Functional connectivity MRI (fcMRI) has been widely applied to explore group and individual differences. A confounding factor is head motion. Children move more than adults, older adults more than younger adults, and patients more than controls. Head motion varies considerably among individuals within the same population. Here we explored the influence of head motion on fcMRI estimates. Mean head displacement, maximum head displacement, the number of micro movements (> 0.1 mm), and head rotation were estimated in 1000 healthy, young adult subjects each scanned for two resting-state runs on matched 3T scanners. The majority of fcMRI variation across subjects was not linked to estimated head motion. However, head motion had significant, systematic effects on fcMRI network measures. Head motion was associated with decreased functional coupling in the default and frontoparietal control networks – two networks characterized by coupling among distributed regions of association cortex. Other network measures increased with motion including estimates of local functional coupling and coupling between left and right motor regions – a region pair sometimes used as a control in studies to establish specificity. Comparisons between groups of individuals with subtly different levels of head motion yielded difference maps that could be mistaken for neuronal effects in other contexts. These effects are important to consider when interpreting variation between groups and across individuals.

Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.

Amyloid deposition is present in 20 -50% of nondemented older adults yet the functional consequences remain unclear. The current study found that amyloid accumulation is correlated with functional disruption of the default network as measured by intrinsic activity correlations. Clinically normal participants (n ϭ 38, aged 60 -88 years) were characterized using 11 C-labeled Pittsburgh Compound B positron emission tomography imaging to estimate fibrillar amyloid burden and, separately, underwent functional magnetic resonance imaging (fMRI). The integrity of the default network was estimated by correlating rest-state fMRI time courses extracted from a priori regions including the posterior cingulate, lateral parietal, and medial prefrontal cortices. Clinically normal participants with high amyloid burden displayed significantly reduced functional correlations within the default network relative to participants with low amyloid burden. These reductions were also observed when amyloid burden was treated as a continuous, rather than a dichotomous, measure and when controlling for age and structural atrophy. Whole-brain analyses initiated by seeding the posterior cingulate cortex, a region of high amyloid burden in Alzheimer's disease, revealed significant disruption in the default network including functional disconnection of the hippocampal formation.

Disruption of functional connectivity between brain regions may represent an early functional consequence of β-amyloid pathology prior to clinical Alzheimer's disease. We aimed to investigate if non-demented older individuals with increased amyloid burden demonstrate disruptions of functional whole-brain connectivity in cortical hubs (brain regions typically highly connected to multiple other brain areas) and if these disruptions are associated with neuronal dysfunction as measured with fluorodeoxyglucose-positron emission tomography. In healthy subjects without cognitive symptoms and patients with mild cognitive impairment, we used positron emission tomography to assess amyloid burden and cerebral glucose metabolism, structural magnetic resonance imaging to quantify atrophy and novel resting state functional magnetic resonance imaging processing methods to calculate whole-brain connectivity. Significant disruptions of whole-brain connectivity were found in amyloid-positive patients with mild cognitive impairment in typical cortical hubs (posterior cingulate cortex/precuneus), strongly overlapping with regional hypometabolism. Subtle connectivity disruptions and hypometabolism were already present in amyloid-positive asymptomatic subjects. Voxel-based morphometry measures indicate that these findings were not solely a consequence of regional atrophy. Whole-brain connectivity values and metabolism showed a positive correlation with each other and a negative correlation with amyloid burden. These results indicate that disruption of functional connectivity and hypometabolism may represent early functional consequences of emerging molecular Alzheimer's disease pathology, evolving prior to clinical onset of dementia. The spatial overlap between hypometabolism and disruption of connectivity in cortical hubs points to a particular susceptibility of these regions to early Alzheimer's-type neurodegeneration and may reflect a link between synaptic dysfunction and functional disconnection.

Coherent fluctuations of spontaneous brain activity are present in distinct functional-anatomic brain systems during undirected wakefulness. However, the behavioral significance of this spontaneous activity has only begun to be investigated. Our previous studies have demonstrated that successful memory formation requires coordinated neural activity in a distributed memory network including the hippocampus and posteromedial cortices, specifically the precuneus and posterior cingulate (PPC), thought to be integral nodes of the default network. In this study, we examined whether intrinsic connectivity during the resting state between the hippocampus and PPC can predict individual differences in the performance of an associative memory task among cognitively intact older individuals. The intrinsic connectivity, between regions within the hippocampus and PPC that were maximally engaged during a subsequent memory fMRI task, was measured during a period of rest prior to the performance of the memory paradigm. Stronger connectivity between the hippocampal and posteromedial regions during rest predicted better performance on the memory task. Furthermore, hippocampal-PPC intrinsic connectivity was also significantly correlated with episodic memory measures on neuropsychological tests, but not with performance in non-memory domains. Whole brain exploratory analyses further confirmed the spatial specificity of the relationship between hippocampal-default network posteromedial cortical connectivity and memory performance in older subjects. Our findings provide support for the hypothesis that one of the functions of this large-scale brain network is to subserve episodic memory processes. Research is ongoing to determine if impaired connectivity between these regions may serve as a predictor of memory decline related to early Alzheimer's disease.Correspondence: Reisa A. Sperling M.

The default-mode network (DMN) is a distributed functional-anatomic network implicated in supporting memory. Current resting-state functional connectivity studies in humans remain divided on the exact involvement of medial temporal lobe (MTL) in this network at rest. Notably, it is unclear to what extent the MTL regions involved in successful memory encoding are connected to the cortical nodes of the DMN during resting-state. Our findings using functional connectivity MRI analyses of resting-state data indicate that the parahippocampal gyrus (PHG) is the primary hub of the DMN in the MTL during resting-state. Also, connectivity of the PHG is distinct from connectivity of hippocampal regions identified by an associative memory encoding task. We confirmed that several hippocampal encoding regions lack significant functional connectivity with cortical DMN nodes during resting-state. Additionally, a mediation analysis showed that resting-state connectivity between the hippocampus and posterior cingulate cortex — a major hub of the DMN — is indirect and mediated by the PHG. Our findings support the hypothesis that the MTL memory system represents a functional sub-network that relates to the cortical nodes of the DMN through parahippocampal functional connections.

Examination of large-scale distributed networks within the individual reveals details of cortical network organization that are absent in group-averaged studies. One recent discovery is that a distributed transmodal network, often referred to as the “default network,” comprises two closely interdigitated networks, only one of which is coupled to posterior parahippocampal cortex. Not all studies of individuals have identified the same networks, and questions remain about the degree to which the two networks are separate, particularly within regions hypothesized to be interconnected hubs. In this study we replicate the observation of network separation across analytical (seed-based connectivity and parcellation) and data projection (volume and surface) methods in two individuals each scanned 31 times. Additionally, three individuals were examined with high-resolution (7T; 1.35 mm) functional magnetic resonance imaging to gain further insight into the anatomical details. The two networks were identified with separate regions localized to adjacent portions of the cortical ribbon, sometimes inside the same sulcus. Midline regions previously implicated as hubs revealed near complete spatial separation of the two networks, displaying a complex spatial topography in the posterior cingulate and precuneus. The network coupled to parahippocampal cortex also revealed a separate region directly within the hippocampus, at or near the subiculum. These collective results support that the default network is composed of at least two spatially juxtaposed networks. Fine spatial details and juxtapositions of the two networks can be identified within individuals at high resolution, providing insight into the network organization of association cortex and placing further constraints on interpretation of group-averaged neuroimaging data. NEW & NOTEWORTHY Recent evidence has emerged that canonical large-scale networks such as the “default network” fractionate into parallel distributed networks when defined within individuals. This research uses high-resolution imaging to show that the networks possess juxtapositions sometimes evident inside the same sulcus and within regions that have been previously hypothesized to be network hubs. Distinct circumscribed regions of one network were also resolved in the hippocampal formation, at or near the parahippocampal cortex and subiculum.

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 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 (ConnecomeDB). 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.