Douglas D. Garrett

The Importance of Being Variable

New work suggests that blood oxygen level-dependent (BOLD) signal variability can be a much more powerful index of human age than mean activation, and that older brains are actually less variable than younger brains. However, little is known of how BOLD variability and task performance may relate. In the current study, we examined BOLD variability in relation to age, and reaction time speed and consistency in healthy younger (20–30 years) and older (56–85 years) adults on three cognitive tasks (perceptual matching, attentional cueing, and delayed match-to-sample). Results indicated that younger, faster, and more consistent performers exhibited significantly higher brain variability across tasks, and showed greater variability-based regional differentiation compared with older, poorer-performing adults. Also, when we compared brain variability- and typical mean-based effects, the respective spatial patterns were essentially orthogonal across brain measures, and any regions that did overlap were largely opposite in directionality of effect. These findings help establish the functional basis of BOLD variability, and further support the statistical and spatial differentiation between BOLD variability and BOLD mean. We thus argue that the precise nature of relations between aging, cognition, and brain function is underappreciated by using mean-based brain measures exclusively.

Blood Oxygen Level-Dependent Signal Variability Is More than Just Noise

Functional magnetic resonance imaging (fMRI) research often attributes blood oxygen level-dependent (BOLD) signal variance to measurement-related confounds. However, what is typically considered “noise” variance in data may be a vital feature of brain function. We examined fMRI signal variability during fixation baseline periods, and then compared SD- and mean-based spatial patterns and their relations with chronological age (20–85 years). We found that not only was the SD-based pattern robust, it differed greatly, both spatially and statistically, from the mean-based pattern. Notably, the unique age-predictive power of the SD-based pattern was more than five times that of the mean-based pattern. This reliable SD-based pattern of activity highlights an important “signal” within what is often considered measurement-related “noise.” We suggest that examination of BOLD signal variability may reveal a host of novel brain-related effects not previously considered in neuroimaging research.

Moment-to-moment brain signal variability: a next frontier in human brain mapping?

Neuroscientists have long observed that brain activity is naturally variable from moment-to-moment, but neuroimaging research has largely ignored the potential importance of this phenomenon. An emerging research focus on within-person brain signal variability is providing novel insights, and offering highly predictive, complementary, and even orthogonal views of brain function in relation to human lifespan development, cognitive performance, and various clinical conditions. As a result, brain signal variability is evolving as a bona fide signal of interest, and should no longer be dismissed as meaningless noise when mapping the human brain.

Neurocognitive markers of cognitive impairment: exploring the roles of speed and inconsistency.

A well-known challenge for research in the cognitive neuropsychology of aging is to distinguish between the deficits and changes associated with normal aging and those indicative of early cognitive impairment. In a series of 2 studies, the authors explored whether 2 neurocognitive markers, speed (mean level) and inconsistency (intraindividual variability), distinguished between age groups (64-73 and 74-90+ years) and cognitive status groups (nonimpaired, mildly impaired, and moderately impaired). Study 1 (n = 416) showed that both level and inconsistency distinguished between the age and 2 cognitive status (not impaired, mildly impaired) groups, with a modest tendency for inconsistency to predict group membership over and above mean level. Study 2 (n = 304) replicated these results but extended them because of the qualifying effects associated with the unique moderately impaired oldest group. Specifically, not only were the groups more firmly distinguished by both indicators of speed, but evidence for the differential contribution of performance inconsistency was stronger. Neurocognitive markers of speed and inconsistency may be leading indicators of emerging cognitive impairment.Supervisory Committee Dr. Kimberly Kerns, Department of Psychology Supervisor Dr. Mauricio Garcia-Barrera, Department of Psychology Departmental Member – Revision Supervisor Dr. John Walsh, Department of Educational Psychology and Leadership Studies External Member Dr. Leslie Saxon, Department of Linguistics Additional Member Abstract The purpose of the current study was to examine the impact of working memory on speech monitoring processes in the primary language of school-age children using the framework of Levelt’s Perceptual Loop Theory of speech production (1983). A community sample of eight children aged 6-8 and fourteen children aged 10-12 completed 4 verbal description tasks under different conditions; control, working memory load, white noise and combined working memory load and white noise. Participants also completed measures of listening span, digit span and spatial span. The results indicate that with increasing working memory load, children make significantly more speech errors, silent pauses and repetitions. No relationship was found between working memory and total repairs per errors or between working memory and total number of editing terms used. Group differences across the conditions were not significant; however, age-related trends were notable. Younger children had greater difficulty monitoring their speech with the introduction of working memory load; whereas, older children had greater difficulty with the introduction of white noise. A revised speech production model incorporating aspects of working memory is recommended and implications for clinical populations are discussed.The purpose of the current study was to examine the impact of working memory on speech monitoring processes in the primary language of school-age children using the framework of Levelt’s Perceptual Loop Theory of speech production (1983). A community sample of eight children aged 6-8 and fourteen children aged 10-12 completed 4 verbal description tasks under different conditions; control, working memory load, white noise and combined working memory load and white noise. Participants also completed measures of listening span, digit span and spatial span. The results indicate that with increasing working memory load, children make significantly more speech errors, silent pauses and repetitions. No relationship was found between working memory and total repairs per errors or between working memory and total number of editing terms used. Group differences across the conditions were not significant; however, age-related trends were notable. Younger children had greater difficulty monitoring their speech with the introduction of working memory load; whereas, older children had greater difficulty with the introduction of white noise. A revised speech production model incorporating aspects of working memory is recommended and implications for clinical populations are discussed.

The Modulation of BOLD Variability between Cognitive States Varies by Age and Processing Speed

Increasing evidence suggests that brain variability plays a number of important functional roles for neural systems. However, the relationship between brain variability and changing cognitive demands remains understudied. In the current study, we demonstrate experimental condition-based modulation in brain variability using functional magnetic resonance imaging. Within a sample of healthy younger and older adults, we found that blood oxygen level-dependent signal variability was an effective discriminator between fixation and external cognitive demand. Across a number of regions, brain variability increased broadly on task compared with fixation, particularly in younger and faster performing adults. Conversely, older and slower performing adults exhibited fewer changes in brain variability within and across experimental conditions and brain regions, indicating a reduction in variability-based neural specificity. Increases in brain variability on task may represent a more complex neural system capable of greater dynamic range between brain states, as well as an enhanced ability to efficiently process varying and unexpected external stimuli. The current results help establish the developmental and performance correlates of state-to-state brain variability-based transitions and offer a new line of inquiry in the study of rest versus task modes in the human brain.

Cognitive decline in high-functioning older adults: reserve or ascertainment bias?

The detection of mild cognitive impairment and dementia in high-functioning older adults can be difficult. It has also been observed that high-functioning persons show a lower prevalence of dementia than low-functioning persons. Three alternative explanations for this observation have been proposed in the literature: brain reserve capacity (BRC), cognitive reserve, and ascertainment bias. With data from a prospective, population-based study of incident dementia, the Canadian Study of Health and Aging (CSHA), we classified participants as being high- (HF) or low-functioning (LF) in three ways: educational and occupational attainment, and estimated premorbid IQ. We observed that fewer HF older adults were diagnosed with dementia after five years, which is in accordance with both the BRC and cognitive reserve models. Contrary to expectations, no difference on rate of memory deterioration was observed between those HF and LF persons who exhibited mild cognitive impairment at CSHA-1. However, HF persons who subsequently were diagnosed with dementia (CSHA-2) showed more rapid decline on five of the six memory measures over time than did LF persons diagnosed with dementia at CSHA-2. When performance on measures of memory functioning at CSHA-1 was examined for highly educated older adults, significantly more of those with dementia at CSHA-2 (n = 59) had scores falling within or below the average range in comparison to normative standards than those who continued to show no cognitive impairment (n = 145). Our findings suggest that the lower incidence of dementia for HF persons may be primarily the result of ascertainment bias, not underlying differences in brain or cognitive reserve.

Understanding variability in the BOLD signal and why it matters for aging

Recent work in neuroscience supports the idea that variability in brain function is necessary for optimal brain responsivity to a changing environment. In this review, we discuss a series of functional magnetic resonance imaging (fMRI) studies in younger and older adults to assess age-related differences in variability of the fMRI signal. This work shows that moment-to-moment brain signal variability represents an important “signal” within what is typically considered measurement-related “noise” in fMRI. This accumulation of evidence suggests that moving beyond the mean will provide a complementary window into aging-related neural processes.

Brain Signal Variability is Parametrically Modifiable

Moment-to-moment brain signal variability is a ubiquitous neural characteristic, yet remains poorly understood. Evidence indicates that heightened signal variability can index and aid efficient neural function, but it is not known whether signal variability responds to precise levels of environmental demand, or instead whether variability is relatively static. Using multivariate modeling of functional magnetic resonance imaging-based parametric face processing data, we show here that within-person signal variability level responds to incremental adjustments in task difficulty, in a manner entirely distinct from results produced by examining mean brain signals. Using mixed modeling, we also linked parametric modulations in signal variability with modulations in task performance. We found that difficulty-related reductions in signal variability predicted reduced accuracy and longer reaction times within-person; mean signal changes were not predictive. We further probed the various differences between signal variance and signal means by examining all voxels, subjects, and conditions; this analysis of over 2 million data points failed to reveal any notable relations between voxel variances and means. Our results suggest that brain signal variability provides a systematic task-driven signal of interest from which we can understand the dynamic function of the human brain, and in a way that mean signals cannot capture.

A Scaffold for Efficiency in the Human Brain

The comprehensive relations between healthy adult human brain white matter (WM) microstructure and gray matter (GM) function, and their joint relations to cognitive performance, remain poorly understood. We investigated these associations in 27 younger and 28 older healthy adults by linking diffusion tensor imaging (DTI) with functional magnetic resonance imaging (fMRI) data collected during an n-back working memory task. We present a novel application of multivariate Partial Least Squares (PLS) analysis that permitted the simultaneous modeling of relations between WM integrity values from all major WM tracts and patterns of condition-related BOLD signal across all GM regions. Our results indicate that greater microstructural integrity of the major WM tracts was negatively related to condition-related blood oxygenation level-dependent (BOLD) signal in task-positive GM regions. This negative relationship suggests that better quality of structural connections allows for more efficient use of task-related GM processing resources. Individuals with more intact WM further showed greater BOLD signal increases in typical “task-negative” regions during fixation, and notably exhibited a balanced magnitude of BOLD response across task-positive and -negative states. Structure—function relations also predicted task performance, including accuracy and speed of responding. Finally, structure–function–behavior relations reflected individual differences over and above chronological age. Our findings provide evidence for the role of WM microstructure as a scaffold for the context-relevant utilization of GM regions.

Driving as an Everyday Competence: A Model of Driving Competence and Behavior

Through a review of the literature on driving models, on models of everyday competence, and on older drivers, we developed a novel model of older drivers. Our proposed Driving as an Everyday Competence (DEC) model, which incorporates both driving competence and performance, was reviewed and critiqued by a group of experts. Our model suggests that the level of driving competence is determined by the interaction between individual and environment and is moderated by beliefs and awareness, leading to strategic level decisions regarding driving behaviors. Decisions made at the strategic, tactical, and operational levels must be viewed within the social/physical environmental context if driving performance is to be fully understood. The DEC model is a comprehensive model of older drivers and provides a foundation for the advancement of research on older drivers.