Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; ), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.

Although beta-amyloid (Abeta) plaques are a primary diagnostic criterion for Alzheimer's disease, this pathology is commonly observed in the brains of non-demented older individuals. To explore the importance of this pathology in the absence of dementia, we compared levels of amyloid deposition (via 'Pittsburgh Compound-B' (PIB) positron emission tomography (PET) imaging) to hippocampus volume (HV) and episodic memory (EM) in three groups: (i) normal controls (NC) from the Berkeley Aging Cohort (BAC NC, n = 20); (ii) normal controls (NC) from the Alzheimer's disease neuroimaging initiative (ADNI NC, n = 17); and (iii) PIB+ mild cognitive impairment subjects from the ADNI (ADNI PIB+ MCI, n = 39). Age, gender and education were controlled for in each statistical model, and HV was adjusted for intracranial volume (aHV). In BAC NC, elevated PIB uptake was significantly associated with smaller aHV (P = 0.0016) and worse EM (P = 0.0086). Within ADNI NC, elevated PIB uptake was significantly associated with smaller aHV (P = 0.047) but not EM (P = 0.60); within ADNI PIB+ MCI, elevated PIB uptake was significantly associated with both smaller aHV (P = 0.00070) and worse EM (P = 0.046). To further understand these relationships, a recursive regression procedure was conducted within all ADNI NC and PIB+ MCI subjects (n = 56) to test the hypothesis that HV mediates the relationship between Abeta and EM. Significant correlations were found between PIB index and EM (P = 0.0044), PIB index and aHV (P < 0.0001), as well as between aHV and EM (P < 0.0001). When both aHV and PIB were included in the same model to predict EM, aHV remained significant (P = 0.0015) whereas PIB index was no longer significantly associated with EM (P = 0.50). These results are consistent with a model in which Abeta deposition, hippocampal atrophy, and EM occur sequentially in elderly subjects, with Abeta deposition as the primary event in this cascade. This pattern suggests that declining EM in older individuals may be caused by Abeta-induced hippocampus atrophy.

The Functional Activities Questionnaire (FAQ) and Alzheimer’s Disease Assessment Scale – cognitive subscale (ADAS-cog) are frequently-used indices of cognitive decline in Alzheimer’s disease (AD). The goal of this study was to compare FDG-PET and clinical measurements in a large sample of elderly subjects with memory disturbance. We examined relationships between glucose metabolism in FDG-PET regions of interest (FDG-ROIs), and ADAS-cog and FAQ scores in AD and mild cognitive impairment (MCI) patients enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Low glucose metabolism at baseline predicted subsequent ADAS-cog and FAQ decline. In addition, longitudinal glucose metabolism decline was associated with concurrent ADAS-cog and FAQ decline. Additionally, a power analysis revealed that FDG-ROI values have greater statistical power than ADAS-cog to detect attenuation of cognitive decline in AD and MCI patients. Glucose metabolism is a sensitive measure of change in cognition and functional ability in AD and MCI, and has value in predicting future cognitive decline.

Amyloid-β, a hallmark of Alzheimer's disease, begins accumulating up to two decades before the onset of dementia, and can be detected in vivo applying amyloid-β positron emission tomography tracers such as carbon-11-labelled Pittsburgh compound-B. A variety of thresholds have been applied in the literature to define Pittsburgh compound-B positron emission tomography positivity, but the ability of these thresholds to detect early amyloid-β deposition is unknown, and validation studies comparing Pittsburgh compound-B thresholds to post-mortem amyloid burden are lacking. In this study we first derived thresholds for amyloid positron emission tomography positivity using Pittsburgh compound-B positron emission tomography in 154 cognitively normal older adults with four complementary approaches: (i) reference values from a young control group aged between 20 and 30 years; (ii) a Gaussian mixture model that assigned each subject a probability of being amyloid-β-positive or amyloid-β-negative based on Pittsburgh compound-B index uptake; (iii) a k-means cluster approach that clustered subjects into amyloid-β-positive or amyloid-β-negative based on Pittsburgh compound-B uptake in different brain regions (features); and (iv) an iterative voxel-based analysis that further explored the spatial pattern of early amyloid-β positron emission tomography signal. Next, we tested the sensitivity and specificity of the derived thresholds in 50 individuals who underwent Pittsburgh compound-B positron emission tomography during life and brain autopsy (mean time positron emission tomography to autopsy 3.1 ± 1.8 years). Amyloid at autopsy was classified using Consortium to Establish a Registry for Alzheimer's Disease (CERAD) criteria, unadjusted for age. The analytic approaches yielded low thresholds (standard uptake value ratiolow = 1.21, distribution volume ratiolow = 1.08) that represent the earliest detectable Pittsburgh compound-B signal, as well as high thresholds (standard uptake value ratiohigh = 1.40, distribution volume ratiohigh = 1.20) that are more conservative in defining Pittsburgh compound-B positron emission tomography positivity. In voxel-wise contrasts, elevated Pittsburgh compound-B retention was first noted in the medial frontal cortex, then the precuneus, lateral frontal and parietal lobes, and finally the lateral temporal lobe. When compared to post-mortem amyloid burden, low proposed thresholds were more sensitive than high thresholds (sensitivities: distribution volume ratiolow 81.0%, standard uptake value ratiolow 83.3%; distribution volume ratiohigh 61.9%, standard uptake value ratiohigh 62.5%) for CERAD moderate-to-frequent neuritic plaques, with similar specificity (distribution volume ratiolow 95.8%; standard uptake value ratiolow, distribution volume ratiohigh and standard uptake value ratiohigh 100.0%). A receiver operator characteristic analysis identified optimal distribution volume ratio (1.06) and standard uptake value ratio (1.20) thresholds that were nearly identical to the a priori distribution volum...

Although beta-amyloid (Aβ) deposition is a characteristic feature of Alzheimer's disease (AD), this pathology is commonly found in elderly normal controls (NC). The pattern of Aβ deposition as detected with Pittsburgh compound-B positron emission tomography (PIB-PET) imaging shows substantial spatial overlap with the default mode network (DMN), a group of brain regions that typically deactivates during externally driven cognitive tasks. In this study, we show that DMN functional connectivity (FC) during rest is altered with increasing levels of PIB uptake in NC. Specifically, FC decreases were identified in regions implicated in episodic memory (EM) processing (posteromedial cortex, ventral medial prefrontal cortex, and angular gyrus), whereas connectivity increases were detected in dorsal and anterior medial prefrontal and lateral temporal cortices. This pattern of decreases is consistent with previous studies that suggest heightened vulnerability of EM-related brain regions in AD, whereas the observed increases in FC may reflect a compensatory response.