Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization.

Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.

Participant diversity is necessary to advance brain aging research.

An absence of population-representative participant samples has limited research in healthy brain aging. We highlight examples of what can be gained by enrolling more diverse participant cohorts, and propose recommendations for specific reforms, both in terms of how researchers accomplish this goal and how institutions support and benchmark these efforts.

Dissociable Effects of Alzheimer's Disease-Related Cognitive Dysfunction and Aging on Functional Brain Network Segregation.

Alzheimer's disease (AD) is associated with changes in large-scale functional brain network organization. Individuals with AD exhibit less segregated resting-state brain networks compared with individuals without dementia. However, declines in brain network segregation are also evident as adult individuals grow older. Determining whether these observations reflect unique or overlapping alterations on the functional connectome of the brain is essential for understanding the impact of AD on network organization and incorporating measures of functional brain network organization toward AD characterization. Relationships between AD dementia severity and participant's age on resting-state brain system segregation were examined in 326 cognitively healthy and 275 cognitively impaired human individuals recruited through the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 601; age range, 55-96 years; 320 females). Greater dementia severity and increasing age were independently associated with lower brain system segregation. Further, dementia versus age relationships with brain network organization varied according to the processing roles of brain systems and types of network interactions. Aging was associated with alterations to association systems, primarily among within-system relationships. Conversely, dementia severity was associated with alterations that included both association systems and sensory-motor systems and was most prominent among cross-system interactions. Dementia-related network alterations were evident regardless of the presence of cortical amyloid burden, revealing that the measures of functional network organization are unique from this marker of AD-related pathology. Collectively, these observations demonstrate the specific and widespread alterations in the topological organization of large-scale brain networks that accompany AD and highlight functionally dissociable brain network vulnerabilities associated with AD-related cognitive dysfunction versus aging.SIGNIFICANCE STATEMENT Alzheimer's disease (AD)-associated cognitive dysfunction is hypothesized to be a consequence of brain network damage. It is unclear exactly how brain network alterations vary with dementia severity and whether they are distinct from alterations associated with aging. We evaluated functional brain network organization measured at rest among individuals who varied in age and dementia status. AD and aging exerted dissociable impacts on the brain's functional connectome. AD-associated brain network alterations were widespread and involved systems that subserve not only higher-order cognitive operations, but also sensory and motor operations. Notably, AD-related network alterations were independent of amyloid pathology. The research furthers our understanding of AD-related brain dysfunction and motivates refining existing frameworks of dementia characterization with measures of functional network organization.

Longitudinal changes in gray matter correspond to changes in cognition across the lifespan: implications for theories of cognition.

The present study examines the association between gray matter volume and cognition. Studies that have examined this issue have focused primarily on older adults, whereas the present study examines the issue across the entire adult lifespan. A total of 463 adults, ages 20-88 at first assessment, were followed longitudinally across three assessments over 8-10years. Significant individual differences in a general cognition measure comprised of measures of speed of processing, working memory, and episodic memory were observed, as well as in measures of cortical and subcortical gray matter. Parallel process latent growth curve modeling showed a reliable relationship between decreases in cortical matter and cognitive decline across the entire adult lifespan, which persisted after controlling for age effects. Implications of these findings in relation to progression toward dementia, risk assessment, cognitive intervention, and environmental factors are discussed, as well as implications for theories of cognitive aging.

Relationship of prefrontal brain lateralization to optimal cognitive function differs with age.

There is considerable debate about whether additional fMRI-measured activity in the right prefrontal cortex readily observed in older adults represents compensatory activation that enhances cognition or whether maintenance of youthful brain activity best supports cognitive function in late adulthood. To investigate this issue, we tested a large lifespan sample of 461 adults (aged 20-89) and treated degree of left-lateralization in ventrolateral and dorsolateral prefrontal cortex during a semantic judgment fMRI task as an individual differences variable to predict cognition. We found that younger adults were highly left-lateralized, but lateralization did not predict better cognition, whereas higher left-lateralization of prefrontal cortex predicted better cognitive performance in middle-aged adults, providing evidence that left-lateralized, youth-like patterns are optimal in middle age. This relationship was reversed in older adults, with lower laterality scores associated with better cognition. The findings suggest that bilaterality in older adults facilitates cognition, but early manifestation of this pattern during middle age is characteristic of low performers. Implications of these findings for current theories of neurocognitive aging are discussed.

Parallel hippocampal-parietal circuits for self- and goal-oriented processing.

The hippocampus is critically important for a diverse range of cognitive processes, such as episodic memory, prospective memory, affective processing, and spatial navigation. Using individual-specific precision functional mapping of resting-state functional MRI data, we found the anterior hippocampus (head and body) to be preferentially functionally connected to the default mode network (DMN), as expected. The hippocampal tail, however, was strongly preferentially functionally connected to the parietal memory network (PMN), which supports goal-oriented cognition and stimulus recognition. This anterior-posterior dichotomy of resting-state functional connectivity was well-matched by differences in task deactivations and anatomical segmentations of the hippocampus. Task deactivations were localized to the hippocampal head and body (DMN), relatively sparing the tail (PMN). The functional dichotomization of the hippocampus into anterior DMN-connected and posterior PMN-connected parcels suggests parallel but distinct circuits between the hippocampus and medial parietal cortex for self- versus goal-oriented processing.

The relationship of functional hippocampal activity, amyloid deposition, and longitudinal memory decline to memory complaints in cognitively healthy older adults.

We evaluated whether self-reports of worse cognition in older adults with normal cognitive function reflected actual memory decline, amyloid pathology, and subtle vulnerabilities in hippocampal function. We measured subjective cognitive decline (SCD) in 156 older participants from the Dallas Lifespan Brain Study. Functional hippocampal activation during encoding, measured with fMRI, and longitudinal memory change that was measured in the four years preceding the SCD measures were used to predict the magnitude of SCD. A subsample (N=105) also underwent 18F-Florbetapir PET imaging that measured amyloid burden. Results showed that increased SCD were associated with greater prior memory decline and amyloid deposition. Importantly, decreased hippocampal activation during encoding was a significant predictor of SCD, particularly in young-old adults below 69 years old, above and beyond prior memory change and amyloid deposition. These results indicate that multiple measures of neural and cognitive dysfunction are simultaneously associated with SCD. Moreover, SCD in younger seniors appears to reflect deficient hippocampal activity that increases their reports of poorer memory, independent of amyloid. This manuscript is part of the Special Issue entitled "Cognitive Neuroscience of Healthy and Pathological Aging" edited by Drs. M. N. Rajah, S. Belleville, and R. Cabeza. This article is part of the Virtual Special Issue titled COGNITIVE NEU-ROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.

Long-term prognosis and educational determinants of brain network decline in older adult individuals.

Older adults with lower education are at greater risk for dementia. It is unclear which brain changes lead to these outcomes. Longitudinal imaging-based measures of brain structure and function were examined in adult individuals (baseline age, 45-86 years; two to five visits per participant over 1-9 years). College degree completion differentiates individual-based and neighborhood-based measures of socioeconomic status and disadvantage. Older adults (~65 years and over) without a college degree exhibit a pattern of declining large-scale functional brain network organization (resting-state system segregation) that is less evident in their college-educated peers. Declining brain system segregation predicts impending changes in dementia severity, measured up to 10 years past the last scan date. The prognostic value of brain network change is independent of Alzheimer's disease (AD)-related genetic risk (APOE status), the presence of AD-associated pathology (cerebrospinal fluid phosphorylated tau, cortical amyloid) and cortical thinning. These results demonstrate that the trajectory of an individual's brain network organization varies in relation to their educational attainment and, more broadly, is a unique indicator of individual brain health during older age.

Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity.

Denoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i.e., 'motion' parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (>0.1 â€‹Hz, here referred to as 'HF-motion'), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2.0-2.5 â€‹s) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2.0 to 2.5 â€‹s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants.

Regional amyloid accumulation and cognitive decline in initially amyloid-negative adults.

OBJECTIVE: To assess whether global or regional changes in amyloid burden over 4 years predict early declines in episodic memory in initially amyloid-negative adults. METHODS: One hundred twenty-six initially amyloid-negative, cognitively normal participants (age 30-89 years) were included from the Dallas Lifespan Brain Study who completed florbetapir PET and a cognitive battery at baseline and 4-year follow-up. Standardized uptake value ratio (SUVR) change was computed across 8 bilateral regions of interest. Using general linear models, we examined the relationship between change in global and regional SUVR and change in episodic memory, controlling for baseline SUVR, baseline memory, age, sex, education, and APOE status. RESULTS: In initially amyloid-negative adults, we detected a regionally specific relationship between declining episodic memory and increasing amyloid accumulation across multiple posterior cortical regions. In addition, these amyloid-related changes in memory persisted when we focused on middle-aged adults only and after controlling for atrophy in global cortical, hippocampal, and Alzheimer disease signature cortical volume. CONCLUSION: Our results indicate that assessing regional changes in amyloid, particularly in posterior cortical regions, can aid in the early detection of subclinical amyloid-related decline in episodic memory as early as middle age. Future research incorporating tau and other markers of neurodegeneration is needed to clarify the sequence of events that lead to this early, subclinical memory decline.

Functional Parcellation of the Cerebral Cortex Across the Human Adult Lifespan.

Adult aging is associated with differences in structure, function, and connectivity of brain areas. Age-based brain comparisons have typically rested on the assumption that brain areas exhibit a similar spatial organization across age; we evaluate this hypothesis directly. Area parcellation methods that identify locations where resting-state functional correlations (RSFC) exhibit abrupt transitions (boundary-mapping) are used to define cortical areas in cohorts of individuals sampled across a large range of the human adult lifespan (20-93 years). Most of the strongest areal boundaries are spatially consistent across age. Differences in parcellation boundaries are largely explained by differences in cortical thickness and anatomical alignment in older relative to younger adults. Despite the parcellation similarities, age-specific parcellations exhibit better internal validity relative to a young-adult parcellation applied to older adults' data, and age-specific parcels are better able to capture variability in task-evoked functional activity. Incorporating age-specific parcels as nodes in RSFC network analysis reveals that the spatial topography of the brain's large-scale system organization is comparable throughout aging, but confirms that the segregation of systems declines with increasing age. These observations demonstrate that many features of areal organization are consistent across adulthood, and reveal sources of age-related brain variation that contribute to the differences.

Socioeconomic status moderates age-related differences in the brain's functional network organization and anatomy across the adult lifespan.

An individual's environmental surroundings interact with the development and maturation of their brain. An important aspect of an individual's environment is his or her socioeconomic status (SES), which estimates access to material resources and social prestige. Previous characterizations of the relation between SES and the brain have primarily focused on earlier or later epochs of the lifespan (i.e., childhood, older age). We broaden this work to examine the relationship between SES and the brain across a wide range of human adulthood (20-89 years), including individuals from the less studied middle-age range. SES, defined by education attainment and occupational socioeconomic characteristics, moderates previously reported age-related differences in the brain's functional network organization and whole-brain cortical structure. Across middle age (35-64 years), lower SES is associated with reduced resting-state system segregation (a measure of effective functional network organization). A similar but less robust relationship exists between SES and age with respect to brain anatomy: Lower SES is associated with reduced cortical gray matter thickness in middle age. Conversely, younger and older adulthood do not exhibit consistent SES-related difference in the brain measures. The SES-brain relationships persist after controlling for measures of physical and mental health, cognitive ability, and participant demographics. Critically, an individual's childhood SES cannot account for the relationship between their current SES and functional network organization. These findings provide evidence that SES relates to the brain's functional network organization and anatomy across adult middle age, and that higher SES may be a protective factor against age-related brain decline.

Segregated Systems of Human Brain Networks.

The organization of the brain network enables its function. Evaluation of this organization has revealed that large-scale brain networks consist of multiple segregated subnetworks of interacting brain areas. Descriptions of resting-state network architecture have provided clues for understanding the functional significance of these segregated subnetworks, many of which correspond to distinct brain systems. The present report synthesizes accumulating evidence to reveal how maintaining segregated brain systems renders the human brain network functionally specialized, adaptable to task demands, and largely resilient following focal brain damage. The organizational properties that support system segregation are harmonious with the properties that promote integration across the network, but confer unique and important features to the brain network that are central to its function and behavior.

Face percept formation in human ventral temporal cortex.

Loci in ventral temporal cortex are selectively active during viewing of faces and other objects, but it remains unclear whether these areas represent accumulation of simple visual information or processing of intact percept. We measured broadband electrocorticographic changes from implanted electrodes on the ventral temporal brain surface while showing patients noise-degraded images of faces and houses. In a subset of posterior fusiform gyrus face-selective regions, cortical activity decreased parametrically with noise increase, until the perceptual threshold was surpassed. At noise levels higher than the perceptual threshold, and for house stimuli, activity remained at baseline. We propose that this convergence of proportional and thresholded response may identify active areas where face percepts are extracted from simple visual features. These loci exist within a topological structure of face percept formation in the human ventral visual stream, preceded by category-nonselective activity in pericalcarine early visual areas and in concert with all-or-nothing activity in postperceptual subregions of the ventral temporal lobe. This topological organization suggests a physiological basis for the anatomy of face perception, explaining different perceptual deficits following temporal lobe injury.NEW & NOTEWORTHY Philosophers have puzzled for millennia about how humans build abstract conceptual objects (house/face/tool) from the simple features of the world they see around them (line/patch/lighting). Understanding the biological foundation of this process requires detailed knowledge of the spatial-temporal characteristics of cerebral cortex. By examining the physiology of the human temporal lobe via implanted electrodes while showing subjects noise-degraded images, we find that face percept formation happens in specific subregions within known face-processing areas.

Resting-State Network Topology Differentiates Task Signals across the Adult Life Span.

Brain network connectivity differs across individuals. For example, older adults exhibit less segregated resting-state subnetworks relative to younger adults (Chan et al., 2014). It has been hypothesized that individual differences in network connectivity impact the recruitment of brain areas during task execution. While recent studies have described the spatial overlap between resting-state functional correlation (RSFC) subnetworks and task-evoked activity, it is unclear whether individual variations in the connectivity pattern of a brain area (topology) relates to its activity during task execution. We report data from 238 cognitively normal participants (humans), sampled across the adult life span (20-89 years), to reveal that RSFC-based network organization systematically relates to the recruitment of brain areas across two functionally distinct tasks (visual and semantic). The functional activity of brain areas (network nodes) were characterized according to their patterns of RSFC: nodes with relatively greater connections to nodes in their own functional system ("non-connector" nodes) exhibited greater activity than nodes with relatively greater connections to nodes in other systems ("connector" nodes). This "activation selectivity" was specific to those brain systems that were central to each of the tasks. Increasing age was accompanied by less differentiated network topology and a corresponding reduction in activation selectivity (or differentiation) across relevant network nodes. The results provide evidence that connectional topology of brain areas quantified at rest relates to the functional activity of those areas during task. Based on these findings, we propose a novel network-based theory for previous reports of the "dedifferentiation" in brain activity observed in aging.SIGNIFICANCE STATEMENT Similar to other real-world networks, the organization of brain networks impacts their function. As brain network connectivity patterns differ across individuals, we hypothesized that individual differences in network connectivity would relate to differences in brain activity. Using functional MRI in a group of individuals sampled across the adult life span (20-89 years), we measured correlations at rest and related the functional connectivity patterns to measurements of functional activity during two independent tasks. Brain activity varied in relation to connectivity patterns revealed by large-scale network analysis. This relationship tracked the differences in connectivity patterns accompanied by older age, providing important evidence for a link between the topology of areal connectivity measured at rest and the functional recruitment of these areas during task performance.

Motion-related artifacts in structural brain images revealed with independent estimates of in-scanner head motion.

Motion-contaminated T1-weighted (T1w) magnetic resonance imaging (MRI) results in misestimates of brain structure. Because conventional T1w scans are not collected with direct measures of head motion, a practical alternative is needed to identify potential motion-induced bias in measures of brain anatomy. Head movements during functional MRI (fMRI) scanning of 266 healthy adults (20-89 years) were analyzed to reveal stable features of in-scanner head motion. The magnitude of head motion increased with age and exhibited within-participant stability across different fMRI scans. fMRI head motion was then related to measurements of both quality control (QC) and brain anatomy derived from a T1w structural image from the same scan session. A procedure was adopted to "flag" individuals exhibiting excessive head movement during fMRI or poor T1w quality rating. The flagging procedure reliably reduced the influence of head motion on estimates of gray matter thickness across the cortical surface. Moreover, T1w images from flagged participants exhibited reduced estimates of gray matter thickness and volume in comparison to age- and gender-matched samples, resulting in inflated effect sizes in the relationships between regional anatomical measures and age. Gray matter thickness differences were noted in numerous regions previously reported to undergo prominent atrophy with age. Recommendations are provided for mitigating this potential confound, and highlight how the procedure may lead to more accurate measurement and comparison of anatomical features. Hum Brain Mapp 38:472-492, 2017. © 2016 Wiley Periodicals, Inc.

Decreased segregation of brain systems across the healthy adult lifespan.

Healthy aging has been associated with decreased specialization in brain function. This characterization has focused largely on describing age-accompanied differences in specialization at the level of neurons and brain areas. We expand this work to describe systems-level differences in specialization in a healthy adult lifespan sample (n = 210; 20-89 y). A graph-theoretic framework is used to guide analysis of functional MRI resting-state data and describe systems-level differences in connectivity of individual brain networks. Young adults' brain systems exhibit a balance of within- and between-system correlations that is characteristic of segregated and specialized organization. Increasing age is accompanied by decreasing segregation of brain systems. Compared with systems involved in the processing of sensory input and motor output, systems mediating "associative" operations exhibit a distinct pattern of reductions in segregation across the adult lifespan. Of particular importance, the magnitude of association system segregation is predictive of long-term memory function, independent of an individual's age.

Parcellating an individual subject's cortical and subcortical brain structures using snowball sampling of resting-state correlations.

We describe methods for parcellating an individual subject's cortical and subcortical brain structures using resting-state functional correlations (RSFCs). Inspired by approaches from social network analysis, we first describe the application of snowball sampling on RSFC data (RSFC-Snowballing) to identify the centers of cortical areas, subdivisions of subcortical nuclei, and the cerebellum. RSFC-Snowballing parcellation is then compared with parcellation derived from identifying locations where RSFC maps exhibit abrupt transitions (RSFC-Boundary Mapping). RSFC-Snowballing and RSFC-Boundary Mapping largely complement one another, but also provide unique parcellation information; together, the methods identify independent entities with distinct functional correlations across many cortical and subcortical locations in the brain. RSFC parcellation is relatively reliable within a subject scanned across multiple days, and while the locations of many area centers and boundaries appear to exhibit considerable overlap across subjects, there is also cross-subject variability-reinforcing the motivation to parcellate brains at the level of individuals. Finally, examination of a large meta-analysis of task-evoked functional magnetic resonance imaging data reveals that area centers defined by task-evoked activity exhibit correspondence with area centers defined by RSFC-Snowballing. This observation provides important evidence for the ability of RSFC to parcellate broad expanses of an individual's brain into functionally meaningful units.

Repetition-related reductions in neural activity reveal component processes of mental simulation.

In everyday life, people adaptively prepare for the future by simulating dynamic events about impending interactions with people, objects and locations. Previous research has consistently demonstrated that a distributed network of frontal-parietal-temporal brain regions supports this ubiquitous mental activity. Nonetheless, little is known about the manner in which specific regions of this network contribute to component features of future simulation. In two experiments, we used a functional magnetic resonance (fMR)-repetition suppression paradigm to demonstrate that distinct frontal-parietal-temporal regions are sensitive to processing the scenarios or what participants imagined was happening in an event (e.g., medial prefrontal, posterior cingulate, temporal-parietal and middle temporal cortices are sensitive to the scenarios associated with future social events), people (medial prefrontal cortex), objects (inferior frontal and premotor cortices) and locations (posterior cingulate/retrosplenial, parahippocampal and posterior parietal cortices) that typically constitute simulations of personal future events. This pattern of results demonstrates that the neural substrates of these component features of event simulations can be reliably identified in the context of a task that requires participants to simulate complex, everyday future experiences.

An approach for parcellating human cortical areas using resting-state correlations.

Resting State Functional Connectivity (RSFC) reveals properties related to the brain's underlying organization and function. Features related to RSFC signals, such as the locations where the patterns of RSFC exhibit abrupt transitions, can be used to identify putative boundaries between cortical areas (RSFC-Boundary Mapping). The locations of RSFC-based area boundaries are consistent across independent groups of subjects. RSFC-based parcellation converges with parcellation information from other modalities in many locations, including task-evoked activity and probabilistic estimates of cellular architecture, providing evidence for the ability of RSFC to parcellate brain structures into functionally meaningful units. We not only highlight a collection of these observations, but also point out several limitations and observations that mandate careful consideration in using and interpreting RSFC for the purposes of parcellating the brain's cortical and subcortical structures.