cooperative Research Centres Australia; Australia. University of Western Australia; Australia. Edith Cowan University; Australia. Australian Alzheimer's Research Foundation; Australia. Macquarie University; AustraliaFil: Sohrabi, Hamid R.. University of Western Australia; Australia. Macquarie University; Australia. Australian Alzheimer's Research Foundation; Australia. Cooperative Research Centres Australia; Australia. Edith Cowan University; AustraliaFil: Taddei, Kevin. Australian Alzheimer's Research Foundation; Australia. Edith Cowan University; AustraliaFil: Villemagne, Victor L.. Austin Health; AustraliaFil: Schofield, Peter R.. Neuroscience Research Australia; Australia. Unsw Medicine; AustraliaFil: Brooks, William S.. Neuroscience Research Australia; Australia. Unsw Medicine; AustraliaFil: Fulham, Michael. Royal Prince Alfred Hospital; AustraliaFil: Masters, Colin L.. University of Melbourne; AustraliaFil: Allegri, Ricardo Francisco. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Instituto de Neurociencias - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Neurociencias; Argentin

Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer's disease

Introduction: Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment. Methods: We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally. Results: Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%–98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion: Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials

Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN)

The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual's point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of 'sporadic' AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers

Positron emission tomography and magnetic resonance imaging methods and datasets within the dominantly inherited Alzheimer network (DIAN)

The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case–control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual’s point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of ‘sporadic’ AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers