Revisiting the seven pillars of RDoC.

BACKGROUND: In 2013, a few years after the launch of the National Institute of Mental Health's Research Domain Criteria (RDoC) initiative, Cuthbert and Insel published a paper titled "Toward the future of psychiatric diagnosis: the seven pillars of RDoC." The RDoC project is a translational research effort to encourage new ways of studying psychopathology through a focus on disruptions in normal functions (such as reward learning or attention) that are defined jointly by observable behavior and neurobiological measures. The paper outlined the principles of the RDoC research framework, including emphases on research that acquires data from multiple measurement classes to foster integrative analyses, adopts dimensional approaches, and employs novel methods for ascertaining participants and identifying valid subgroups. DISCUSSION: To mark the first decade of the RDoC initiative, we revisit the seven pillars and highlight new research findings and updates to the framework that are related to each. This reappraisal emphasizes the flexible nature of the RDoC framework and its application in diverse areas of research, new findings related to the importance of developmental trajectories within and across neurobehavioral domains, and the value of computational approaches for clarifying complex multivariate relations among behavioral and neurobiological systems. CONCLUSION: The seven pillars of RDoC have provided a foundation that has helped to guide a surge of new studies that have examined neurobehavioral domains related to mental disorders, in the service of informing future psychiatric nosology. Building on this footing, future areas of emphasis for the RDoC project will include studying central-peripheral interactions, developing novel approaches to phenotyping for genomic studies, and identifying new targets for clinical trial research to facilitate progress in precision psychiatry.

The cortical signature of Alzheimer's disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals.

Alzheimer's disease (AD) is associated with neurodegeneration in vulnerable limbic and heteromodal regions of the cerebral cortex, detectable in vivo using magnetic resonance imaging. It is not clear whether abnormalities of cortical anatomy in AD can be reliably measured across different subject samples, how closely they track symptoms, and whether they are detectable prior to symptoms. An exploratory map of cortical thinning in mild AD was used to define regions of interest that were applied in a hypothesis-driven fashion to other subject samples. Results demonstrate a reliably quantifiable in vivo signature of abnormal cortical anatomy in AD, which parallels known regional vulnerability to AD neuropathology. Thinning in vulnerable cortical regions relates to symptom severity even in the earliest stages of clinical symptoms. Furthermore, subtle thinning is present in asymptomatic older controls with brain amyloid binding as detected with amyloid imaging. The reliability and clinical validity of AD-related cortical thinning suggests potential utility as an imaging biomarker. This "disease signature" approach to cortical morphometry, in which disease effects are mapped across the cortical mantle and then used to define ROIs for hypothesis-driven analyses, may provide a powerful methodological framework for studies of neuropsychiatric diseases.

MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths.

Automated MRI-derived measurements of in-vivo human brain volumes provide novel insights into normal and abnormal neuroanatomy, but little is known about measurement reliability. Here we assess the impact of image acquisition variables (scan session, MRI sequence, scanner upgrade, vendor and field strengths), FreeSurfer segmentation pre-processing variables (image averaging, B1 field inhomogeneity correction) and segmentation analysis variables (probabilistic atlas) on resultant image segmentation volumes from older (n=15, mean age 69.5) and younger (both n=5, mean ages 34 and 36.5) healthy subjects. The variability between hippocampal, thalamic, caudate, putamen, lateral ventricular and total intracranial volume measures across sessions on the same scanner on different days is less than 4.3% for the older group and less than 2.3% for the younger group. Within-scanner measurements are remarkably reliable across scan sessions, being minimally affected by averaging of multiple acquisitions, B1 correction, acquisition sequence (MPRAGE vs. multi-echo-FLASH), major scanner upgrades (Sonata-Avanto, Trio-TrioTIM), and segmentation atlas (MPRAGE or multi-echo-FLASH). Volume measurements across platforms (Siemens Sonata vs. GE Signa) and field strengths (1.5 T vs. 3 T) result in a volume difference bias but with a comparable variance as that measured within-scanner, implying that multi-site studies may not necessarily require a much larger sample to detect a specific effect. These results suggest that volumes derived from automated segmentation of T1-weighted structural images are reliable measures within the same scanner platform, even after upgrades; however, combining data across platform and across field-strength introduces a bias that should be considered in the design of multi-site studies, such as clinical drug trials. The results derived from the young groups (scanner upgrade effects and B1 inhomogeneity correction effects) should be considered as preliminary and in need for further validation with a larger dataset.

Feasibility of multi-site clinical structural neuroimaging studies of aging using legacy data.

The application of advances in biomedical computing to medical imaging research is enabling scientists to conduct quantitative clinical imaging studies using data collected across multiple sites to test new hypotheses on larger cohorts, increasing the power to detect subtle effects. Given that many research groups have valuable existing (legacy) data, one goal of the Morphometry Biomedical Informatics Research Network (BIRN) Testbed is to assess the feasibility of pooled analyses of legacy structural neuroimaging data in normal aging and Alzheimer's disease. The present study examined whether such data could be meaningfully reanalyzed as a larger combined data set by using rigorous data curation, image analysis, and statistical modeling methods; in this case, to test the hypothesis that hippocampal volume decreases with age and to investigate findings of hippocampal asymmetry. This report describes our work with legacy T1-weighted magnetic resonance (MR) and demographic data related to normal aging that have been shared through the BIRN by three research sites. Results suggest that, in the present application, legacy MR data from multiple sites can be pooled to investigate questions of scientific interest. In particular, statistical analyses suggested that a mixed-effects model employing site as a random effect best fits the data, accounting for site-specific effects while taking advantage of expected comparability of age-related effects. In the combined sample from three sites, significant age-related decline of hippocampal volume and right-dominant hippocampal asymmetry were detected in healthy elderly controls. These expected findings support the feasibility of combining legacy data to investigate novel scientific questions.

Geometrically accurate topology-correction of cortical surfaces using nonseparating loops.

In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator.

Cortical surface shape analysis based on spherical wavelets.

In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, which allows us to characterize the order of development of large-scale and finer folding patterns independently. Given a limited amount of training data, we use a regularization framework to estimate the parameters of the Gompertz model to improve the prediction performance on new data. We develop an efficient method to estimate this regularized Gompertz model based on the Broyden-Fletcher-Goldfarb-Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomic information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurologic deficits in newborns.

Differential effects of aging and Alzheimer's disease on medial temporal lobe cortical thickness and surface area.

The volume of parcellated cortical regions is a composite measure related to both thickness and surface area. It is not clear whether volumetric decreases in medial temporal lobe (MTL) cortical regions in aging and Alzheimer's disease (AD) are due to thinning, loss of surface area, or both, nor is it clear whether aging and AD differ in their effects on these properties. Participants included 28 Younger Normals, 47 Older Normals, and 29 patients with mild AD. T1-weighted MRI data were analyzed using a novel semi-automated protocol (presented in a companion article) to delineate the boundaries of entorhinal (ERC), perirhinal (PRC), and posterior parahippocampal (PPHC) cortical regions and calculate their mean thickness, surface area, and volume. Compared to Younger Normals, Older Normals demonstrated moderately reduced ERC and PPHC volumes, which were due primarily to reduced surface area. In contrast, the expected AD-related reduction in ERC volume was produced by a large reduction in thickness with minimal additional effect (beyond that of aging) on surface area. PRC and PPHC also showed large AD-related reductions in thickness. Of all these MTL morphometric measures, ERC and PRC thinning were the best predictors of poorer episodic memory performance in AD. Although the volumes of MTL cortical regions may decrease with both aging and AD, thickness is relatively preserved in normal aging, while even in its mild clinical stage, AD is associated with a large degree of thinning of MTL cortex. These differential morphometric effects of aging and AD may reflect distinct biologic processes and ultimately may provide insights into the anatomic substrates of change in memory-related functions of MTL cortex.

Prefrontal contributions to rule-based and information-integration category learning.

Previous research revealed that the basal ganglia play a critical role in category learning [Ell, S. W., Marchant, N. L., & Ivry, R. B. (2006). Focal putamen lesions impair learning in rule-based, but not information-integration categorization tasks. Neuropsychologia, 44(10), 1737-1751; Maddox, W. T. & Filoteo, J. V. (2007). Modeling visual attention and category learning in amnesiacs, striatal-damaged patients and normal aging. In Advances in Clinical-cognitive science: formal modeling and assessment of processes and symptoms (pp. 113-146). Washington DC: American Psychological Association] but less is known about the specific role of prefrontal cortical (PFC) regions in category learning. The current study examined rule-based (RB) and information-integration (II) category learning in 13 patients with damage primarily to ventral PFC regions. After 600 learning trials with feedback, patients were significantly less accurate than matched controls on both RB and II learning. Model-based analysis identified subgroups of patients whose impaired performance in each task was due to the use of sub-optimal learning strategies. Those patients impaired at either II or RB learning, performed significantly worse on the Wisconsin Card Sorting Test, a test of abstract rule formation and the ability to shift and maintain rules. Lesion analysis pointed to damage in a fairly circumscribed region of ventral medial prefrontal cortex as common to the impaired group of patients and those patients without ventral PFC damage mostly performed normally. These results provide further evidence that the ventromedial prefrontal cortex is critically important for the ability to monitor and integrate feedback in order to select and maintain optimal learning strategies.

Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer.

In vivo MRI-derived measurements of human cerebral cortex thickness are providing novel insights into normal and abnormal neuroanatomy, but little is known about their reliability. We investigated how the reliability of cortical thickness measurements is affected by MRI instrument-related factors, including scanner field strength, manufacturer, upgrade and pulse sequence. Several data processing factors were also studied. Two test-retest data sets were analyzed: 1) 15 healthy older subjects scanned four times at 2-week intervals on three scanners; 2) 5 subjects scanned before and after a major scanner upgrade. Within-scanner variability of global cortical thickness measurements was <0.03 mm, and the point-wise standard deviation of measurement error was approximately 0.12 mm. Variability was 0.15 mm and 0.17 mm in average, respectively, for cross-scanner (Siemens/GE) and cross-field strength (1.5 T/3 T) comparisons. Scanner upgrade did not increase variability nor introduce bias. Measurements across field strength, however, were slightly biased (thicker at 3 T). The number of (single vs. multiple averaged) acquisitions had a negligible effect on reliability, but the use of a different pulse sequence had a larger impact, as did different parameters employed in data processing. Sample size estimates indicate that regional cortical thickness difference of 0.2 mm between two different groups could be identified with as few as 7 subjects per group, and a difference of 0.1 mm could be detected with 26 subjects per group. These results demonstrate that MRI-derived cortical thickness measures are highly reliable when MRI instrument and data processing factors are controlled but that it is important to consider these factors in the design of multi-site or longitudinal studies, such as clinical drug trials.