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Subtypes based on cerebrospinal fluid and magnetic resonance imaging markers in normal elderly predict cognitive decline.

ABSTRACT: Cerebrospinal fluid (CSF) and structural magnetic resonance imaging (MRI) show patterns of change in Alzheimer's disease (AD) that precede dementia. The Alzheimer's Disease Neuroimaging Initiative (ADNI) studied normal controls (NC), subjects with mild cognitive impairment (MCI), and subjects with AD to identify patterns of biomarkers to aid in early diagnosis and effective treatment of AD. Two hundred twenty-two NC underwent baseline MRI and clinical examination at baseline and at least one follow-up. One hundred twelve also provided CSF at baseline. Unsupervised clustering based on initial CSF and MRI measures was used to identify clusters of participants with similar profiles. Repeated measures regression modeling assessed the relationship of individual measures, and of cluster membership, to cognitive change over 3 years. Most individuals showed little cognitive change. Individual biomarkers had limited predictive value for cognitive decline, but membership in the cluster with the most extreme profile was associated with more rapid decline in ADAS-cog. Subtypes among NC based on multiple biomarkers may represent the earliest stages of subclinical cognitive decline and AD.

SUBMITTER: Nettiksimmons J

PROVIDER: S-EPMC2902683 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Subtypes based on cerebrospinal fluid and magnetic resonance imaging markers in normal elderly predict cognitive decline.

Nettiksimmons J J Harvey D D Brewer J J Carmichael O O DeCarli C C Jack C R CR Petersen R R Shaw L M LM Trojanowski J Q JQ Weiner M W MW Beckett L L

Neurobiology of aging 20100609 8

Cerebrospinal fluid (CSF) and structural magnetic resonance imaging (MRI) show patterns of change in Alzheimer's disease (AD) that precede dementia. The Alzheimer's Disease Neuroimaging Initiative (ADNI) studied normal controls (NC), subjects with mild cognitive impairment (MCI), and subjects with AD to identify patterns of biomarkers to aid in early diagnosis and effective treatment of AD. Two hundred twenty-two NC underwent baseline MRI and clinical examination at baseline and at least one fol ...[more]

PMID: 20542598

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Project description:Disturbed clearance of toxic metabolites from the brain via cerebrospinal fluid is emerging as an important mechanism behind dementia and neurodegeneration. To this end, magnetic resonance imaging work-up of dementia diseases is largely focused on anatomical derangements of the brain. This study explores magnetic resonance imaging biomarkers of cerebrospinal fluid tracer dynamics in patients with the dementia subtype idiopathic normal pressure hydrocephalus and a cohort of reference subjects. All study participants underwent multi-phase magnetic resonance imaging up to 48 h after intrathecal administration of the contrast agent gadobutrol (0.5 ml, 1 mmol/ml), serving as cerebrospinal fluid tracer. Imaging biomarkers of cerebrospinal fluid tracer dynamics (i.e. ventricular reflux grades 0-4 and clearance) were compared with anatomical magnetic resonance imaging biomarkers of cerebrospinal fluid space anatomy (Evans' index, callosal angle and disproportional enlargement of subarachnoid spaces hydrocephalus) and neurodegeneration (Schelten's medial temporal atrophy scores, Fazeka's scores and entorhinal cortex thickness). The imaging scores were also related to a pulsatile intracranial pressure score indicative of intracranial compliance. In shunt-responsive idiopathic normal pressure hydrocephalus, the imaging biomarkers demonstrated significantly altered cerebrospinal fluid tracer dynamics (ventricular reflux grades 3-4 and reduced clearance of tracer), deranged cerebrospinal fluid space anatomy and pronounced neurodegeneration. The altered MRI biomarkers were accompanied by pressure indices of impaired intracranial compliance. In conclusion, we present novel magnetic resonance imaging biomarkers characterizing idiopathic normal pressure hydrocephalus pathophysiology, namely measures of cerebrospinal fluid molecular redistribution and clearance, which add information to traditional imaging scores of cerebrospinal fluid space anatomy and neurodegeneration.

Project description:Lobar cerebral microbleeds (CMBs) in Alzheimer's disease (AD) are associated with cerebral amyloid angiopathy (CAA) due to vascular amyloid beta (Aβ) deposits. However, the relationship between lobar CMBs and clinical subtypes of AD remains unknown. Here, we enrolled patients with early- and late-onset amnestic dominant AD, logopenic variant of primary progressive aphasia (lvPPA) and posterior cortical atrophy (PCA) who were compatible with the AD criteria. We then examined the levels of cerebrospinal fluid (CSF) biomarkers [Aβ1-42, Aβ1-40, Aβ1-38, phosphorylated tau 181 (P-Tau), total tau (T-Tau), neurofilament light chain (NFL), and chitinase 3-like 1 protein (YKL-40)], analyzed the number and localization of CMBs, and measured the cerebral blood flow (CBF) volume by 99mTc-ethyl cysteinate dimer single photon emission computerized tomography (99mTc ECD-SPECT), as well as the mean cortical standard uptake value ratio by 11C-labeled Pittsburgh Compound B-positron emission tomography (11C PiB-PET). Lobar CMBs in lvPPA were distributed in the temporal, frontal, and parietal lobes with the left side predominance, while the CBF volume in lvPPA significantly decreased in the left temporal area, where the number of lobar CMBs and the CBF volumes showed a significant inversely correlation. The CSF levels of NFL in lvPPA were significantly higher compared to the other AD subtypes and non-demented subjects. The numbers of lobar CMBs significantly increased the CSF levels of NFL in the total AD patients, additionally, among AD subtypes, the CSF levels of NFL in lvPPA predominantly were higher by increasing number of lobar CMBs. On the other hand, the CSF levels of Aβ1-38, Aβ1-40, Aβ1-42, P-Tau, and T-Tau were lower by increasing number of lobar CMBs in the total AD patients. These findings may suggest that aberrant brain hypoperfusion in lvPPA was derived from the brain atrophy due to neurodegeneration, and possibly may involve the aberrant microcirculation causing by lobar CMBs and cerebrovascular injuries, with the left side dominance, consequently leading to a clinical phenotype of logopenic variant.

Project description:Importance:Which children with fetal ventriculomegaly, or enlargement of the cerebral ventricles in utero, will develop hydrocephalus requiring treatment after birth is unclear. Objective:To determine whether extraction of multiple imaging features from fetal magnetic resonance imaging (MRI) and integration using machine learning techniques can predict which patients require postnatal cerebrospinal fluid (CSF) diversion after birth. Design, Setting, and Patients:This retrospective case-control study used an institutional database of 253 patients with fetal ventriculomegaly from January 1, 2008, through December 31, 2014, to generate a predictive model. Data were analyzed from January 1, 2008, through December 31, 2015. All 25 patients who required postnatal CSF diversion were selected and matched by gestational age with 25 patients with fetal ventriculomegaly who did not require CSF diversion (discovery cohort). The model was applied to a sample of 24 consecutive patients with fetal ventriculomegaly who underwent evaluation at a separate institution (replication cohort) from January 1, 1998, through December 31, 2007. Data were analyzed from January 1, 1998, through December 31, 2009. Exposures:To generate the model, linear measurements, area, volume, and morphologic features were extracted from the fetal MRI, and a machine learning algorithm analyzed multiple features simultaneously to find the combination that was most predictive of the need for postnatal CSF diversion. Main Outcomes and Measures:Accuracy, sensitivity, and specificity of the model in correctly classifying patients requiring postnatal CSF diversion. Results:A total of 74 patients (41 girls [55%] and 33 boys [45%]; mean [SD] gestational age, 27.0 [5.6] months) were included from both cohorts. In the discovery cohort, median time to CSF diversion was 6 days (interquartile range [IQR], 2-51 days), and patients with fetal ventriculomegaly who did not develop symptoms were followed up for a median of 29 months (IQR, 9-46 months). The model correctly classified patients who required CSF diversion with 82% accuracy, 80% sensitivity, and 84% specificity. In the replication cohort, the model achieved 91% accuracy, 75% sensitivity, and 95% specificity. Conclusion and Relevance:Image analysis and machine learning can be applied to fetal MRI findings to predict the need for postnatal CSF diversion. The model provides prognostic information that may guide clinical management and select candidates for potential fetal surgical intervention.

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Subtypes based on cerebrospinal fluid and magnetic resonance imaging markers in normal elderly predict cognitive decline.

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Subtypes based on cerebrospinal fluid and magnetic resonance imaging markers in normal elderly predict cognitive decline.

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