Methods for decoding cortical gradients of functional connectivity.

Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network and representative of abstract functioning at the other. The functional significance and interpretation of macroscale gradients remains a central topic of discussion in the neuroimaging community, with some studies demonstrating that gradients may be described using meta-analytic functional decoding techniques. However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance. Here, we conducted a comprehensive series of analyses to investigate and improve the framework of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient segmentation and functional decoding. We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding functional connectivity gradients. Finally, we proposed a method for decoding additional components of the gradient decomposition. The current work aims to provide recommendations on best practices and flexible methods for gradient-based functional decoding of fMRI data.

Neuroscout, a unified platform for generalizable and reproducible fMRI research.

Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of fMRI studies using naturalistic stimuli-such as movies and narratives-allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.

Hippocampal acidity and volume are differentially associated with spatial navigation in older adults.

The hippocampus is negatively affected by aging and is critical for spatial navigation. While there is evidence that wayfinding navigation tasks are especially sensitive to preclinical hippocampal deterioration, these studies have primarily used volumetric hippocampal imaging without considering microstructural properties or anatomical variation within the hippocampus. T1ρ is an MRI measure sensitive to regional pH, with longer relaxation rates reflecting acidosis as a marker of metabolic dysfunction and neuropathological burden. For the first time, we investigate how measures of wayfinding including landmark location learning and delayed memory in cognitively normal older adults (N = 84) relate to both hippocampal volume and T1ρ in the anterior and posterior hippocampus. Regression analyses revealed hippocampal volume was bilaterally related to learning, while right lateralized T1ρ was related to delayed landmark location memory and bilateral T1ρ was related to the delayed use of a cognitive map. Overall, results suggest hippocampal volume and T1ρ relaxation rate tap into distinct mechanisms involved in preclinical cognitive decline as assessed by wayfinding navigation, and laterality influenced these relationships more than the anterior-posterior longitudinal axis of the hippocampus.

Analysis of task-based functional MRI data preprocessed with fMRIPrep.

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

Home-Based, Adaptive Cognitive Training for Cognitively Normal Older adults: Initial Efficacy Trial.

OBJECTIVES: We examined whether a home-based, adaptive cognitive training (CT) program would lead to cognitive performance changes on a neuropsychological test battery in cognitively normal older adults. METHOD: Sixty-eight older adults (age = 70.0, SD = 3.74) were randomly assigned to either CT or an active control group (AC, casual computer games). Participants were instructed to train on their assigned programs for 42 min per day, 5 days per week, over 10 weeks (35 hr of total program usage). Participants completed tests of processing speed, working memory, and executive control before and after 10 weeks of training. RESULTS: Training groups did not differ in performance before training. After training, CT participants out-performed AC participants in the overall cognitive composite score, driven by processing speed and working memory domains. DISCUSSION: Our results show that a limited dose of home-based CT can drive cognitive improvements as measured with neuropsychological test battery, suggesting potential cognitive health maintenance implications for cognitively normal older adults.

fMRIPrep: a robust preprocessing pipeline for functional MRI.

Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.

Carotid ß-stiffness index is associated with slower processing speed but not working memory or white matter integrity in healthy middle-aged/older adults.

Aging is associated with increased carotid artery stiffness, a predictor of incident stroke, and reduced cognitive performance and brain white matter integrity (WMI) in humans. Therefore, we hypothesized that higher carotid stiffness/lower compliance would be independently associated with slower processing speed, higher working memory cost, and lower WMI in healthy middle-aged/older (MA/O) adults. Carotid ß-stiffness (P < 0.001) was greater and compliance (P < 0.001) was lower in MA/O (n = 32; 64.4 ± 4.3 yr) vs. young (n = 19; 23.8 ± 2.9 yr) adults. MA/O adults demonstrated slower processing speed (27.4 ± 4.6 vs. 35.4 ± 5.0 U/60 s, P < 0.001) and higher working memory cost (-15.4 ± 0.14 vs. -2.2 ± 0.05%, P < 0.001) vs. young adults. Global WMI was lower in MA/O adults (P < 0.001) and regionally in the frontal lobe (P = 0.020) and genu (P = 0.009). In the entire cohort, multiple regression analysis that included education, sex, and body mass index, carotid ß-stiffness index (B = -0.53 ± 0.15 U, P = 0.001) and age group (B = -4.61 ± 1.7, P = 0.012, adjusted R2 = 0.4) predicted processing speed but not working memory cost or WMI. Among MA/O adults, higher ß-stiffness (B = -0.60 ± 0.18, P = 0.002) and lower compliance (B = 0.93 ± 0.26, P = 0.002) were associated with slower processing speed but not working memory cost or WMI. These data suggest that greater carotid artery stiffness is independently and selectively associated with slower processing speed but not working memory among MA/O adults. Carotid artery stiffening may modulate reductions in processing speed earlier than working memory with healthy aging in humans.NEW & NOTEWORTHY Previously, studies investigating the relation between large elastic artery stiffness, cognition, and brain structure have focused mainly on aortic stiffness in aged individuals with cardiovascular disease risk factors and other comorbidities. This study adds to the field by demonstrating that the age-related increases in carotid artery stiffness, but not aortic stiffness, is independently and selectively associated with slower processing speed but not working memory among middle-aged/older adults with low cardiovascular disease risk factor burden.