The impact of the human thalamus on brain-wide information processing.

The thalamus is a small, bilateral structure in the diencephalon that integrates signals from many areas of the CNS. This critical anatomical position allows the thalamus to influence whole-brain activity and adaptive behaviour. However, traditional research paradigms have struggled to attribute specific functions to the thalamus, and it has remained understudied in the human neuroimaging literature. Recent advances in analytical techniques and increased accessibility to large, high-quality data sets have brought forth a series of studies and findings that (re-)establish the thalamus as a core region of interest in human cognitive neuroscience, a field that otherwise remains cortico-centric. In this Perspective, we argue that using whole-brain neuroimaging approaches to investigate the thalamus and its interaction with the rest of the brain is key for understanding systems-level control of information processing. To this end, we highlight the role of the thalamus in shaping a range of functional signatures, including evoked activity, interregional connectivity, network topology and neuronal variability, both at rest and during the performance of cognitive tasks.

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.

Lost Dynamics and the Dynamics of Loss: Longitudinal Compression of Brain Signal Variability is Coupled with Declines in Functional Integration and Cognitive Performance.

Reduced moment-to-moment blood oxygen level-dependent (BOLD) signal variability has been consistently linked to advanced age and poorer cognitive performance, showing potential as a functional marker of brain aging. To date, however, this promise has rested exclusively on cross-sectional comparisons. In a sample of 74 healthy adults, we provide the first longitudinal evidence linking individual differences in BOLD variability, age, and performance across multiple cognitive domains over an average period of 2.5 years. As expected, those expressing greater loss of BOLD variability also exhibited greater decline in cognition. The fronto-striato-thalamic system emerged as a core neural substrate for these change-change associations. Preservation of signal variability within regions of the fronto-striato-thalamic system also cohered with preservation of functional integration across regions of this system, suggesting that longitudinal maintenance of "local" dynamics may require across-region communication. We therefore propose this neural system as a primary target in future longitudinal studies on the neural substrates of cognitive aging. Given that longitudinal change-change associations between brain and cognition are notoriously difficult to detect, the presence of such an association within a relatively short follow-up period bolsters the promise of brain signal variability as a viable, experimentally sensitive probe for studying individual differences in human cognitive aging.

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What's signal irregularity got to do with it?

Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its' presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time scales reflects signal irregularity at those precise time scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time scales. Specifically, we show that the typical definition of temporal patterns via "similarity bounds" biases coarse MSE scales-that are thought to reflect slow dynamics-by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time scales-presumed to indicate fast dynamics-is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched temporal scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time scales of interest.

Functional Connectivity within and beyond the Face Network Is Related to Reduced Discrimination of Degraded Faces in Young and Older Adults.

Degrading face stimuli reduces face discrimination in both young and older adults, but the brain correlates of this decline in performance are not fully understood. We used functional magnetic resonance imaging to examine the effects of degraded face stimuli on face and nonface brain networks and tested whether these changes would predict the linear declines seen in performance. We found decreased activity in the face network (FN) and a decrease in the similarity of functional connectivity (FC) in the FN across conditions as degradation increased but no effect of age. FC in whole-brain networks also changed with increasing degradation, including increasing FC between the visual network and cognitive control networks. Older adults showed reduced modulation of this whole-brain FC pattern. The strongest predictors of within-participant decline in accuracy were changes in whole-brain network FC and FC similarity of the FN. There was no influence of age on these brain-behavior relations. These results suggest that a systems-level approach beyond the FN is required to understand the brain correlates of performance decline when faces are obscured with noise. In addition, the association between brain and behavior changes was maintained into older age, despite the dampened FC response to face degradation seen in older adults.

Auditory-Articulatory Neural Alignment between Listener and Speaker during Verbal Communication.

Whether auditory processing of speech relies on reference to the articulatory motor information of speaker remains elusive. Here, we addressed this issue under a two-brain framework. Functional magnetic resonance imaging was applied to record the brain activities of speakers when telling real-life stories and later of listeners when listening to the audio recordings of these stories. Based on between-brain seed-to-voxel correlation analyses, we revealed that neural dynamics in listeners' auditory temporal cortex are temporally coupled with the dynamics in the speaker's larynx/phonation area. Moreover, the coupling response in listener's left auditory temporal cortex follows the hierarchical organization for speech processing, with response lags in A1+, STG/STS, and MTG increasing linearly. Further, listeners showing greater coupling responses understand the speech better. When comprehension fails, such interbrain auditory-articulation coupling vanishes substantially. These findings suggest that a listener's auditory system and a speaker's articulatory system are inherently aligned during naturalistic verbal interaction, and such alignment is associated with high-level information transfer from the speaker to the listener. Our study provides reliable evidence supporting that references to the articulatory motor information of speaker facilitate speech comprehension under a naturalistic scene.

Dopamine D2/3 Binding Potential Modulates Neural Signatures of Working Memory in a Load-Dependent Fashion.

Dopamine (DA) modulates corticostriatal connections. Studies in which imaging of the DA system is integrated with functional imaging during cognitive performance have yielded mixed findings. Some work has shown a link between striatal DA (measured by PET) and fMRI activations, whereas others have failed to observe such a relationship. One possible reason for these discrepant findings is differences in task demands, such that a more demanding task with greater prefrontal activations may yield a stronger association with DA. Moreover, a potential DA-BOLD association may be modulated by task performance. We studied 155 (104 normal-performing and 51 low-performing) healthy older adults (43% females) who underwent fMRI scanning while performing a working memory (WM) n-back task along with DA D2/3 PET assessment using [11C]raclopride. Using multivariate partial-least-squares analysis, we observed a significant pattern revealing positive associations of striatal as well as extrastriatal DA D2/3 receptors to BOLD response in the thalamo-striatal-cortical circuit, which supports WM functioning. Critically, the DA-BOLD association in normal-performing, but not low-performing, individuals was expressed in a load-dependent fashion, with stronger associations during 3-back than 1-/2-back conditions. Moreover, normal-performing adults expressing upregulated BOLD in response to increasing task demands showed a stronger DA-BOLD association during 3-back, whereas low-performing individuals expressed a stronger association during 2-back conditions. This pattern suggests a nonlinear DA-BOLD performance association, with the strongest link at the maximum capacity level. Together, our results suggest that DA may have a stronger impact on functional brain responses during more demanding cognitive tasks.SIGNIFICANCE STATEMENT Dopamine (DA) is a major neuromodulator in the CNS and plays a key role in several cognitive processes via modulating the blood oxygenation level-dependent (BOLD) signal. Some studies have shown a link between DA and BOLD, whereas others have failed to observe such a relationship. A possible reason for the discrepancy is differences in task demands, such that a more demanding task with greater prefrontal activations may yield a stronger association with DA. We examined the relationship of DA to BOLD response during working memory under three load conditions and found that the DA-BOLD association is expressed in a load-dependent fashion. These findings may help explain the disproportionate impairment evident in more effortful cognitive tasks in normal aging and in those suffering dopamine-dependent neurodegenerative diseases (e.g., Parkinson's disease).

Single-trial characterization of neural rhythms: Potential and challenges.

The average power of rhythmic neural responses as captured by MEG/EEG/LFP recordings is a prevalent index of human brain function. Increasing evidence questions the utility of trial-/group averaged power estimates however, as seemingly sustained activity patterns may be brought about by time-varying transient signals in each single trial. Hence, it is crucial to accurately describe the duration and power of rhythmic and arrhythmic neural responses on the single trial-level. However, it is less clear how well this can be achieved in empirical MEG/EEG/LFP recordings. Here, we extend an existing rhythm detection algorithm (extended Better OSCillation detection: "eBOSC"; cf. Whitten et al., 2011) to systematically investigate boundary conditions for estimating neural rhythms at the single-trial level. Using simulations as well as resting and task-based EEG recordings from a micro-longitudinal assessment, we show that alpha rhythms can be successfully captured in single trials with high specificity, but that the quality of single-trial estimates varies greatly between subjects. Despite those signal-to-noise-based limitations, we highlight the utility and potential of rhythm detection with multiple proof-of-concept examples, and discuss implications for single-trial analyses of neural rhythms in electrophysiological recordings. Using an applied example of working memory retention, rhythm detection indicated load-related increases in the duration of frontal theta and posterior alpha rhythms, in addition to a frequency decrease of frontal theta rhythms that was observed exclusively through amplification of rhythmic amplitudes.

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.

Neurocognitive Profiles of Older Adults with Working-Memory Dysfunction.

Individuals differ in how they perceive, remember, and think. There is evidence for the existence of distinct subgroups that differ in cognitive performance within the older population. However, it is less clear how individual differences in cognition in old age are linked to differences in brain-based measures. We used latent-profile analysis on n-back working-memory (WM) performance to identify subgroups in a large sample of older adults (n = 181; age = 64-68 years). Our analysis identified one larger normal subgroup with higher performance (n = 113; 63%), and a second smaller subgroup (n = 55; 31%) with lower performance. The low-performing subgroup showed weaker load-dependent BOLD modulation and lower connectivity within the fronto-parietal network (FPN) as well as between FPN and striatum during n-back, along with lower FPN connectivity at rest. This group also exhibited lower FPN structural integrity, lower frontal dopamine D2 binding potential, inferior performance on offline WM tests, and a trend-level genetic predisposition for lower dopamine-system efficiency. By contrast, this group exhibited relatively intact episodic memory and associated brain measures (i.e., hippocampal volume, structural, and functional connectivity within the default-mode network). Collectively, these data provide converging evidence for the existence of a group of older adults with impaired WM functioning characterized by reduced cortico-striatal coupling and aberrant cortico-cortical integrity within FPN.

Latent-Profile Analysis Reveals Behavioral and Brain Correlates of Dopamine-Cognition Associations.

Evidence suggests that associations between the neurotransmitter dopamine and cognition are nonmonotonic and open to modulation by various other factors. The functional implications of a given level of dopamine may therefore differ from person to person. By applying latent-profile analysis to a large (n = 181) sample of adults aged 64-68 years, we probabilistically identified 3 subgroups that explain the multivariate associations between dopamine D2/3R availability (probed with 11C-raclopride-PET, in cortical, striatal, and hippocampal regions) and cognitive performance (episodic memory, working memory, and perceptual speed). Generally, greater receptor availability was associated with better cognitive performance. However, we discovered a subgroup of individuals for which high availability, particularly in striatum, was associated with poor performance, especially for working memory. Relative to the rest of the sample, this subgroup also had lower education, higher body-mass index, and lower resting-state connectivity between caudate nucleus and dorsolateral prefrontal cortex. We conclude that a smaller subset of individuals induces a multivariate non-linear association between dopamine D2/3R availability and cognitive performance in this group of older adults, and discuss potential reasons for these differences that await further empirical scrutiny.

Dopamine D2 receptor availability is linked to hippocampal-caudate functional connectivity and episodic memory.

D1 and D2 dopamine receptors (D1DRs and D2DRs) may contribute differently to various aspects of memory and cognition. The D1DR system has been linked to functions supported by the prefrontal cortex. By contrast, the role of the D2DR system is less clear, although it has been hypothesized that D2DRs make a specific contribution to hippocampus-based cognitive functions. Here we present results from 181 healthy adults between 64 and 68 y of age who underwent comprehensive assessment of episodic memory, working memory, and processing speed, along with MRI and D2DR assessment with [(11)C]raclopride and PET. Caudate D2DR availability was positively associated with episodic memory but not with working memory or speed. Whole-brain analyses further revealed a relation between hippocampal D2DR availability and episodic memory. Hippocampal and caudate D2DR availability were interrelated, and functional MRI-based resting-state functional connectivity between the ventral caudate and medial temporal cortex increased as a function of caudate D2DR availability. Collectively, these findings indicate that D2DRs make a specific contribution to hippocampus-based cognition by influencing striatal and hippocampal regions, and their interactions.

Brain signal variability is modulated as a function of internal and external demand in younger and older adults.

Variability in the Blood Oxygen-Level Dependent (BOLD) signal from fMRI is often associated with better cognitive performance and younger age. It has been proposed that neural variability enables flexible responding to uncertainty in a changing environment. However, signal variability reflecting environmental uncertainty may reduce to the extent that a task depends on internally-directed attention and is supported by neural "solutions" that are schematic and relatively stable within each individual. Accordingly, we examined the hypothesis that BOLD variability will be low at rest, higher during internally-directed tasks, and higher still during externally-directed tasks, and that this effect will be reduced with aging. Modulation of BOLD variability across conditions was consistent with these hypotheses, and was associated with faster and more stable behavioral performance in both young and older adults. These data support the idea that brain signal variability may modulate in response to environmental uncertainty, which is presumed to be greater in the external environment than in the internal milieu. Reduced flexibility of signal variability with age may indicate less ability to switch between internal and external brain states.

Local temporal variability reflects functional integration in the human brain.

Local moment-to-moment variability exists at every level of neural organization, but its driving forces remain opaque. Inspired by animal work demonstrating that local temporal variability may reflect synaptic input rather than locally-generated "noise," we used publicly-available high-temporal-resolution fMRI data (N = 100 adults; 33 males) to test in humans whether greater BOLD signal variability in local brain regions was associated with functional integration (estimated via spatiotemporal PCA dimensionality). Using a multivariate partial least squares analysis, we indeed found that individuals with higher local temporal variability had a more integrated (lower dimensional) network fingerprint. Notably, temporal variability in the thalamus showed the strongest negative association with PCA dimensionality. Previous animal work also shows that local variability may upregulate from thalamus to visual cortex; however, such principled upregulation from thalamus to cortex has not been demonstrated in humans. In the current study, we rather establish a more general putative dynamic role of the thalamus by demonstrating that greater within-person thalamo-cortical upregulation in variability is itself a unique hallmark of greater functional integration that cannot be accounted for by local fluctuations in several other well-known integrative-hub regions. Our findings indicate that local variability primarily reflects functional integration, and establish a fundamental role for the thalamus in how the brain fluctuates and communicates across moments.

Amphetamine modulates brain signal variability and working memory in younger and older adults.

Better-performing younger adults typically express greater brain signal variability relative to older, poorer performers. Mechanisms for age and performance-graded differences in brain dynamics have, however, not yet been uncovered. Given the age-related decline of the dopamine (DA) system in normal cognitive aging, DA neuromodulation is one plausible mechanism. Hence, agents that boost systemic DA [such as d-amphetamine (AMPH)] may help to restore deficient signal variability levels. Furthermore, despite the standard practice of counterbalancing drug session order (AMPH first vs. placebo first), it remains understudied how AMPH may interact with practice effects, possibly influencing whether DA up-regulation is functional. We examined the effects of AMPH on functional-MRI-based blood oxygen level-dependent (BOLD) signal variability (SD(BOLD)) in younger and older adults during a working memory task (letter n-back). Older adults expressed lower brain signal variability at placebo, but met or exceeded young adult SD(BOLD) levels in the presence of AMPH. Drug session order greatly moderated change-change relations between AMPH-driven SD(BOLD) and reaction time means (RT(mean)) and SDs (RT(SD)). Older adults who received AMPH in the first session tended to improve in RT(mean) and RT(SD) when SD(BOLD) was boosted on AMPH, whereas younger and older adults who received AMPH in the second session showed either a performance improvement when SD(BOLD) decreased (for RT(mean)) or no effect at all (for RT(SD)). The present findings support the hypothesis that age differences in brain signal variability reflect aging-induced changes in dopaminergic neuromodulation. The observed interactions among AMPH, age, and session order highlight the state- and practice-dependent neurochemical basis of human brain dynamics.

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.

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.

Single-neuron spiking variability in hippocampus dynamically tracks sensory content during memory formation in humans.

During memory formation, the hippocampus is presumed to represent the content of stimuli, but how it does so is unknown. Using computational modelling and human single-neuron recordings, we show that the more precisely hippocampal spiking variability tracks the composite features of each individual stimulus, the better those stimuli are later remembered. We propose that moment-to-moment spiking variability may provide a new window into how the hippocampus constructs memories from the building blocks of our sensory world.

A spatiotemporal complexity architecture of human brain activity.

The human brain operates in large-scale functional networks. These networks are an expression of temporally correlated activity across brain regions, but how global network properties relate to the neural dynamics of individual regions remains incompletely understood. Here, we show that the brain's network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous "complexity drops" in functional magnetic resonance imaging signals. These episodes closely explain functional connectivity strength between regions, subserve the propagation of neural activity patterns, and reflect interindividual differences in age and behavior. Furthermore, complexity drops define neural activity states that dynamically shape the connectivity strength, topological configuration, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a principled complexity architecture of neural activity-a human "complexome" that underpins the brain's functional network organization.