The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale heritability-based screening and high-dimensional heritability profile construction.

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP.

In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.

Cognitive decline is commonly observed in advanced aging even in the absence of disease. Here we explore the possibility that normal aging is accompanied by disruptive alterations in the coordination of large-scale brain systems that support high-level cognition. In 93 adults aged 18 to 93, we demonstrate that aging is characterized by marked reductions in normally present functional correlations within two higher-order brain systems. Anterior to posterior components within the default network were most severely disrupted with age. Furthermore, correlation reductions were severe in older adults free from Alzheimer's disease (AD) pathology as determined by amyloid imaging, suggesting that functional disruptions were not the result of AD. Instead, reduced correlations were associated with disruptions in white matter integrity and poor cognitive performance across a range of domains. These results suggest that cognitive decline in normal aging arises from functional disruption in the coordination of large-scale brain systems that support cognition.

Networks of widely distributed regions populate human association cortex. One network, often called the default network, is positioned at the apex of a gradient of sequential networks that radiate outward from primary cortex. Here, extensive anatomical data made available through the Marmoset Brain Architecture Project are explored to show a homologue exists in marmoset. Results reveal that a gradient of networks extend outward from primary cortex to progressively higher-order transmodal association cortex in both frontal and temporal cortex. The apex transmodal network comprises frontopolar and rostral temporal association cortex, parahippocampal areas TH / TF, the ventral posterior midline, and lateral parietal association cortex. The positioning of this network in the gradient and its composition of areas make it a candidate homologue to the human default network. That the marmoset, a physiologically- and genetically-accessible primate, might possess a default-network-like candidate creates opportunities for study of higher cognitive and social functions.

The striatum receives projections from multiple regions of the cerebral cortex consistent with its role in diverse motor, affective, and cognitive functions. Supporting cognitive functions, the caudate receives projections from cortical association regions. Building on recent insights about the details of how multiple cortical networks are specialized for distinct aspects of higher-order cognition, we revisited caudate organization using within-individual precision neuroimaging (n=2, each participant scanned 31 times). Detailed analysis revealed that the caudate has side-by-side zones that are coupled to at least Give distinct distributed association networks, paralleling the specialization observed in the cerebral cortex. Examining correlation maps from closely juxtaposed seed regions in the caudate recapitulated the Give distinct cerebral networks including their multiple spatially distributed regions. These results extend the general notion of parallel specialized basal ganglia circuits, with the additional discovery that even within the caudate, there is Gine-grained separation of multiple distinct higher-order networks.

T1-weighted structural MRI is widely used to measure brain morphometry (e.g., cortical thickness and subcortical volumes). Accelerated scans as fast as one minute or less are now available but it is unclear if they are adequate for quantitative morphometry. Here we compared the measurement properties of a widely adopted 1.0 mm resolution scan from the Alzheimer's Disease Neuroimaging Initiative (ADNI = 5'12'') with two variants of highly accelerated 1.0 mm scans (compressed-sensing, CSx6 = 1'12''; and wave-controlled aliasing in parallel imaging, WAVEx9 = 1'09'') in a test-retest study of 37 older adults aged 54 to 86 (including 19 individuals diagnosed with a neurodegenerative dementia). Rapid scans produced highly reliable morphometric measures that largely matched the quality of morphometrics derived from the ADNI scan. Regions of lower reliability and relative divergence between ADNI and rapid scan alternatives tended to occur in midline regions and regions with susceptibility-induced artifacts. Critically, the rapid scans yielded morphometric measures similar to the ADNI scan in regions of high atrophy. The results converge to suggest that, for many current uses, extremely rapid scans can replace longer scans. As a final test, we explored the possibility of a 0'49'' 1.2 mm CSx6 structural scan, which also showed promise. Rapid structural scans may benefit MRI studies by shortening the scan session and reducing cost, minimizing opportunity for movement, creating room for additional scan sequences, and allowing for the repetition of structural scans to increase precision of the estimates.

The human cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks using a Multi-Session Hierarchical Bayesian Model (MS-HBM) applied to intensively sampled within-individual functional MRI (fMRI) data. The network estimation procedure was initially developed and tested in two participants (each scanned 31 times) and then prospectively applied to 15 new participants (each scanned 8 to 11 times). Detailed analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that also linked to distant regions. Third-order networks each possessed regions distributed widely throughout association cortex. Moreover, regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated similarly across multiple cortical zones. We refer to these as Supra-Areal Association Megaclusters (SAAMs). Within each SAAM, two candidate control regions were typically adjacent to three separate domain-specialized regions. Independent task data were analyzed to explore functional response properties. The somatomotor and visual first-order networks responded to body movements and visual stimulation, respectively. A subset of the second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient or novel events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions within each SAAM did not track working memory load but rather dissociated across language, social, and spatial / episodic processing domains. These results support a model of the cerebral cortex in which progressively higher-order networks nest outwards from primary sensory and motor cortices. Within the apex zones of association cortex there is specialization of large-scale networks that divides domain-flexible from domain-specialized regions repeatedly across parietal, temporal, and prefrontal cortices. We discuss implications of these findings including how repeating organizational motifs may emerge during development.

Aging-related differences in white matter integrity, the presence of amyloid plaques, and density of biomarkers indicative of dopamine functions can be detected and quantified with in vivo human imaging. The primary aim of the present study was to investigate whether these imaging-based measures constitute independent imaging biomarkers in older adults, which would speak to the hypothesis that the aging brain is characterized by multiple independent neurobiological cascades. We assessed MRI-based markers of white matter integrity and PET-based marker of dopamine transporter density and amyloid deposition in the same set of 53 clinically normal individuals (age 65-87). A multiple regression analysis demonstrated that dopamine transporter availability is predicted by white matter integrity, which was detectable even after controlling for chronological age. Further post-hoc exploration revealed that dopamine transporter availability was further associated with systolic blood pressure, mirroring the established association between cardiovascular health and white matter integrity. Dopamine transporter availability was not associated with the presence of amyloid burden. Neurobiological correlates of dopamine transporter measures in aging are therefore likely unrelated to Alzheimer's disease but are aligned with white matter integrity and cardiovascular risk. More generally, these results suggest that two common imaging markers of the aging brain that are typically investigated separately do not reflect independent neurobiological processes. Hum Brain Mapp 37:621-631, 2016. © 2015 Wiley Periodicals, Inc.

Mild cognitive impairment can represent a transitional state between normal ageing and Alzheimer's disease. Non-invasive diagnostic methods are needed to identify mild cognitive impairment individuals for early therapeutic interventions. Our objective was to determine whether automated magnetic resonance imaging-based measures could identify mild cognitive impairment individuals with a high degree of accuracy. Baseline volumetric T1-weighted magnetic resonance imaging scans of 313 individuals from two independent cohorts were examined using automated software tools to identify the volume and mean thickness of 34 neuroanatomic regions. The first cohort included 49 older controls and 48 individuals with mild cognitive impairment, while the second cohort included 94 older controls and 57 mild cognitive impairment individuals. Sixty-five patients with probable Alzheimer's disease were also included for comparison. For the discrimination of mild cognitive impairment, entorhinal cortex thickness, hippocampal volume and supramarginal gyrus thickness demonstrated an area under the curve of 0.91 (specificity 94%, sensitivity 74%, positive likelihood ratio 12.12, negative likelihood ratio 0.29) for the first cohort and an area under the curve of 0.95 (specificity 91%, sensitivity 90%, positive likelihood ratio 10.0, negative likelihood ratio 0.11) for the second cohort. For the discrimination of Alzheimer's disease, these three measures demonstrated an area under the curve of 1.0. The three magnetic resonance imaging measures demonstrated significant correlations with clinical and neuropsychological assessments as well as with cerebrospinal fluid levels of tau, hyperphosphorylated tau and abeta 42 proteins. These results demonstrate that automated magnetic resonance imaging measures can serve as an in vivo surrogate for disease severity, underlying neuropathology and as a non-invasive diagnostic method for mild cognitive impairment and Alzheimer's disease.

Implicit memory, including priming, can be preserved in aging and dementia despite impairment of explicit memory. To explore the neural correlates of preserved memory ability, whole-brain functional MRI (fMRI) was used during a repetition priming paradigm to study 34 young adults, 33 older adults without dementia, and 24 older adults in the early stages of dementia of the Alzheimer type (DAT). Both older adult groups showed repetition-based response time benefits (priming) and changes in activation along inferior frontal gyrus similar to those shown by young adults. Across all three groups, repetition-related response time reductions correlated with prefrontal activity reductions, demonstrating a direct relation between priming and fMRI-measured activity change. These results suggest that despite difficulties with deliberate memory, both older adults without dementia and those with early-stage DAT can modify behavior mediated by prefrontal contributions, making these preserved abilities an attractive target for cognitive training and rehabilitation.

Spelling performance across a common set of stimuli was examined in young adults, healthy older adults, individuals with early stage dementia of the Alzheimer's type (DAT), and four individuals with a primary semantic impairment (PSI). The stimuli included homophones and low-frequency sound-to-spelling consistent (i.e. words with more predictable spellings) and inconsistent words (i.e. words with less predictable spellings). The results indicate that when spelling homophonic words (spelling/pleIn/ as plane versus plain), younger adults and to a greater extent individuals with PSI placed relatively more emphasis on phonological information (i.e. spell the word based on sound-to-spelling principles) whereas healthy older adults and individuals with DAT placed relatively more emphasis on semantic information (i.e. spell the word based on the dominant usage). For non-homophonic words, large consistency effects (spelling plaid as plad) were observed for both individuals with DAT and individuals with PSI. It is proposed that the decrease in accuracy for inconsistent words has different bases in DAT and PSI. We propose that deficits in attentional control (i.e. selection) underlie performance in DAT whereas disruption of semantic representations underlies performance in PSI.

The MGH-USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing a magnetic field gradient of up to 300 mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH-USC Adult Diffusion Dataset (N=35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU-Minn Connectome Database (ConnectomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH-Harvard-USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH-USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography.

Network properties can be estimated using functional connectivity MRI (fcMRI). However, regional variation of the fMRI signal causes systematic biases in network estimates including correlation attenuation in regions of low measurement reliability. Here we computed the spatial distribution of fcMRI reliability using longitudinal fcMRI datasets and demonstrated how pre-estimated reliability maps can correct for correlation attenuation. As a test case of reliability-based attenuation correction we estimated properties of the default network, where reliability was significantly lower than average in the medial temporal lobe and higher in the posterior medial cortex, heterogeneity that impacts estimation of the network. Accounting for this bias using attenuation correction revealed that the medial temporal lobe's contribution to the default network is typically underestimated. To render this approach useful to a greater number of datasets, we demonstrate that test-retest reliability maps derived from repeated runs within a single scanning session can be used as a surrogate for multi-session reliability mapping. Using data segments with different scan lengths between 1 and 30 min, we found that test-retest reliability of connectivity estimates increases with scan length while the spatial distribution of reliability is relatively stable even at short scan lengths. Finally, analyses of tertiary data revealed that reliability distribution is influenced by age, neuropsychiatric status and scanner type, suggesting that reliability correction may be especially important when studying between-group differences. Collectively, these results illustrate that reliability-based attenuation correction is an easily implemented strategy that mitigates certain features of fMRI signal nonuniformity.

The goal of the Brain Genomics Superstruct Project (GSP) is to enable large-scale exploration of the links between brain function, behavior, and ultimately genetic variation. To provide the broader scientific community data to probe these associations, a repository of structural and functional magnetic resonance imaging (MRI) scans linked to genetic information was constructed from a sample of healthy individuals. The initial release, detailed in the present manuscript, encompasses quality screened cross-sectional data from 1,570 participants ages 18 to 35 years who were scanned with MRI and completed demographic and health questionnaires. Personality and cognitive measures were obtained on a subset of participants. Each dataset contains a T1-weighted structural MRI scan and either one (n=1,570) or two (n=1,139) resting state functional MRI scans. Test-retest reliability datasets are included from 69 participants scanned within six months of their initial visit. For the majority of participants self-report behavioral and cognitive measures are included (n=926 and n=892 respectively). Analyses of data quality, structure, function, personality, and cognition are presented to demonstrate the dataset's utility.

Individuals with semantic dementia (SD) were differentiated neuropsychologically from individuals with dementia of the Alzheimer type (DAT) at very mild-to-mild stages (clinical dementia rating 0.5 or 1). A picture naming and recognition memory experiment provided a particularly useful probe for early identification, with SD individuals showing preserved picture recognition memory and impaired naming, and DAT individuals tending to show the reverse dissociation. The identification of an early SD group provided the opportunity to inform models of reading by exploring the influence of isolated lexical semantic impairment on reading regular words. Results demonstrated prolonged latency in both SD and DAT group reading compared to a control group but exaggerated influence of frequency and length only for the SD group. The SD reading pattern was associated with focal atrophy of the left temporal pole. These cognitive-neuroanatomical findings suggest a role for the left temporal pole in lexical/semantic components of reading and demonstrate that cortical thickness differences in the left temporal pole correlate with prolonged latency associated with increased reliance on sublexical components of reading.

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.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Recent technological and analytical progress in brain imaging has enabled the examination of brain organization and connectivity at unprecedented levels of detail. The Human Connectome Project in Development (HCP-D) is exploiting these tools to chart developmental changes in brain connectivity. When complete, the HCP-D will comprise approximately ∼1750 open access datasets from 1300 + healthy human participants, ages 5-21 years, acquired at four sites across the USA. The participants are from diverse geographical, ethnic, and socioeconomic backgrounds. While most participants are tested once, others take part in a three-wave longitudinal component focused on the pubertal period (ages 9-17 years). Brain imaging sessions are acquired on a 3 T Siemens Prisma platform and include structural, functional (resting state and task-based), diffusion, and perfusion imaging, physiological monitoring, and a battery of cognitive tasks and self-reports. For minors, parents additionally complete a battery of instruments to characterize cognitive and emotional development, and environmental variables relevant to development. Participants provide biological samples of blood, saliva, and hair, enabling assays of pubertal hormones, health markers, and banked DNA samples. This paper outlines the overarching aims of the project, the approach taken to acquire maximally informative data while minimizing participant burden, preliminary analyses, and discussion of the intended uses and limitations of the dataset.