Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance

Adolescent-to-adult gains in cognitive flexibility are adaptively supported by reward sensitivity, exploration, and neural variability

Cognitive flexibility exhibits dynamic changes throughout development, with different forms of flexibility showing dissociable developmental trajectories. In this review, we propose that an adolescent-specific mode of flexibility in the face of changing environmental contingencies supports the emergence of adolescent-to-adult gains in cognitive shifting efficiency. We first describe how cognitive shifting abilities monotonically improve from childhood to adulthood, accompanied by increases in brain state flexibility, neural variability, and excitatory/inhibitory balance. We next summarize evidence supporting the existence of a dopamine-driven, adolescent peak in flexible behavior that results in reward seeking, undirected exploration, and environmental sampling. We propose a neurodevelopmental framework that relates these adolescent behaviors to the refinement of neural phenotypes relevant to mature cognitive flexibility, and thus highlight the importance of the adolescent period in fostering healthy neurocognitive trajectories.

Brain Network Interconnectivity Dynamics Explain Metacognitive Differences in Listening Behavior

Complex auditory scenes pose a challenge to attentive listening, rendering listeners slower and more uncertain in their perceptual decisions. How can we explain such behaviors from the dynamics of cortical networks that pertain to the control of listening behavior? We here follow up on the hypothesis that human adaptive perception in challenging listening situations is supported by modular reconfiguration of auditory-control networks in a sample of N = 40 participants (13 males) who underwent resting-state and task functional magnetic resonance imaging (fMRI). Individual titration of a spatial selective auditory attention task maintained an average accuracy of ∼70% but yielded considerable interindividual differences in listeners' response speed and reported confidence in their own perceptual decisions. Whole-brain network modularity increased from rest to task by reconfiguring auditory, cinguloopercular, and dorsal attention networks. Specifically, interconnectivity between the auditory network and cinguloopercular network decreased during the task relative to the resting state. Additionally, interconnectivity between the dorsal attention network and cinguloopercular network increased. These interconnectivity dynamics were predictive of individual differences in response confidence, the degree of which was more pronounced after incorrect judgments. Our findings uncover the behavioral relevance of functional cross talk between auditory and attentional-control networks during metacognitive assessment of one's own perception in challenging listening situations and suggest two functionally dissociable cortical networked systems that shape the considerable metacognitive differences between individuals in adaptive listening behavior.

Electroencephalography Spectral-power Volatility Predicts Problem-solving Outcomes

Temporal variability is a fundamental property of brain processes and is functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance as one example of how temporal variability affects high-level cognition. We used volatility to assess step-by-step fluctuations of EEG spectral power while individuals attempted to solve word-association puzzles. Inspired by recent results with hidden-state modeling, we tested the hypothesis that spectral-power volatility is directly associated with problem-solving outcomes. As predicted, volatility was lower during trials solved with insight compared with those solved analytically. Moreover, volatility during prestimulus preparation for problem-solving predicted solving outcomes, including solving success and solving time. These novel findings were replicated in a separate data set from an anagram-solving task, suggesting that less-rapid transitions between neural oscillatory synchronization and desynchronization predict better solving performance and are conducive to solving with insight for these types of problems. Thus, volatility can be a valuable index of cognition-related brain dynamics.

Greater up-modulation of intra-individual brain signal variability makes a high-load cognitive task more arduous for older adults

The extent to which brain responses are less distinctive across varying cognitive loads in older adults is referred to as neural dedifferentiation. Moment-to-moment brain signal variability, an emerging indicator, reveals not only the adaptability of an individual's brain as an inter-individual trait, but also the allocation of neural resources within an individual due to ever-changing task demands, thus shedding novel insight into the process of neural dedifferentiation. However, how the modulation of intra-individual brain signal variability reflects behavioral differences related to cognitively demanding tasks remains unclear. In this study, we employed functional near-infrared spectroscopy (fNIRS) imaging to capture the variability of brain signals, which was quantified by the standard deviation, during both the resting state and an n-back task (n = 1, 2, 3) in 57 healthy older adults. Using multivariate Partial Least Squares (PLS) analysis, we found that fNIRS signal variability increased from the resting state to the task and increased with working memory load in older adults. We further confirmed that greater fNIRS signal variability generally supported faster and more stable response time in the 2- and 3-back conditions. However, the intra-individual level analysis showed that the greater the up-modulation in fNIRS signal variability with cognitive loads, the more its accuracy decreases and mean response time increases, suggesting that a greater intra-individual brain signal variability up-modulation may reflect decreased efficiency in neural information processing. Taken together, our findings offer new insights into the nature of brain signal variability, suggesting that inter- and intra-individual brain signal variability may index distinct theoretical constructs.

Dopamine precursor depletion affects performance and confidence judgements when events are timed from an explicit, but not an implicit onset

Dopamine affects processing of temporal information, but most previous work has tested its role in prospective tasks, where participants know in advance when the event to be timed starts. However, we are often exposed to events whose onset we do not know in advance. We can evaluate their duration after they have elapsed, but mechanisms underlying this ability are still elusive. Here we contrasted effects of acute phenylalanine and tyrosine depletion (APTD) on both forms of timing in healthy volunteers, in a within-subject, placebo-controlled design. Participants were presented with a disc moving around a circular path and asked to reproduce the duration of one full revolution and to judge their confidence in performance. The onset of the revolution was either known in advance (explicit onset) or revealed only at the end of the trial (implicit onset). We found that APTD shortened reproduced durations in the explicit onset task but had no effect on temporal performance in the implicit onset task. This dissociation is corroborated by effects of APTD on confidence judgements in the explicit task only. Our findings suggest that dopamine has a specific role in prospective encoding of temporal intervals, rather than the processing of temporal information in general.

Neural variability in three major psychiatric disorders

Across the major psychiatric disorders (MPDs), a shared disruption in brain physiology is suspected. Here we investigate the neural variability at rest, a well-established behavior-relevant marker of brain function, and probe its basis in gene expression and neurotransmitter receptor profiles across the MPDs. We recruited 219 healthy controls and 279 patients with schizophrenia, major depressive disorder, or bipolar disorders (manic or depressive state). The standard deviation of blood oxygenation level-dependent signal (SDBOLD) obtained from resting-state fMRI was used to characterize neural variability. Transdiagnostic disruptions in SDBOLD patterns and their relationships with clinical symptoms and cognitive functions were tested by partial least-squares correlation. Moving beyond the clinical sample, spatial correlations between the observed patterns of SDBOLD disruption and postmortem gene expressions, Neurosynth meta-analytic cognitive functions, and neurotransmitter receptor profiles were estimated. Two transdiagnostic patterns of disrupted SDBOLD were discovered. Pattern 1 is exhibited in all diagnostic groups and is most pronounced in schizophrenia, characterized by higher SDBOLD in the language/auditory networks but lower SDBOLD in the default mode/sensorimotor networks. In comparison, pattern 2 is only exhibited in unipolar and bipolar depression, characterized by higher SDBOLD in the default mode/salience networks but lower SDBOLD in the sensorimotor network. The expression of pattern 1 related to the severity of clinical symptoms and cognitive deficits across MPDs. The two disrupted patterns had distinct spatial correlations with gene expressions (e.g., neuronal projections/cellular processes), meta-analytic cognitive functions (e.g., language/memory), and neurotransmitter receptor expression profiles (e.g., D2/serotonin/opioid receptors). In conclusion, neural variability is a potential transdiagnostic biomarker of MPDs with a substantial amount of its spatial distribution explained by gene expressions and neurotransmitter receptor profiles. The pathophysiology of MPDs can be traced through the measures of neural variability at rest, with varying clinical-cognitive profiles arising from differential spatial patterns of aberrant variability.

Within-Individual BOLD Signal Variability and its Implications for Task-Based Cognition: A Systematic Review

Within-individual blood oxygen level-dependent (BOLD) signal variability, intrinsic moment-to-moment signal fluctuations within a single individual in specific voxels across a given time course, is a relatively new metric recognized in the neuroimaging literature. Within-individual BOLD signal variability has been postulated to provide information beyond that provided by mean-based analysis. Synthesis of the literature using within-individual BOLD signal variability methodology to examine various cognitive domains is needed to understand how intrinsic signal fluctuations contribute to optimal performance. This systematic review summarizes and integrates this literature to assess task-based cognitive performance in healthy groups and few clinical groups. Included papers were published through October 17, 2022. Searches were conducted on PubMed and APA PsycInfo. Studies eligible for inclusion used within-individual BOLD signal variability methodology to examine BOLD signal fluctuations during task-based functional magnetic resonance imaging (fMRI) and/or examined relationships between task-based BOLD signal variability and out-of-scanner behavioral measure performance, were in English, and were empirical research studies. Data from each of the included 19 studies were extracted and study quality was systematically assessed. Results suggest that variability patterns for different cognitive domains across the lifespan (ages 7-85) may depend on task demands, measures, variability quantification method used, and age. As neuroimaging methods explore individual-level contributions to cognition, within-individual BOLD signal variability may be a meaningful metric that can inform understanding of neurocognitive performance. Further research in understudied domains/populations, and with consistent quantification methods/cognitive measures, will help conceptualize how intrinsic BOLD variability impacts cognitive abilities in healthy and clinical groups.

The Role of Blood Oxygen Level Dependent Signal Variability in Pediatric Neuroscience: A Systematic Review

BACKGROUND

As pediatric BOLD Signal Variability (SV) analysis is relatively novel, there is a need to provide a foundational framework that gives researchers an entry point into engaging with the topic. This begins with clarifying the definition of BOLD signal variability by identifying and categorizing the various metrics utilized to measure BOLD SV.

METHODS

A systematic review of the literature was conducted. Inclusion criteria were restricted to studies utilizing any metric of BOLD SV and with individuals younger than 18 in the study population. The definition of BOLD SV was any measure of intra-individual variability in the BOLD signal. Five databases were searched: Psychinfo, Healthstar, MEDLINE, Embase, and Scopus.

RESULTS

A total of 17 observational studies, including male (n = 1796) and female (n = 1324) pediatric participants were included. Eight studies quantified variability as the amount of deviation from the average BOLD signal, seven used complexity-based metrics, three used correlation measures of variability, and one used the structure of the hemodynamic response function. In this study, 10 methods of quantifying signal variability were identified. Associations and trends in BOLD SV were commonly found with age, factors specific to mental and/or neurological disorders such as attention deficit disorder, epilepsy, psychotic symptoms, and performance on psychological and behavioral tasks.

CONCLUSIONS

BOLD SV is a potential biomarker of neurodevelopmental and neurological conditions and symptom severity in mental disorders for defined pediatric populations. Studies that establish clinical trends and identify the mechanisms underlying BOLD SV with a low risk of bias are needed before clinical applications can be utilized by physicians.

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.

External drivers of BOLD signal's non-stationarity

A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal's external drivers and shines a light on the likely external sources contributing to the BOLD signal's non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain's time-varying functional dynamics.

Distributed networks for auditory memory differentially contribute to recall precision

Re-directing attention to objects in working memory can enhance their representational fidelity. However, how this attentional enhancement of memory representations is implemented across distinct, sensory and cognitive-control brain network is unspecified. The present fMRI experiment leverages psychophysical modelling and multivariate auditory-pattern decoding as behavioral and neural proxies of mnemonic fidelity. Listeners performed an auditory syllable pitch-discrimination task and received retro-active cues to selectively attend to a to-be-probed syllable in memory. Accompanied by increased neural activation in fronto-parietal and cingulo-opercular networks, valid retro-cues yielded faster and more perceptually sensitive responses in recalling acoustic detail of memorized syllables. Information about the cued auditory object was decodable from hemodynamic response patterns in superior temporal sulcus (STS), fronto-parietal, and sensorimotor regions. However, among these regions retaining auditory memory objects, neural fidelity in the left STS and its enhancement through attention-to-memory best predicted individuals' gain in auditory memory recall precision. Our results demonstrate how functionally discrete brain regions differentially contribute to the attentional enhancement of memory representations.

Moment-to-Moment Brain Signal Variability Reliably Predicts Psychiatric Treatment Outcome

BACKGROUND

Biomarkers of psychiatric treatment response remain elusive. Functional magnetic resonance imaging (fMRI) has shown promise, but low reliability has limited the utility of typical fMRI measures (e.g., average brain signal) as harbingers of treatment success. Notably, although historically considered a source of noise, temporal brain signal variability continues to gain momentum as a sensitive and reliable indicator of individual differences in neural efficacy, yet has not been examined in relation to psychiatric treatment outcomes.

METHODS

A total of 45 patients with social anxiety disorder were scanned twice (11 weeks apart) using simple task-based and resting-state fMRI to capture moment-to-moment neural variability. After fMRI test-retest, patients underwent a 9-week cognitive behavioral therapy. Multivariate modeling and reliability-based cross-validation were used to perform brain-based prediction of treatment outcomes.

RESULTS

Task-based brain signal variability was the strongest contributor in a treatment outcome prediction model (total r_{ACTUAL,PREDICTED} = 0.77), outperforming self-reports, resting-state neural variability, and standard mean-based measures of neural activity. Notably, task-based brain signal variability showed excellent test-retest reliability (intraclass correlation coefficient = 0.80), even with a task length less than 3 minutes long.

CONCLUSIONS

Rather than a source of undesirable noise, moment-to-moment fMRI signal variability may instead serve as a highly reliable and efficient prognostic indicator of clinical outcome.

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.

Bilingual language entropy influences executive functions through functional connectivity and signal variability

How bilinguals' experience in managing multiple languages in different communicative contexts influences the cognitive and neural aspects of executive functions remains unclear. Therefore, we examined whether variations in language experience in young adult bilinguals were associated with data-driven brain functional network patterns (connectivity and signal variability) defined by performance during executive control tasks (Stroop and task-switching). Multiple aspects of language experience, such as the extent of balanced bilingualism in language proficiency and usage, and language diversity across social contexts (i.e., language entropy) were assessed. We found that greater language diversity, rather than balanced bilingualism per se, was related to higher brain network specialization and segregation concentrated on the default mode and executive control networks, and lower signal variability, a pattern linked to smaller RT-indices related to executive functioning of shifting, goal-maintenance, and conflict-monitoring. Our findings underscore the important role of language diversity in influencing bilinguals' neurocognitive characteristics of executive functioning.

Fronto-striatal dopamine D2 receptor availability is associated with cognitive variability in older individuals with low dopamine integrity

Within-person, moment-to-moment, variability in behavior increases with advancing adult age, potentially reflecting the influence of reduced structural and neurochemical brain integrity, especially that of the dopaminergic system. We examined the role of dopamine D2 receptor (D2DR) availability, grey-, and white-matter integrity, for between-person differences in cognitive variability in a large sample of healthy older adults (n = 181; 64-68 years) from the Cognition, Brain, and Aging (COBRA) study. Intra-individual variability (IIV) in cognition was measured as across-trial variability in participants' response times for tasks assessing perceptual speed and working memory, as well as for a control task of motor speed. Across the whole sample, no associations of D2DR availability, or grey- and white-matter integrity, to IIV were observed. However, within-person variability in cognition was increased in two subgroups of individuals displaying low mean-level cognitive performance, one of which was characterized by low subcortical and cortical D2DR availability. In this latter group, fronto-striatal D2DR availability correlated negatively with within-person variability in cognition. This finding suggests that the influence of D2DR availability on cognitive variability may be more easily disclosed among individuals with low dopamine-system integrity, highlighting the benefits of large-scale studies for delineating heterogeneity in brain-behavior associations in older age.

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.

Baseline-dependent effect of dopamine's precursor L-tyrosine on working memory gating but not updating

Adaptive goal-directed behavior requires a dynamic balance between maintenance and updating within working memory (WM). This balance is controlled by an input-gating mechanism implemented by dopamine in the basal ganglia. Given that dopaminergic manipulations can modulate performance on WM-related tasks, it is important to gain mechanistic insight into whether such manipulations differentially affect updating (i.e., encoding and removal) and the closely-related gate opening/closing processes that respectively enable/prevent updating. To clarify this issue, 2.0 g of dopamine’s precursor L-tyrosine was administered to healthy young adults (N = 45) in a double-blind, placebo-controlled, within-subjects study. WM processes were empirically distinguished using the reference-back paradigm, which isolates performance related to updating, gate opening, and gate closing. L-tyrosine had a selective, baseline-dependent effect only on gate opening, which was evidenced by markedly reduced variance across subjects in gate opening performance in the L-tyrosine compared with the placebo condition, whereas the whole-sample average performance did not differ between conditions. This indicates a pattern of results whereby low-performing subjects improved, whereas high-performing subjects were impaired on L-tyrosine. Importantly, this inverted U-shaped pattern was not explained by regression to the mean. These results are consistent with an inverted-U relationship between dopamine and WM, and they indicate that updating and gating are differentially affected by a dopaminergic manipulation. This highlights the importance of distinguishing these processes when studying WM, for example, in the context of WM deficits in disorders with a dopaminergic pathophysiology.

Intrinsic functional connectivity of the frontoparietal network predicts inter-individual differences in the propensity for costly third-party punishment

Humans are motivated to give norm violators their just deserts through costly punishment even as unaffected third parties (i.e., third-party punishment, TPP). A great deal of individual variability exists in costly punishment; however, how this variability particularly in TPP is represented by the brain's intrinsic network architecture remains elusive. Here, we examined whether inter-individual differences in the propensity for TPP can be predicted based on resting-state functional connectivity (RSFC) combining an economic TPP game with task-free functional neuroimaging and a multivariate prediction framework. Our behavioral results revealed that TPP punishment increased with the severity of unfairness for offers. People with higher TPP propensity punished more harshly across norm-violating scenarios. Our neuroimaging findings showed RSFC within the frontoparietal network predicted individual differences in TPP propensity. Our findings contribute to understanding the neural fingerprint for an individual's propensity to costly punish strangers, and shed some light on how social norm enforcement behaviors are represented by the brain's intrinsic network architecture.

The coupling of BOLD signal variability and degree centrality underlies cognitive functions and psychiatric diseases

Brain signal variability has been consistently linked to functional integration; however, whether this coupling is associated with cognitive functions and/or psychiatric diseases has not been clarified. Using multiple multimodality datasets, including resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP: N = 927) and a Beijing sample (N = 416) and cerebral blood flow (CBF) and rsfMRI data from a Hangzhou sample (N = 29), we found that, compared with the existing variability measure (i.e., SD_BOLD), the mean-scaled (standardized) fractional standard deviation of the BOLD signal (mfSD_BOLD) maintained very high test-retest reliability, showed greater cross-site reliability and was less affected by head motion. We also found strong reproducible couplings between the mfSD_BOLD and functional integration measured by the degree centrality (DC), both cross-voxel and cross-subject, which were robust to scanning and preprocessing parameters. Moreover, both mfSD_BOLD and DC were correlated with CBF, suggesting a common physiological basis for both measures. Critically, the degree of coupling between mfSD_BOLD and long-range DC was positively correlated with individuals' cognitive total composite scores. Brain regions with greater mismatches between mfSD_BOLD and long-range DC were more vulnerable to brain diseases. Our results suggest that BOLD signal variability could serve as a meaningful index of local function that underlies functional integration in the human brain and that a strong coupling between BOLD signal variability and functional integration may serve as a hallmark of balanced brain networks that are associated with optimal brain functions.

Dopaminergic Regulation of Reward System Connectivity Underpins Pain and Emotional Suffering in Migraine

Purpose

It has been suggested that reward system dysfunction may account for emotion and pain suffering in migraine. However, there is a lack of evidence whether the altered reward system connectivity is directly associated with clinical manifestations, including negative affect and ictal pain severity and, at the molecular level, the dopamine (DA) D2/D3 receptors (D2/3Rs) signaling implicated in encoding motivational and emotional cues.

Patients and Methods

We acquired resting-state functional MRI from interictal episodic migraine (EM) patients and age-matched healthy controls, as well as positron emission tomography (PET) with [¹¹C]raclopride, a selective radiotracer for DA D2/3Rs, from a subset of these participants. The nucleus accumbens (NAc) was seeded to measure functional connectivity (FC) and DA D2/3Rs availability based on its essential involvement in pain-related aversive/reward functions. Associations of the brain measures with positive/negative affect and ictal pain severity were also assessed.

Results

Compared with controls, the EM group showed weaker right NAc connectivity with areas implicated in pain and emotional regulation, such as the amygdala, rostral anterior cingulate cortex, hippocampus, and thalamus; but showed stronger left NAc connectivity with the dorsolateral prefrontal cortex and lingual gyrus. Moreover, among the altered NAc connectivities, only right NAc-amygdala connectivity was inversely correlated with DA D2/3Rs availability in migraine patients (diagnostic group-by-D2/3Rs interaction p < 0.007). At a clinical level, such weaker NAc-amygdala connectivity was associated with lower interictal positive affect and greater ictal pain severity over the head and facial extension area (pain area and intensity number summation, PAINS).

Conclusion

Together, our findings suggest that altered reward system connectivity, specifically between the NAc and amygdala, might be affected by endogenous DA D2/3Rs signaling, and such process might be a neural mechanism that underlies emotional and pain suffering in episodic migraineurs.

Aberrant Brain Signal Variability and COMT Genotype in Chronic TMD Patients

The analysis of brain signal variability is a promising approach to understand pathological brain function related to chronic pain. This study investigates whether blood-oxygen-level-dependent signal variability (BOLD_SV) in specific frequency bands is altered in temporomandibular disorder (TMD) and correlated to its clinical features. Twelve patients with chronic myofascial TMD and 24 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. The BOLD_SV was measured as the standard deviation of the BOLD time series at each voxel and compared between groups. We also examined the potential relationship between the BOLD_SV and the catechol-O-methyltransferase (COMT) Val¹⁵⁸Met polymorphism. We assessed sensory-discriminative pain in the craniofacial region, pain sensitivity to sustained masseteric pain challenge, and TMD pain frequency for clinical correlation. Patients displayed reduced BOLD_SV in the dorsolateral prefrontal cortex (dlPFC) as compared with HC in all frequency bands. In the slow-3 band, patients also showed reduced BOLD_SV in the medial dorsal thalamus, primary motor cortex (M1), and primary somatosensory cortex (S1) and heightened BOLD_SV in the temporal pole. Notably, we found a significant correlation between lower BOLD_SV (slow-3) in the orofacial M1/S1 regions and higher clinical pain (intensity/area) and higher sensitivity of the masseter muscle pain. Moreover, lower BOLD_SV (slow-3) in the dlPFC and ventrolateral PFC was associated with a higher TMD pain frequency. Participants who had the COMT ¹⁵⁸Met substitution exhibited lower BOLD_SV in the dlPFC and higher BOLD_SV in the temporal pole as compared with participants without the COMT ¹⁵⁸Met substitution. An increasing number of Met alleles was associated with lower dlPFC and greater temporal pole BOLD_SV in both HC and TMD groups. Together, we demonstrated that chronic TMD patients exhibit aberrant BOLD_SV in the top-down pain modulatory and sensorimotor circuits associated with their pain frequency and severity. COMT Val¹⁵⁸Met polymorphism might affect clinical symptoms in association with regional brain signal variability, specifically involved in cognitive and emotional regulation of pain.

Behavior needs neural variability

Human and non-human animal behavior is highly malleable and adapts successfully to internal and external demands. Such behavioral success stands in striking contrast to the apparent instability in neural activity (i.e., variability) from which it arises. Here, we summon the considerable evidence across scales, species, and imaging modalities that neural variability represents a key, undervalued dimension for understanding brain-behavior relationships at inter- and intra-individual levels. We believe that only by incorporating a specific focus on variability will the neural foundation of behavior be comprehensively understood.

Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks

Channel pruning is an effective technique that has been widely applied to deep neural network compression. However, many existing methods prune from a pretrained model, thus resulting in repetitious pruning and fine-tuning processes. In this article, we propose a dynamical channel pruning method, which prunes unimportant channels at the early stage of training. Rather than utilizing some indirect criteria (e.g., weight norm, absolute weight sum, and reconstruction error) to guide connection or channel pruning, we design criteria directly related to the final accuracy of a network to evaluate the importance of each channel. Specifically, a channelwise gate is designed to randomly enable or disable each channel so that the conditional accuracy changes (CACs) can be estimated under the condition of each channel disabled. Practically, we construct two effective and efficient criteria to dynamically estimate CAC at each iteration of training; thus, unimportant channels can be gradually pruned during the training process. Finally, extensive experiments on multiple data sets (i.e., ImageNet, CIFAR, and MNIST) with various networks (i.e., ResNet, VGG, and MLP) demonstrate that the proposed method effectively reduces the parameters and computations of baseline network while yielding the higher or competitive accuracy. Interestingly, if we Double the initial Channels and then Prune Half (DCPH) of them to baseline's counterpart, it can enjoy a remarkable performance improvement by shaping a more desirable structure.

Structural and functional underpinnings of precentral abnormalities in amyotrophic lateral sclerosis

BACKGROUND AND PURPOSE

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the loss of both upper and lower motor neurons. Studies using various magnetic resonance imaging (MRI) analytical approaches have consistently identified significant precentral abnormalities in ALS, whereas their structural and functional underpinnings remain poorly understood.

METHODS

Using cortical thickness, fractional anisotropy (FA), and effective connectivity, we performed a multimodal MRI study to examine the structural and functional alterations associated with precentral abnormalities in patients with ALS (n = 60) compared with healthy controls (n = 60).

RESULTS

Cortical thickness analysis revealed significant cortical thinning in the right precentral gyrus (PCG), superior frontal gyrus, and superior temporal gyrus in patients with ALS. Tractwise white matter microstructure analyses revealed decreased FA in the tracts connected to the PCG cluster in patients with ALS involving the right corticospinal tract and the middle posterior body of the corpus callosum. Additionally, the cortical thickness of the PCG cluster was found to be positively correlated with FA of the tracts connected to the PCG cluster, suggesting that these two structural features are tightly coupled. Using spectral dynamic causal modelling, effective connectivity analysis among the three regions with cortical thinning revealed decreased self-inhibitory influence in the PCG cluster in patients with ALS, which might be an endophenotypic manifestation of an imbalance in inhibitory and excitatory neurotransmitters in this region.

CONCLUSIONS

The present data shed new light on the structural and functional underpinnings of precentral abnormalities in ALS.

Oxytocin modulates local topography of human functional connectome in healthy men at rest

Oxytocin has recently received remarkable attention for its role as a modulator of human behaviour. Here, we aimed to expand our knowledge of the neural circuits engaged by oxytocin by investigating the effects of intranasal and intravenous oxytocin on the functional connectome at rest in 16 healthy men. Oxytocin modulates the functional connectome within discrete neural systems, but does not affect the global capacity for information transfer. These local effects encompass key hubs of the oxytocin system (e.g. amygdala) but also regions overlooked in previous hypothesis-driven research (i.e. the visual circuits, temporal lobe and cerebellum). Increases in levels of oxytocin in systemic circulation induce broad effects on the functional connectome, yet we provide indirect evidence supporting the involvement of nose-to-brain pathways in at least some of the observed changes after intranasal oxytocin. Together, our results suggest that oxytocin effects on human behaviour entail modulation of multiple levels of brain processing distributed across different systems.

A Multimodal Fusion Analysis of Pretreatment Anatomical and Functional Cortical Abnormalities in Responsive and Non-responsive Schizophrenia

Background: Antipsychotic medications provide limited long-term benefit to ~30% of schizophrenia patients. Multimodal magnetic resonance imaging (MRI) data have been used to investigate brain features between responders and nonresponders to antipsychotic treatment; however, these analytical techniques are unable to weigh the interrelationships between modalities. Here, we used multiset canonical correlation and joint independent component analysis (mCCA + jICA) to fuse MRI data to examine the shared and specific multimodal features between the patients and healthy controls (HCs) and between the responders and non-responders. Method: Resting-state functional and structural MRI data were collected from 55 patients with drug-naïve first-episode schizophrenia (FES) and demographically matched HCs. Based on the decrease in Positive and Negative Syndrome Scale scores from baseline to the 1-year follow-up, FES patients were divided into a responder group (RG) and a non-responder group (NRG). Gray matter volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) maps were used as features in mCCA + jICA. Results: Between FES patients and HCs, there were three modality-specific discriminative independent components (ICs) showing the difference in mixing coefficients (GMV-IC7, GMV-IC8, and fALFF-IC5). The fusion analysis indicated one modality-shared IC (GMV-IC2 and ReHo-IC2) and three modality-specific ICs (GMV-IC1, GMV-IC3, and GMV-IC6) between the RG and NRG. The right postcentral gyrus showed a significant difference in GMV features between FES patients and HCs and modality-shared features (GMV and ReHo) between responders and nonresponders. The modality-shared component findings were highlighted by GMV, mainly in the bilateral temporal gyrus and the right cerebellum associated with ReHo in the right postcentral gyrus. Conclusions: This study suggests that joint anatomical and functional features of the cortices may reflect an early pathophysiological mechanism that is related to a 1-year treatment response.

Whole-brain signatures of functional connectivity after bidirectional modulation of the dopaminergic system in mice

While neuropsychiatric drugs influence neural activity across multiple brain regions, the current understanding of their mechanism of action derives from studies that investigate an influence of a given drug onto a pre-selected and small number of brain regions. To understand how neuropsychiatric drugs affect coordinated activity across brain regions and to detect the brain regions most relevant to pharmacological action in an unbiased way, studies that assess brain-wide neuronal activity are paramount. Here, we used whole-brain immunostaining of the neuronal activity marker cFOS, and graph theory to generate brain-wide maps of neuronal activity upon pharmacological challenges. We generated brain-wide maps 2.5 h after treatment of the atypical dopamine transporter inhibitor modafinil (10, 30, and 100 mg/kg) or the vesicular monoamine transporter 2 inhibitor tetrabenazine (0.25, 0.5 and 1 mg/kg). Modafinil increased the number of cFOS positive neurons in a dose-dependent manner. Moreover, modafinil significantly reduced functional connectivity across the entire brain. Graph theory analysis revealed that modafinil decreased the node degree of cortical and subcortical regions at the three doses tested, followed by a reduction in global efficiency. Simultaneously, we identified highly interconnected hub regions that emerge exclusively upon modafinil treatment. These regions were the mediodorsal thalamus, periaqueductal gray, subiculum, and rhomboid nucleus. On the other hand, while tetrabenazine had mild effects on cFOS counts, it reduced functional connectivity across the entire brain, cortical node degree, and global efficiency. As hub regions, we identified the substantia innominata and ventral pallidum. Our results uncovered novel mechanisms of action at a brain-wide scale for modafinil and tetrabenazine. Our analytical approach offers a tool to characterize signatures of whole-brain functional connectivity for drug candidates and to identify potential undesired effects at a mesoscopic scale. Additionally, it offers a guide towards targeted experiments on newly identified hub regions.

Dopamine release, diffusion and uptake: A computational model for synaptic and volume transmission

Computational modeling of dopamine transmission is challenged by complex underlying mechanisms. Here we present a new computational model that (I) simultaneously regards release, diffusion and uptake of dopamine, (II) considers multiple terminal release events and (III) comprises both synaptic and volume transmission by incorporating the geometry of the synaptic cleft. We were able to validate our model in that it simulates concentration values comparable to physiological values observed in empirical studies. Further, although synaptic dopamine diffuses into extra-synaptic space, our model reflects a very localized signal occurring on the synaptic level, i.e. synaptic dopamine release is negligibly recognized by neighboring synapses. Moreover, increasing evidence suggests that cognitive performance can be predicted by signal variability of neuroimaging data (e.g. BOLD). Signal variability in target areas of dopaminergic neurons (striatum, cortex) may arise from dopamine concentration variability. On that account we compared spatio-temporal variability in a simulation mimicking normal dopamine transmission in striatum to scenarios of enhanced dopamine release and dopamine uptake inhibition. We found different variability characteristics between the three settings, which may in part account for differences in empirical observations. From a clinical perspective, differences in striatal dopaminergic signaling contribute to differential learning and reward processing, with relevant implications for addictive- and compulsive-like behavior. Specifically, dopaminergic tone is assumed to impact on phasic dopamine and hence on the integration of reward-related signals. However, in humans DA tone is classically assessed using PET, which is an indirect measure of endogenous DA availability and suffers from temporal and spatial resolution issues. We discuss how this can lead to discrepancies with observations from other methods such as microdialysis and show how computational modeling can help to refine our understanding of DA transmission.

Boosts in brain signal variability track liberal shifts in decision bias

Adopting particular decision biases allows organisms to tailor their choices to environmental demands. For example, a liberal response strategy pays off when target detection is crucial, whereas a conservative strategy is optimal for avoiding false alarms. Using conventional time-frequency analysis of human electroencephalographic (EEG) activity, we previously showed that bias setting entails adjustment of evidence accumulation in sensory regions (Kloosterman et al., 2019), but the presumed prefrontal signature of a conservative-to-liberal bias shift has remained elusive. Here, we show that a liberal bias shift is reflected in a more unconstrained neural regime (boosted entropy) in frontal regions that is suited to the detection of unpredictable events. Overall EEG variation, spectral power and event-related potentials could not explain this relationship, highlighting that moment-to-moment neural variability uniquely tracks bias shifts. Neural variability modulation through prefrontal cortex appears instrumental for permitting an organism to adapt its biases to environmental demands.

Dynamic functional connectivity between nucleus accumbens and the central executive network relates to chronic cannabis use

The neural mechanisms of drug cue‐reactivity regarding the temporal fluctuations of functional connectivity, namely the dynamic connectivity, are sparsely studied. Quantifying the task‐modulated variability in dynamic functional connectivity at cue exposure can aid the understanding. We analyzed changes in dynamic connectivity in 54 adult cannabis users and 90 controls during a cannabis cue exposure task. The variability was measured as standard deviation in the (a) connectivity weights of the default mode, the central executive, and the salience networks and two reward loci (amygdalae and nuclei accumbens); and (b) topological indexes of the whole brain (global efficiency, modularity and network resilience). These were compared for the main effects of task conditions and the group (users vs. controls), and correlated with pre‐ and during‐scan subjective craving. The variability of connectivity weights between the central executive network and nuclei accumbens was increased in users throughout the cue exposure task, and, was positively correlated with during‐scan craving for cannabis. The variability of modularity was not different by groups, but positively correlated with prescan craving. The variability of dynamic connectivity during cannabis cue exposure task between the central executive network and the nuclei accumbens, and, the level of modularity, seem to relate to the neural underpinning of cannabis use and the subjective craving.

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.

Hemodynamic responses are abnormal in isolated cervical dystonia

Neuroimaging studies using functional magnetic resonance imaging (fMRI), which measures brain activity by detecting the changes in blood oxygenation levels, are advancing our understanding of the pathophysiology of dystonia. Neurobiological disturbances in dystonia, however, may affect neurovascular coupling and impact the interpretability of fMRI studies. We evaluated here whether the hemodynamic response patterns during a behaviorally matched motor task are altered in isolated cervical dystonia (CD). Twenty-five CD patients and 25 healthy controls (HCs) underwent fMRI scanning during a paced finger tapping task (nondystonic task in patients). Imaging data were analyzed using a constrained principal component analysis-a statistical method that combines regression analysis and principal component analysis and enables the extraction of task-related functional networks and determination of the spatial and temporal hemodynamic response patterns associated with the task performance. Data from three patients and two controls were removed due to excessive movement. No significant differences in demographics or motor performance were observed. Three task-associated functional brain networks were identified. During task performance, reduced hemodynamic responses were seen in a sensorimotor network and in a network that included key nodes of the default mode, executive control and visual networks. During rest, reductions in hemodynamic responses were seen in the cognitive/visual network. Lower hemodynamic responses within the primary sensorimotor network in patients were correlated with the increased dystonia severity. Pathophysiological disturbances in isolated CD, such as alterations in inhibitory signaling and dopaminergic neurotransmission, may impact neurovascular coupling. Not accounting for hemodynamic response differences in fMRI studies of dystonia could lead to inaccurate results and interpretations.

Opioidergic System and Functional Architecture of Intrinsic Brain Activity: Implications for Psychiatric Disorders

The opioidergic system and intrinsic brain activity, as organized in large-scale networks such as the salience network (SN), sensorimotor network (SMN), and default-mode network (DMN), play core roles in healthy behavior and psychiatric disorders. This work aimed to investigate how opioidergic signaling affects intrinsic brain activity in healthy individuals by reviewing relevant neuroanatomical, molecular, functional, and pharmacological magnetic resonance imaging studies in order to clarify their physiological links and changes in psychiatric disorders. The SN shows dense opioidergic innervations of subcortical structures and high expression levels of opioid receptors in subcortical-cortical areas, with enhanced or reduced activity with low or very high doses of opioids, respectively. The SMN shows high levels of opioid receptors in subcortical areas and functional disconnection caused by opioids. The DMN shows low levels of opioid receptors in cortical areas and inhibited or enhanced activity with low or high doses of opioids, respectively. Finally, we proposed a working model. Opioidergic signaling enhances SN and suppresses SMN (and DMN) activity, resulting in affective excitation with psychomotor inhibition; stronger increases in opioidergic signaling attenuate the SN and SMN while disinhibiting the DMN, dissociating affective and psychomotor functions from the internal states; the opposite occurs with a deficit of opioidergic signaling.

Dopamine Signaling Modulates the Stability and Integration of Intrinsic Brain Networks

Dopaminergic projections are hypothesized to stabilize neural signaling and neural representations, but how they shape regional information processing and large-scale network interactions remains unclear. Here we investigated effects of lowered dopamine levels on within-region temporal signal variability (measured by sample entropy) and between-region functional connectivity (measured by pairwise temporal correlations) in the healthy brain at rest. The acute phenylalanine and tyrosine depletion (APTD) method was used to decrease dopamine synthesis in 51 healthy participants who underwent resting-state functional MRI (fMRI) scanning. Functional connectivity and regional signal variability were estimated for each participant. Multivariate partial least squares (PLS) analysis was used to statistically assess changes in signal variability following APTD as compared with the balanced control treatment. The analysis captured a pattern of increased regional signal variability following dopamine depletion. Changes in hemodynamic signal variability were concomitant with changes in functional connectivity, such that nodes with greatest increase in signal variability following dopamine depletion also experienced greatest decrease in functional connectivity. Our results suggest that dopamine may act to stabilize neural signaling, particularly in networks related to motor function and orienting attention towards behaviorally-relevant stimuli. Moreover, dopamine-dependent signal variability is critically associated with functional embedding of individual areas in large-scale networks.

Linking Structure and Function in Macroscale Brain Networks

Structure-function relationships are a fundamental principle of many naturally occurring systems. However, network neuroscience research suggests that there is an imperfect link between structural connectivity and functional connectivity in the brain. Here, we synthesize the current state of knowledge linking structure and function in macroscale brain networks and discuss the different types of models used to assess this relationship. We argue that current models do not include the requisite biological detail to completely predict function. Structural network reconstructions enriched with local molecular and cellular metadata, in concert with more nuanced representations of functions and properties, hold great potential for a truly multiscale understanding of the structure-function relationship.

Questions and controversies in the study of time-varying functional connectivity in resting fMRI

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

Abnormal Functional Relationship of Sensorimotor Network With Neurotransmitter-Related Nuclei via Subcortical-Cortical Loops in Manic and Depressive Phases of Bipolar Disorder

OBJECTIVE

Manic and depressive phases of bipolar disorder (BD) show opposite psychomotor symptoms. Neuronally, these may depend on altered relationships between sensorimotor network (SMN) and subcortical structures. The study aimed to investigate the functional relationships of SMN with substantia nigra (SN) and raphe nuclei (RN) via subcortical-cortical loops, and their alteration in bipolar mania and depression, as characterized by psychomotor excitation and inhibition.

METHOD

In this resting-state functional magnetic resonance imaging (fMRI) study on healthy (n = 67) and BD patients (n = 100), (1) functional connectivity (FC) between thalamus and SMN was calculated and correlated with FC from SN or RN to basal ganglia (BG)/thalamus in healthy; (2) using an a-priori-driven approach, thalamus-SMN FC, SN-BG/thalamus FC, and RN-BG/thalamus FC were compared between healthy and BD, focusing on manic (n = 34) and inhibited depressed (n = 21) patients.

RESULTS

(1) In healthy, the thalamus-SMN FC showed a quadratic correlation with SN-BG/thalamus FC and a linear negative correlation with RN-BG/thalamus FC. Accordingly, the SN-related FC appears to enable the thalamus-SMN coupling, while the RN-related FC affects it favoring anti-correlation. (2) In BD, mania showed an increase in thalamus-SMN FC toward positive values (ie, thalamus-SMN abnormal coupling) paralleled by reduction of RN-BG/thalamus FC. By contrast, inhibited depression showed a decrease in thalamus-SMN FC toward around-zero values (ie, thalamus-SMN disconnection) paralleled by reduction of SN-BG/thalamus FC (and RN-BG/thalamus FC). The results were replicated in independent HC and BD datasets.

CONCLUSIONS

These findings suggest an abnormal relationship of SMN with neurotransmitters-related areas via subcortical-cortical loops in mania and inhibited depression, finally resulting in psychomotor alterations.

Impact of dopamine and cognitive impairment on neural reactivity to facial emotion in Parkinson's disease

Emotional and cognitive impairments in Parkinson's disease (PD) are prevalent, hamper interpersonal relations and reduce quality of life. It is however unclear to what extent these domains interplay in PD-related deficits and how they are influenced by dopaminergic availability. This study examined the effect of cognitive impairment and dopaminergic medication on neural and behavioral mechanisms of facial emotion recognition in PD patients. PD patients on and off dopaminergic medication and matched healthy controls underwent an emotional face matching task during functional MRI. In addition, a comprehensive neuropsychological evaluation of cognitive function was conducted. Increased BOLD response to emotional faces was found in the visual cortex of PD patients relative to controls irrespective of cognitive function and medication status. Administration of dopaminergic medication in PD patients resulted in restored behavioral accuracy for emotional faces relative to controls and decreased retrosplenial cortex BOLD response to emotion relative to off-medication state. Furthermore, cognitive impairment in PD patients was associated with reduced behavioral accuracy for non-emotional stimuli and predicted BOLD response to emotion in the anterior and posterior cingulate cortices, depending on medication status. Findings of aberrant visual and retrosplenial BOLD response to emotion are suggested to stem from altered attentional and/or emotion-driven modulation from subcortical and higher cortical regions. Our results indicate neural disruptions and behavioral deficits in emotion processing in PD patients that are dependent on dopaminergic availability and independent of cognitive function. Our findings highlight the importance of dopaminergic treatment not only for the motor symptoms but also the emotional disturbances in PD.

The relationship of age and DRD2 polymorphisms to frontostriatal brain activity and working memory performance

Dopamine (DA) in both prefrontal cortex (PFC) and caudate nucleus is critical for working memory (WM) function. The C957T and Taq1A polymorphisms of the DRD2 gene are related to DA D2 receptor densities in PFC and striatum. Using functional MRI, we investigated the relationship of age and these 2 DRD2 gene polymorphisms to WM function and examined possible age by gene interactions. Results demonstrated less caudate activity for older adults (70-80 years; n = 112) compared with the younger age group (25-65 years; n = 191), suggesting age-related functional differences in this region. Importantly, there was a gene-related difference regarding WM performance and frontostriatal brain activity. Specifically, better WM performance and greater activity in PFC were found among C957T C allele carriers. Combined genetic markers for increased DA D2 receptor density were associated with greater caudate activity and higher WM updating performance. The genetic effects on blood oxygen level-dependent activity were only observed in older participants, suggesting magnified genetic effects in aging. Our findings emphasize the importance of DA-related genes in regulating WM functioning in aging and demonstrate a positive link between DA and brain activation in the frontostriatal circuitry.

Effect of Dopaminergic Medications on Blood Oxygen Level-Dependent Variability and Functional Connectivity in Parkinson's Disease and Healthy Aging

Both functional connectivity (FC) and blood oxygen level-dependent (BOLD) signal variability (SDBOLD) are methods that are used for examining the physiological state of the brain. Although they are derived from signal changes and are related, a few studies have explored their relationship. Here, we examined the relationship between SDBOLD and FC within the default mode network (DMN) in healthy aging participants and those with Parkinson's disease (PD) ON and OFF dopaminergic medications. Dopaminergic medications had profound effects on both DMN FC and SDBOLD measured separately in PD. Analyzing DMN FC and SDBOLD in a joint independent component analysis, we identified joint components of DMN FC and SDBOLD that were separately associated with measurements of motor and cognitive impairment in PD and qualitatively similar to those in healthy aging. Dopaminergic medications had a differential effect on these components depending on these measures of disease severity, "normalizing" the relationships. Importantly, we show that dopaminergic medication status matters in imaging PD, and it can affect both connectivity and SDBOLD. Imaging PD ON may lead to inflated estimates of SDBOLD and diminish the ability to measure changes associated with declining motor and cognitive capacities.

Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes

The time course of neuroanatomical structural and functional measures across the lifespan is commonly reported in association with aging. Blood oxygen-level dependent signal variability, estimated using the standard deviation of the signal, or "BOLDSD," is an emerging metric of variability in neural processing, and has been shown to be positively correlated with cognitive flexibility. Generally, BOLDSD is reported to decrease with aging, and is thought to reflect age-related cognitive decline. Additionally, it is well established that normative aging is associated with structural changes in brain regions, and that these predict functional decline in various cognitive domains. Nevertheless, the interaction between alterations in cortical morphology and BOLDSD changes has not been modeled quantitatively. The objective of the current study was to investigate the influence of cortical morphology metrics [i.e., cortical thickness (CT), gray matter (GM) volume, and cortical area (CA)] on age-related BOLDSD changes by treating these cortical morphology metrics as possible physiological confounds using linear mixed models. We studied these metrics in 28 healthy older subjects scanned twice at approximately 2.5 years interval. Results show that BOLDSD is confounded by cortical morphology metrics. Respectively, changes in CT but not GM volume nor CA, show a significant interaction with BOLDSD alterations. Our study highlights that CT changes should be considered when evaluating BOLDSD alternations in the lifespan.

Modular reconfiguration of an auditory control brain network supports adaptive listening behavior

How do brain networks shape our listening behavior? We here develop and test the hypothesis that, during challenging listening situations, intrinsic brain networks are reconfigured to adapt to the listening demands and thus, to enable successful listening. We find that, relative to a task-free resting state, networks of the listening brain show higher segregation of temporal auditory, ventral attention, and frontal control regions known to be involved in speech processing, sound localization, and effortful listening. Importantly, the relative change in modularity of this auditory control network predicts individuals’ listening success. Our findings shed light on how cortical communication dynamics tune selection and comprehension of speech in challenging listening situations and suggest modularity as the network principle of auditory attention.

Speech comprehension in noisy, multitalker situations poses a challenge. Successful behavioral adaptation to a listening challenge often requires stronger engagement of auditory spatial attention and context-dependent semantic predictions. Human listeners differ substantially in the degree to which they adapt behaviorally and can listen successfully under such circumstances. How cortical networks embody this adaptation, particularly at the individual level, is currently unknown. We here explain this adaptation from reconfiguration of brain networks for a challenging listening task (i.e., a linguistic variant of the Posner paradigm with concurrent speech) in an age-varying sample of n = 49 healthy adults undergoing resting-state and task fMRI. We here provide evidence for the hypothesis that more successful listeners exhibit stronger task-specific reconfiguration (hence, better adaptation) of brain networks. From rest to task, brain networks become reconfigured toward more localized cortical processing characterized by higher topological segregation. This reconfiguration is dominated by the functional division of an auditory and a cingulo-opercular module and the emergence of a conjoined auditory and ventral attention module along bilateral middle and posterior temporal cortices. Supporting our hypothesis, the degree to which modularity of this frontotemporal auditory control network is increased relative to resting state predicts individuals’ listening success in states of divided and selective attention. Our findings elucidate how fine-tuned cortical communication dynamics shape selection and comprehension of speech. Our results highlight modularity of the auditory control network as a key organizational principle in cortical implementation of auditory spatial attention in challenging listening situations.