Dynamic Recovery: GABA Agonism Restores Neural Variability in Older, Poorer Performing Adults.

Aging is associated with cognitive impairment, but there are large individual differences in these declines. One neural measure that is lower in older adults and predicts these individual differences is moment-to-moment brain signal variability. Testing the assumption that GABA should heighten neural variability, we examined whether reduced brain signal variability in older, poorer performing adults could be boosted by increasing GABA pharmacologically. Brain signal variability was estimated using fMRI in 20 young and 24 older healthy human adults during placebo and GABA agonist sessions. As expected, older adults exhibited lower signal variability at placebo, and, crucially, GABA agonism boosted older adults' variability to the levels of young adults. Furthermore, poorer performing older adults experienced a greater increase in variability on drug, suggesting that those with more to gain benefit the most from GABA system potentiation. GABA may thus serve as a core neurochemical target in future work on aging- and cognition-related human brain dynamics.SIGNIFICANCE STATEMENT Prior research indicates that moment-to-moment brain signal variability is lower in older, poorer performing adults. We found that this reduced brain signal variability could be boosted through GABA agonism in older adults to the levels of young adults and that this boost was largest in the poorer performing older adults. These results provide the first evidence that brain signal variability can be restored by increasing GABAergic activity and suggest the promise of developing interventions targeting inhibitory systems to help slow cognitive declines in healthy aging.

Person-specific and precision neuroimaging: Current methods and future directions.

Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.

Age-Related Differences in White Matter: Understanding Tensor-Based Results Using Fixel-Based Analysis.

Aging is associated with widespread alterations in cerebral white matter (WM). Most prior studies of age differences in WM have used diffusion tensor imaging (DTI), but typical DTI metrics (e.g., fractional anisotropy; FA) can reflect multiple neurobiological features, making interpretation challenging. Here, we used fixel-based analysis (FBA) to investigate age-related WM differences observed using DTI in a sample of 45 older and 25 younger healthy adults. Age-related FA differences were widespread but were strongly associated with differences in multi-fiber complexity (CX), suggesting that they reflected differences in crossing fibers in addition to structural differences in individual fiber segments. FBA also revealed a frontolimbic locus of age-related effects and provided insights into distinct microstructural changes underlying them. Specifically, age differences in fiber density were prominent in fornix, bilateral anterior internal capsule, forceps minor, body of the corpus callosum, and corticospinal tract, while age differences in fiber cross section were largest in cingulum bundle and forceps minor. These results provide novel insights into specific structural differences underlying major WM differences associated with aging.

Age differences in functional network reconfiguration with working memory training.

Demanding cognitive functions like working memory (WM) depend on functional brain networks being able to communicate efficiently while also maintaining some degree of modularity. Evidence suggests that aging can disrupt this balance between integration and modularity. In this study, we examined how cognitive training affects the integration and modularity of functional networks in older and younger adults. Twenty three younger and 23 older adults participated in 10 days of verbal WM training, leading to performance gains in both age groups. Older adults exhibited lower modularity overall and a greater decrement when switching from rest to task, compared to younger adults. Interestingly, younger but not older adults showed increased task-related modularity with training. Furthermore, whereas training increased efficiency within, and decreased participation of, the default-mode network for younger adults, it enhanced efficiency within a task-specific salience/sensorimotor network for older adults. Finally, training increased segregation of the default-mode from frontoparietal/salience and visual networks in younger adults, while it diffusely increased between-network connectivity in older adults. Thus, while younger adults increase network segregation with training, suggesting more automated processing, older adults persist in, and potentially amplify, a more integrated and costly global workspace, suggesting different age-related trajectories in functional network reorganization with WM training.

Effects of self-transcendence on neural responses to persuasive messages and health behavior change.

Self-transcendence refers to a shift in mindset from focusing on self-interests to the well-being of others. We offer an integrative neural model of self-transcendence in the context of persuasive messaging by examining the mechanisms of self-transcendence in promoting receptivity to health messages and behavior change. Specifically, we posited that focusing on values and activities that transcend the self can allow people to see that their self-worth is not tied to a specific behavior in question, and in turn become more receptive to subsequent, otherwise threatening health information. To test whether inducing self-transcendent mindsets before message delivery would help overcome defensiveness and increase receptivity, we used two priming tasks, affirmation and compassion, to elicit a transcendent mindset among 220 sedentary adults. As preregistered, those who completed a self-transcendence task before health message exposure, compared with controls, showed greater increases in objectively logged levels of physical activity throughout the following month. In the brain, self-transcendence tasks up-regulated activity in a region of the ventromedial prefrontal cortex, chosen for its role in positive valuation and reward processing. During subsequent health message exposure, self-transcendence priming was associated with increased activity in subregions of the ventromedial prefrontal cortex, implicated in self-related processing and positive valuation, which predicted later decreases in sedentary behavior. The present findings suggest that having a positive self-transcendent mindset can increase behavior change, in part by increasing neural receptivity to health messaging.

Higher performers upregulate brain signal variability in response to more feature-rich visual input.

The extent to which brain responses differ across varying cognitive demands is referred to as "neural differentiation," and greater neural differentiation has been associated with better cognitive performance in older adults. An emerging approach has examined within-person neural differentiation using moment-to-moment brain signal variability. A number of studies have found that brain signal variability differs by cognitive state; however, the factors that cause signal variability to rise or fall on a given task remain understudied. We hypothesized that top performers would modulate signal variability according to the complexity of sensory input, upregulating variability when processing more feature-rich stimuli. In the current study, 46 older adults passively viewed face and house stimuli during fMRI. Low-level analyses showed that house images were more feature-rich than faces, and subsequent computational modelling of ventral visual stream responses (HMAX) revealed that houses were more feature-rich especially in V1/V2-like model layers. Notably, we then found that participants exhibiting greater face-to-house upregulation of brain signal variability in V1/V2 (higher for house relative to face stimuli) also exhibited more accurate, faster, and more consistent behavioral performance on a battery of offline visuo-cognitive tasks. Further, control models revealed that face-house modulation of mean brain signal was relatively insensitive to offline cognition, providing further evidence for the importance of brain signal variability for understanding human behavior. We conclude that the ability to align brain signal variability to the richness of perceptual input may mark heightened trait-level behavioral performance in older adults.

Neural correlates of working memory training: Evidence for plasticity in older adults.

Brain activity typically increases with increasing working memory (WM) load, regardless of age, before reaching an apparent ceiling. However, older adults exhibit greater brain activity and reach ceiling at lower loads than younger adults, possibly reflecting compensation at lower loads and dysfunction at higher loads. We hypothesized that WM training would bolster neural efficiency, such that the activation peak would shift towards higher memory loads after training. Pre-training, older adults showed greater recruitment of the WM network than younger adults across all loads, with decline at the highest load. Ten days of adaptive training on a verbal WM task improved performance and led to greater brain responsiveness at higher loads for both groups. For older adults the activation peak shifted rightward towards higher loads. Finally, training increased task-related functional connectivity in older adults, both within the WM network and between this task-positive network and the task-negative/default-mode network. These results provide new evidence for functional plasticity with training in older adults and identify a potential signature of improvement at the neural level.

Network segregation varies with neural distinctiveness in sensorimotor cortex.

Normal aging is associated with declines in sensorimotor function. Previous studies have linked age-related behavioral declines to decreases in neural differentiation (i.e., dedifferentiation), including decreases in the distinctiveness of neural activation patterns and in the segregation of large-scale neural networks at rest. However, no studies to date have explored the relationship between these two neural measures and whether they explain the same aspects of behavior. To investigate these issues, we collected a battery of sensorimotor behavioral measures in older and younger adults and estimated (a) the distinctiveness of neural representations in sensorimotor cortex and (b) sensorimotor network segregation in the same participants. Consistent with prior findings, sensorimotor representations were less distinct and sensorimotor resting state networks were less segregated in older compared to younger adults. We also found that participants with the most distinct sensorimotor representations exhibited the most segregated sensorimotor networks. However, only sensorimotor network segregation was associated with individual differences in sensorimotor performance, particularly in older adults. These novel findings link network segregation to neural distinctiveness, but also suggest that network segregation may play a larger role in maintaining sensorimotor performance with age.

Independent Components of Neural Activation Associated with 100 Days of Cognitive Training.

Some cognitive training studies have reported working memory benefits that generalize beyond the trained tasks, whereas others have only found task-specific training effects. What brain networks are associated with general training effects, rather than task-specific effects? We investigated this question in the context of working memory training using the COGITO data set, a longitudinal project including behavioral assessments before and after 100 days of cognitive training in 101 younger (20-31 years) and 103 older (65-80 years) adults. Pre- and postassessments included verbal, numerical, and spatial measures of working memory. It was therefore possible to assess training effects on working memory at a general latent ability level. Previous analyses of these data found training-related improvements on this latent working memory factor in both young and old participants. fMRI data were collected from a subsample of participants (24 young and 15 old) during pre- and post-training sessions. We used independent component analysis to identify networks involved in a perceptual decision-making task performed in the scanner. We identified five task-positive components that were task-related: two frontal networks, a ventral visual network, a motor network, and a cerebellar network. Pre-training activity of the motor network predicted latent working memory performance before training. Additionally, activity in the motor network predicted training-related changes in working memory ability. These findings suggest activity in the motor network plays a role in task-independent working memory improvements and have implications for our understanding of working memory training and for the design and implementation of future training interventions.

Neural distinctiveness declines with age in auditory cortex and is associated with auditory GABA levels.

Neural activation patterns in the ventral visual cortex in response to different categories of visual stimuli (e.g., faces vs. houses) are less selective, or distinctive, in older adults than in younger adults, a phenomenon known as age-related neural dedifferentiation. In this study, we investigated whether neural dedifferentiation extends to the auditory cortex. Inspired by previous animal work, we also investigated whether individual differences in GABA are associated with individual differences in neural distinctiveness in humans. 20 healthy young adults (ages 18-29) and 23 healthy older adults (over 65) completed a functional magnetic resonance imaging (fMRI) scan, during which neural activity was estimated while they listened to music and foreign speech. GABA levels in the auditory, ventrovisual and sensorimotor cortex were estimated in the same individuals in a separate magnetic resonance spectroscopy (MRS) scan. Relative to the younger adults, the older adults exhibited both (1) less distinct activation patterns for music vs. speech stimuli and (2) lower GABA levels in the auditory cortex. Also, individual differences in auditory GABA levels (but not ventrovisual or sensorimotor GABA levels) were associated with individual differences in neural distinctiveness in the auditory cortex in the older adults. These results demonstrate that age-related neural dedifferentiation extends to the auditory cortex and suggest that declining GABA levels may play a role in neural dedifferentiation in older adults.

Sensorimotor network segregation declines with age and is linked to GABA and to sensorimotor performance.

Aging is typically associated with declines in sensorimotor performance. Previous studies have linked some age-related behavioral declines to reductions in network segregation. For example, compared to young adults, older adults typically exhibit weaker functional connectivity within the same functional network but stronger functional connectivity between different networks. Based on previous animal studies, we hypothesized that such reductions of network segregation are linked to age-related reductions in the brain's major inhibitory transmitter, gamma aminobutyric acid (GABA). To investigate this hypothesis, we conducted graph theoretical analyses of resting state functional MRI data to measure sensorimotor network segregation in both young and old adults. We also used magnetic resonance spectroscopy to measure GABA levels in the sensorimotor cortex and collected a battery of sensorimotor behavioral measures. We report four main findings. First, relative to young adults, old adults exhibit both less segregated sensorimotor brain networks and reduced sensorimotor GABA levels. Second, less segregated networks are associated with lower GABA levels. Third, less segregated networks and lower GABA levels are associated with worse sensorimotor performance. Fourth, network segregation mediates the relationship between GABA and performance. These findings link age-related differences in network segregation to age-related differences in GABA levels and sensorimotor performance. More broadly, they suggest a neurochemical substrate of age-related dedifferentiation at the level of large-scale brain networks.

Michigan Neural Distinctiveness (MiND) study protocol: investigating the scope, causes, and consequences of age-related neural dedifferentiation.

BACKGROUND: Aging is often associated with behavioral impairments, but some people age more gracefully than others. Why? One factor that may play a role is individual differences in the distinctiveness of neural representations. Previous research has found that neural activation patterns in visual cortex in response to different visual stimuli are often more similar (i.e., less distinctive) in older vs. young participants, a phenomenon referred to as age-related neural dedifferentiation. Furthermore, older people whose neural representations are less distinctive tend to perform worse on a wide range of behavioral tasks. The Michigan Neural Distinctiveness (MiND) project aims to investigate the scope of neural dedifferentiation (e.g., does it also occur in auditory, motor, and somatosensory cortex?), one potential cause (age-related reductions in the inhibitory neurotransmitter gamma-aminobutyric acid (GABA)), and the behavioral consequences of neural dedifferentiation. This protocol paper describes the study rationale and methods being used in complete detail, but not the results (data collection is currently underway). METHODS: The MiND project consists of two studies: the main study and a drug study. In the main study, we are recruiting 60 young and 100 older adults to perform behavioral tasks that measure sensory and cognitive function. They also participate in functional MRI (fMRI), MR spectroscopy, and diffusion weighted imaging sessions, providing data on neural distinctiveness and GABA concentrations. In the drug study, we are recruiting 25 young and 25 older adults to compare neural distinctiveness, measured with fMRI, after participants take a placebo or a benzodiazepine (lorazepam) that should increase GABA activity. DISCUSSION: By collecting multimodal imaging measures along with extensive behavioral measures from the same subjects, we are linking individual differences in neurochemistry, neural representation, and behavioral performance, rather than focusing solely on group differences between young and old participants. Our findings have the potential to inform new interventions for age-related declines. TRIAL REGISTRATION: This study was retrospectively registered with the ISRCTN registry on March 4, 2019. The registration number is ISRCTN17266136 .

Human aging alters the neural computation and representation of space.

The hippocampus and striatum are core neural circuits involved in spatial learning and memory. Although both neural systems support spatial navigation, experimental and theoretical evidence indicate that they play different roles. In particular, whereas hippocampal place cells generate allocentric neural representations of space that are sensitive to geometric information, striatum-dependent learning is influenced by local landmarks. How human aging affects these different neural representations, however, is still not well understood. In this paper, we combined virtual reality, computational modeling, and neuroimaging to investigate the effects of age upon the neural computation and representation of space in humans. We manipulated the geometry and local landmarks of a virtual environment and examined the effects on memory performance and brain activity during spatial learning. In younger adults, both behavior and brain activity in the medial-temporal lobe were consistent with predictions of a computational model of hippocampus-dependent boundary processing. In contrast, older adults' behavior and medial-temporal lobe activity were primarily influenced by local cue information, and spatial learning was more associated with activity in the caudate nucleus rather than the hippocampus. Together these results point to altered spatial representations and information processing in the hippocampal-striatal circuitry with advancing adult age, which may contribute to spatial learning and memory deficits associated with normal and pathological aging.

Parietal functional connectivity in numerical cognition.

The parietal cortex is central to numerical cognition. The right parietal region is primarily involved in basic quantity processing, while the left parietal region is additionally involved in precise number processing and numerical operations. Little is known about how the 2 regions interact during numerical cognition. We hypothesized that functional connectivity between the right and left parietal cortex is critical for numerical processing that engages both basic number representation and learned numerical operations. To test this hypothesis, we estimated neural activity using functional magnetic resonance imaging in participants performing numerical and arithmetic processing on dot arrays. We first found task-based functional connectivity between a right parietal seed and the left sensorimotor cortex in all task conditions. As we hypothesized, we found enhanced functional connectivity between this right parietal seed and both the left parietal cortex and neighboring right parietal cortex, particularly during subtraction. The degree of functional connectivity also correlated with behavioral performance across individual participants, while activity within each region did not. These results highlight the role of parietal functional connectivity in numerical processing. They suggest that arithmetic processing depends on crosstalk between and within the parietal cortex and that this crosstalk contributes to one's numerical competence.

Estimating individual trajectories of structural and cognitive decline in mild cognitive impairment for early prediction of progression to dementia of the Alzheimer's type.

Only a third of individuals with mild cognitive impairment (MCI) progress to dementia of the Alzheimer's type (DAT). Identifying biomarkers that distinguish individuals with MCI who will progress to DAT (MCI-Converters) from those who will not (MCI-Non-Converters) remains a key challenge in the field. In our study, we evaluate whether the individual rates of loss of volumes of the Hippocampus and entorhinal cortex (EC) with age in the MCI stage can predict progression to DAT. Using data from 758 MCI patients in the Alzheimer's Disease Neuroimaging Database, we employ Linear Mixed Effects (LME) models to estimate individual trajectories of regional brain volume loss over 12 years on average. Our approach involves three key analyses: (1) mapping age-related volume loss trajectories in MCI-Converters and Non-Converters, (2) using logistic regression to predict progression to DAT based on individual rates of hippocampal and EC volume loss, and (3) examining the relationship between individual estimates of these volumetric changes and cognitive decline across different cognitive functions-episodic memory, visuospatial processing, and executive function. We find that the loss of Hippocampal volume is significantly more rapid in MCI-Converters than Non-Converters, but find no such difference in EC volumes. We also find that the rate of hippocampal volume loss in the MCI stage is a significant predictor of conversion to DAT, while the rate of volume loss in the EC and other additional regions is not. Finally, individual estimates of rates of regional volume loss in both the Hippocampus and EC, and other additional regions, correlate strongly with individual rates of cognitive decline. Across all analyses, we find significant individual variation in the initial volumes and the rates of changes in volume with age in individuals with MCI. This study highlights the importance of personalized approaches in predicting AD progression, offering insights for future research and intervention strategies.

Neural broadening or neural attenuation? Investigating age-related dedifferentiation in the face network in a large lifespan sample.

Previous studies have found that cortical responses to different stimuli become less distinctive as people get older. This age-related dedifferentiation may reflect the broadening of the tuning curves of category-selective neurons (broadening hypothesis) or it may be due to decreased activation of category-selective neurons (attenuation hypothesis). In this study, we evaluated these hypotheses in the context of the face-selective neural network. Over 300 participants, ranging in age from 20 to 89 years, viewed images of faces, houses, and control stimuli in a functional magnetic resonance imaging session. Regions within the core face network and extended face network were identified in individual subjects. Activation in many of these regions became significantly less face-selective with age, confirming previous reports of age-related dedifferentiation. Consistent with the broadening hypothesis, this dedifferentiation in the fusiform face area (FFA) was driven by increased activation to houses. In contrast, dedifferentiation in the extended face network was driven by decreased activation to faces, consistent with the attenuation hypothesis. These results suggest that age-related dedifferentiation reflects distinct processes in different brain areas. More specifically, dedifferentiation in FFA activity may be due to broadening of the tuning curves for face-selective neurons, while dedifferentiation in the extended face network reflects reduced face- or emotion-selective activity.

The behavioral and neural effects of parietal theta burst stimulation on the grasp network are stronger during a grasping task than at rest.

Repetitive transcranial magnetic stimulation (TMS) is widely used in neuroscience and clinical settings to modulate human cortical activity. The effects of TMS on neural activity depend on the excitability of specific neural populations at the time of stimulation. Accordingly, the brain state at the time of stimulation may influence the persistent effects of repetitive TMS on distal brain activity and associated behaviors. We applied intermittent theta burst stimulation (iTBS) to a region in the posterior parietal cortex (PPC) associated with grasp control to evaluate the interaction between stimulation and brain state. Across two experiments, we demonstrate the immediate responses of motor cortex activity and motor performance to state-dependent parietal stimulation. We randomly assigned 72 healthy adult participants to one of three TMS intervention groups, followed by electrophysiological measures with TMS and behavioral measures. Participants in the first group received iTBS to PPC while performing a grasping task concurrently. Participants in the second group received iTBS to PPC while in a task-free, resting state. A third group of participants received iTBS to a parietal region outside the cortical grasping network while performing a grasping task concurrently. We compared changes in motor cortical excitability and motor performance in the three stimulation groups within an hour of each intervention. We found that parietal stimulation during a behavioral manipulation that activates the cortical grasping network increased downstream motor cortical excitability and improved motor performance relative to stimulation during rest. We conclude that constraining the brain state with a behavioral task during brain stimulation has the potential to optimize plasticity induction in cortical circuit mechanisms that mediate movement processes.

Neural dissociation of number from letter recognition and its relationship to parietal numerical processing.

The visual recognition of letters dissociates from the recognition of numbers at both the behavioral and neural level. In this article, using fMRI, we investigate whether the visual recognition of numbers dissociates from letters, thereby establishing a double dissociation. In Experiment 1, participants viewed strings of consonants and Arabic numerals. We found that letters activated the left midfusiform and inferior temporal gyri more than numbers, replicating previous studies, whereas numbers activated a right lateral occipital area more than letters at the group level. Because the distinction between letters and numbers is culturally defined and relatively arbitrary, this double dissociation provides some of the strongest evidence to date that a neural dissociation can emerge as a result of experience. We then investigated a potential source of the observed neural dissociation. Specifically, we tested the hypothesis that lateralization of visual number recognition depends on lateralization of higher-order numerical processing. In Experiment 2, the same participants performed addition, subtraction, and counting on arrays of nonsymbolic stimuli varying in numerosity, which produced neural activity in and around the intraparietal sulcus, a region associated with higher-order numerical processing. We found that individual differences in the lateralization of number activity in visual cortex could be explained by individual differences in the lateralization of numerical processing in parietal cortex, suggesting a functional relationship between the two regions. Together, these results demonstrate a neural double dissociation between letter and number recognition and suggest that higher-level numerical processing in parietal cortex may influence the neural organization of number processing in visual cortex.

Correlation and heritability in neuroimaging datasets: a spatial decomposition approach with application to an fMRI study of twins.

Advances in modern neuroimaging in combination with behavioral genetics have allowed neuroscientists to investigate how genetic and environmental factors shape human brain structure and function. Estimating the heritability of brain structure and function via twin studies has become one of the major approaches in studying the genetics of the brain. In a classical twin study, heritability is estimated by computing genetic and phenotypic variation based on the similarity of monozygotic and dizygotic twins. However, heritability has traditionally been measured for univariate, scalar traits, and it is challenging to assess the heritability of a spatial process, such as a pattern of neural activity. In this work, we develop a statistical method to estimate phenotypic variance and covariance at each location in a spatial process, which in turn can be used to estimate the heritability of a spatial dataset. The method is based on a dimensionally-reduced model of spatial variation in paired images, in which adjusted least squares estimates can be used to estimate the key model parameters. The advantage of the proposed method compared to conventional methods such as voxelwise or mean-ROI approaches is demonstrated in both a simulation study and a real data study assessing genetic influence on patterns of brain activity in the visual and motor cortices in response to a simple visuomotor task.

Amygdala response to smoking-cessation messages mediates the effects of serotonin transporter gene variation on quitting.

The amygdala is critically involved in detecting emotionally salient stimuli and in enhancing memory for emotional information. Growing evidence also suggests that the amygdala plays a crucial role in addiction, perhaps by strengthening associations between emotionally-charged drug cues and drug-seeking behavior. In the current study, by integrating functional MRI (fMRI), genetics, and outcome data from a large group of smokers who completed a smoking-cessation intervention and attempted to quit, we show that the amygdala also plays a role in quitting. Specifically, we demonstrate that the amygdala response to smoking-cessation messages in smokers trying to quit is a predictor of their post-intervention quitting outcome. We further show that the amygdala response is modulated by genetic variation in the serotonin transporter and mediates the impact of this genetic variation on quitting. These results point to a gene-brain-behavior pathway relevant to smoking cessation, and add to our understanding of the role of the amygdala in nicotine addiction.