Micaela Y. Chan

Large-scale lifespan neuroimaging studies increasingly integrate data across distinct cohorts to characterize trajectories of brain development and aging. However, systematic differences in acquisition protocols and hardware across cohorts can alter signal characteristics in ways that bias downstream analyses. Here, we examine three cohorts from the Human Connectome Project (HCP), spanning development (HCP-D), young adulthood (HCP-YA) and aging (HCP-A), to illustrate this issue and evaluate existing strategies to mitigate it. HCP has set standards for open, deeply phenotyped, high-resolution human neuroimaging, which are frequently used as high-quality reference datasets in tool validation, replication studies, and cross-cohort meta-analyses. However, neuroimaging acquisitions have differed across HCP cohorts because of changes in scanner hardware and acquisition sequences across study phases. Because of HCP’s widespread usage, even modest protocol differences between cohorts—and their downstream effects—can have outsized impacts on the field of neuroscience research. Our analysis reveals that the HCP-YA cohort exhibits systematically weaker temporal signal-to-noise ratio (tSNR) relative to HCP-D/A. These signal quality discrepancies propagate to downstream analyses, leading to differences in overall resting-state functional correlations and whole-brain and node-level measures of resting-state network organization (e.g., system segregation, modularity, participation coefficient). Consistent with protocol-driven signal differences, resting-state network measures derived from HCP-YA depart from expected lifespan trajectories, as confirmed by examination of two other lifespan datasets. Harmonization approaches accounting for protocol and scanner-model differences substantially lessen these artifactual differences in brain network measures. Our findings underscore that signal differences do not merely introduce noise, but can qualitatively alter estimated lifespan trajectories of functional network organization, including partially inverting expected lifespan patterns. Without appropriate harmonization, analyses that combine HCP cohorts can therefore result in biologically misleading inferences about brain development and aging. We demonstrate how small acquisition differences bias resting-state-derived network metrics, and how these effects can be mitigated. This work advances best practices for valid inference in multi-cohort lifespan neuroscience research.

Brain MRI shows promise for predicting cognitive functioning, but its utility depends on its capacity to capture stable between-person differences (e.g., patient stratification), longitudinal within-person changes (e.g., prognosis, treatment monitoring), or both. Using longitudinal data from 450 adults (aged 21-90; up to three waves, five years apart) in the Dallas Lifespan Brain Study, we benchmarked five modalities, task fMRI, functional connectivity (FC), structural MRI (sMRI), diffusion-weighted imaging (DWI), and arterial spin labeling (ASL), across 37 phenotypes and their combination. Stacking all MRI modalities into one marker predicted cognitive functioning with the highest accuracy (r-squared=.51), followed by DWI and FC. Variance decomposition showed MRI markers explained substantial between-person variance (up to 60.3%) but modest within-person changes (up to 17.2%) in cognitive functioning. Commonality analysis revealed most markers, except ASL, overlapped with age-related variance in cognitive functioning. These findings clarify the strengths and limitations of MRI markers for stratifying and monitoring cognitive aging.

Large-scale lifespan neuroimaging studies increasingly integrate data across distinct cohorts to characterize trajectories of brain development and aging. However, systematic differences in acquisition protocols and hardware across cohorts can alter signal characteristics in ways that bias downstream analyses. Here, we examine three cohorts from the Human Connectome Project (HCP), spanning development (HCP-D), young adulthood (HCP-YA) and aging (HCP-A), to illustrate this issue and evaluate existing strategies to mitigate it. HCP has set standards for open, deeply phenotyped, high-resolution human neuroimaging, which are frequently used as high-quality reference datasets in tool validation, replication studies, and cross-cohort meta-analyses. However, neuroimaging acquisitions have differed across HCP cohorts because of changes in scanner hardware and acquisition sequences across study phases. Because of HCP’s widespread usage, even modest protocol differences between cohorts–and their downstream effects–can have outsized impacts on the field of neuroscience research. Our analysis reveals that the HCP-YA cohort exhibits systematically weaker temporal signal-to-noise ratio (tSNR) relative to HCP-D/A. These signal quality discrepancies propagate to downstream analyses, leading to differences in overall resting-state functional correlations and whole-brain and node-level measures of resting-state network organization (e.g., system segregation, modularity, participation coefficient). Consistent with protocol-driven signal differences, resting-state network measures derived from HCP-YA depart from expected lifespan trajectories, as confirmed by examination of two other lifespan datasets. Harmonization approaches accounting for protocol and scanner-model differences substantially lessen these artifactual differences in brain network measures. Our findings underscore that signal differences do not merely introduce noise, but can qualitatively alter estimated lifespan trajectories of functional network organization, including partially inverting expected lifespan patterns. Without appropriate harmonization, analyses that combine HCP cohorts can therefore result in biologically misleading inferences about brain development and aging. We demonstrate how small acquisition differences bias resting-state-derived network metrics, and how these effects can be mitigated. This work advances best practices for valid inference in multi-cohort lifespan neuroscience research.

Background: Interpretations of brain and cognitive reserve were tested by assessing the parallel, longitudinal relationship between grey matter atrophy and cognitive decline in an adult lifespan sample.Method: Cognitive battery and structural MRI images were assessed in 463 healthy participants of age 20‐89. Participants were assessed up to 3 times over up to 10 years. Change‐over‐time in cortical grey mater, subcortical grey matter, and general cognitive ability were modeled via latent growth curve modeling, and covariance of these latent change scores were assessed via parallel‐process latent change modeling.Result: Latent change in general cognition was found to significantly covary with latent change in both cortical grey matter. Cortical grey matter volume, subcortical grey matter volume, and cognition at baseline were found to significantly relate to longitudinal cognitive decline, such that individuals with higher volume and better cognitive performance experienced less decline over the observation period.Conclusion: Individual differences in cognitive and grey matter decline are demonstrably related. Both cognitive and grey matter resources at baseline reserve to differentiate longitudinal patterns of cognitive decline. Grey matter volume appears to provide a good estimate of what some refer to as “brain reserve” and the latent cognition factor estimated “cognitive reserve.” Overall, these latent measures may be parsimoniously viewed as measures of neoro‐cognitive resource that do not require invoking the construct of “reserve” to explain individual differences in brain structure and cognition.

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.

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open and inclusive environment. Departing from the formats of typical scientific workshops, these events are based on grassroots projects and training, and foster open and reproducible scientific practices. We describe here the multifaceted, lasting benefits of Brainhacks for individual participants, particularly early career researchers. We further highlight the unique contributions that Brainhacks can make to the research community, contributing to scientific progress by complementing opportunities available in conventional formats.

Affect inductions have become essential for testing theories of affect and for conducting experimental research on the effects of mood and emotion. The current review takes stock of the vast body of existing literature on affect induction procedures (AIPs; also referred to as mood inductions) to evaluate the effectiveness of affect inductions as research tools and to test theories of affect (e.g., the bipolarity hypothesis, negativity bias, positivity offset, and theories of emotionality and gender) using meta-analytic data. In doing so, we seek to address whether AIPs are effective for inducing affective states, what conditions maximize their effectiveness, for which emotions they are most effective, for whom they are most effective, and whether affect induction findings can provide insight into theories of affect. A meta-analysis of 874 samples and 53,509 participants suggests that affect inductions are effective on average (δ = 1.32), but this effectiveness varies with the type of affect induction, the emotion being induced, and the gender of the participants. Further, results indicate coupled activation where the induction of positive (negative) emotions leads to a corresponding reduction in negative (positive) emotions, which provides support for the bipolar continuum of positive and negative affect. Results also revealed a negativity bias in which individuals display stronger reactions to negative stimuli than positive stimuli. A practical guide in the choice of affect induction procedures for researchers is presented and implications for emotion theory are discussed. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Significance: An individual’s socioeconomic status (SES) is a central feature of their environmental surroundings and has been shown to relate to the development and maturation of their brain in childhood. Here, we demonstrate that an individual’s present (adult) SES relates to their brain function and anatomy across a broad range of middle-age adulthood. In middle-aged adults (35–64 years), lower SES individuals exhibit less organized functional brain networks and reduced cortical thickness compared with higher SES individuals. These relationships cannot be fully explained by differences in health, demographics, or cognition. Additionally, childhood SES does not explain the relation between SES and brain network organization. These observations provide support for a powerful relationship between the environment and the brain that is evident in adult middle age.

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 STATEMENTSimilar 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.