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

Protocol and preregistration for the CODEC project: measuring, modelling and mechanistically understanding the nature of cognitive variability in early childhood

BACKGROUND

Children's cognitive performance fluctuates across multiple timescales. However, fluctuations have often been neglected in favour of research into average cognitive performance, limiting the unique insights into cognitive abilities and development that cognitive variability may afford. Preliminary evidence suggests that greater variability is associated with increased symptoms of neurodevelopmental disorders, and differences in behavioural and neural functioning. The relative dearth of empirical work on variability, historically limited due to a lack of suitable data and quantitative methodology, has left crucial questions unanswered, which the CODEC (COgnitive Dynamics in Early Childhood) study aims to address.

METHOD

The CODEC cohort is an accelerated 3-year longitudinal study which encompasses 600 7-to-10-year-old children. Each year includes a 'burst' week (3 times per day, 5 days per week) of cognitive measurements on five cognitive domains (reasoning, working memory, processing speed, vocabulary, exploration), conducted both in classrooms and at home through experience sampling assessments. We also measure academic outcomes and external factors hypothesised to predict cognitive variability, including sleep, mood, motivation and background noise. A subset of 200 children (CODEC-MRI) are invited for two deep phenotyping sessions (in year 1 and year 3 of the study), including structural and functional magnetic resonance imaging, eye-tracking, parental measurements and questionnaire-based demographic and psychosocial measures. We will quantify developmental differences and changes in variability using Dynamic Structural Equation Modelling, allowing us to simultaneously capture variability and the multilevel structure of trials nested in sessions, days, children and classrooms.

DISCUSSION

CODEC's unique design allows us to measure variability across a range of different cognitive domains, ages, and temporal resolutions. The deep-phenotyping arm allows us to test hypotheses concerning variability, including the role of mind wandering, strategy exploration, mood, sleep, and brain structure. Due to CODEC's longitudinal nature, we are able to quantify which measures of variability at baseline predict long-term outcomes. In summary, the CODEC study is a unique longitudinal study combining experience sampling, an accelerated longitudinal 'burst' design, deep phenotyping, and cutting-edge statistical methodologies to better understand the nature, causes, and consequences of cognitive variability in children.

TRIAL REGISTRATION

ClinicalTrials.gov - NCT06330090.

Linking Cognitive Integrity to Working Memory Dynamics in the Aging Human Brain

Aging is accompanied by a decline of working memory, an important cognitive capacity that involves stimulus-selective neural activity that persists after stimulus presentation. Here, we unraveled working memory dynamics in older human adults (male and female) including those diagnosed with mild cognitive impairment (MCI) using a combination of behavioral modeling, neuropsychological assessment, and MEG recordings of brain activity. Younger adults (male and female) were studied with behavioral modeling only. Participants performed a visuospatial delayed match-to-sample task under systematic manipulation of the delay and distance between sample and test stimuli. Their behavior (match/nonmatch decisions) was fit with a computational model permitting the dissociation of noise in the internal operations underlying the working memory performance from a strategic decision threshold. Task accuracy decreased with delay duration and sample/test proximity. When sample/test distances were small, older adults committed more false alarms than younger adults. The computational model explained the participants' behavior well. The model parameters reflecting internal noise (not decision threshold) correlated with the precision of stimulus-selective cortical activity measured with MEG during the delay interval. The model uncovered an increase specifically in working memory noise in older compared with younger participants. Furthermore, in the MCI group, but not in the older healthy controls, internal noise correlated with the participants' clinically assessed cognitive integrity. Our results are consistent with the idea that the stability of working memory contents deteriorates in aging, in a manner that is specifically linked to the overall cognitive integrity of individuals diagnosed with MCI.

Brain signal variability and executive functions across the life span

Neural variability is thought to facilitate survival through flexible adaptation to changing environmental demands. In humans, such capacity for flexible adaptation may manifest as fluid reasoning, inhibition of automatic responses, and mental set-switching-skills falling under the broad domain of executive functions that fluctuate over the life span. Neural variability can be quantified via the BOLD signal in resting-state fMRI. Variability of large-scale brain networks is posited to underpin complex cognitive activities requiring interactions between multiple brain regions. Few studies have examined the extent to which network-level brain signal variability across the life span maps onto high-level processes under the umbrella of executive functions. The present study leveraged a large publicly available neuroimaging dataset to investigate the relationship between signal variability and executive functions across the life span. Associations between brain signal variability and executive functions shifted as a function of age. Limbic-specific variability was consistently associated with greater performance across subcomponents of executive functions. Associations between executive function subcomponents and network-level variability of the default mode and central executive networks, as well as whole-brain variability, varied across the life span. Findings suggest that brain signal variability may help to explain to age-related differences in executive functions across the life span.

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.

Relation of resting brain signal variability to cognitive and socioemotional measures in an adult lifespan sample

Temporal variability of the fMRI-derived blood-oxygen-level-dependent (BOLD) signal during cognitive tasks shows important associations with individual differences in age and performance. Less is known about relations between spontaneous BOLD variability measured at rest and relatively stable cognitive measures, such as IQ or socioemotional function. Here, we examined associations among resting BOLD variability, cognitive/socioemotional scores from the NIH Toolbox and optimal time of day for alertness (chronotype) in a sample of 157 adults from 20 to 86 years of age. To investigate individual differences in these associations independently of age, we regressed age out from both behavioral and BOLD variability scores. We hypothesized that greater BOLD variability would be related to higher fluid cognition scores, more positive scores on socioemotional scales and a morningness chronotype. Consistent with this idea, we found positive correlations between resting BOLD variability, positive socioemotional scores (e.g. self-efficacy) and morning chronotype, as well as negative correlations between variability and negative emotional scores (e.g. loneliness). Unexpectedly, we found negative correlations between BOLD variability and fluid cognition. These results suggest that greater resting brain signal variability facilitates optimal socioemotional function and characterizes those with morning-type circadian rhythms, but individuals with greater fluid cognition may be more likely to show less temporal variability in spontaneous measures of BOLD activity.

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.

Functional MRI responses to naturalistic stimuli are increasingly typical across early childhood

While findings show that throughout development, there are child- and age-specific patterns of brain functioning, there is also evidence for significantly greater inter-individual response variability in young children relative to adults. It is currently unclear whether this increase in functional "typicality" (i.e., inter-individual similarity) is a developmental process that occurs across early childhood, and what changes in BOLD response may be driving changes in typicality. We collected fMRI data from 81 typically developing 4-8-year-old children during passive viewing of age-appropriate television clips and asked whether there is increasing typicality of brain response across this age range. We found that the "increasing typicality" hypothesis was supported across many regions engaged by passive viewing. Post hoc analyses showed that in a priori ROIs related to language and face processing, the strength of the group-average shared component of activity increased with age, with no concomitant decline in residual signal or change in spatial extent or variability. Together, this suggests that increasing inter-individual similarity of functional responses to audiovisual stimuli is an important feature of early childhood functional brain development.

Poorer White Matter Microstructure Predicts Slower and More Variable Reaction Time Performance: Evidence for a Neural Noise Hypothesis in a Large Lifespan Cohort

Most prior research has focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual's mean. In particular, enhanced white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing Gaussian noise in signal transfer. Conversely, lower indices of WM microstructure are associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical populations. We tested a mechanistic account of the "neural noise" hypothesis in a large adult lifespan cohort (Cambridge Centre for Ageing and Neuroscience) with over 2500 adults (ages 18-102; 1508 female; 1173 male; 2681 behavioral sessions; 708 MRI scans) using WM fractional anisotropy to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model. By modeling robust and reliable individual differences in within-person variability, we found support for a neural noise hypothesis (Kail, 1997), with lower fractional anisotropy predicted individual differences in separable components of behavioral performance estimated using dynamic structural equation model, including slower mean responses and increased variability. These effects remained when including age, suggesting consistent effects of WM microstructure across the adult lifespan unique from concurrent effects of aging. Crucially, we show that variability can be reliably separated from mean performance using advanced modeling tools, enabling tests of distinct hypotheses for each component of performance.SIGNIFICANCE STATEMENT Human cognitive performance is defined not just by the long-run average, but trial-to-trial variability around that average. However, investigations of cognitive abilities and changes during aging have largely ignored this variability component of behavior. We provide evidence that white matter (WM) microstructure predicts individual differences in mean performance and variability in a sample spanning the adult lifespan (18-102). Unlike prior studies of cognitive performance and variability, we modeled variability directly and distinct from mean performance using a dynamic structural equation model, which allows us to decouple variability from mean performance and other complex features of performance (e.g., autoregression). The effects of WM were robust above the effect of age, highlighting the role of WM in promoting fast and consistent performance.

From local properties to brain-wide organization: A review of intraregional temporal features in functional magnetic resonance imaging data

Based on the fluctuations ensembled over neighbouring neurons, blood oxygen level-dependent (BOLD) signal is a mesoscale measurement of brain signals. Intraregional temporal features (IRTFs) of BOLD signal, extracted from regional neural activities, are utilized to investigate how the brain functions in local brain areas. This literature highlights four types of IRTFs and their representative calculations including variability in the temporal domain, variability in the frequency domain, entropy, and intrinsic neural timescales, which are tightly related to cognitions. In the brain-wide spatial organization, these brain features generally organized into two spatial hierarchies, reflecting structural constraints of regional dynamics and hierarchical functional processing workflow in brain. Meanwhile, the spatial organization gives rise to the link between neuronal properties and cognitive performance. Disrupted or unbalanced spatial conditions of IRTFs emerge with suboptimal cognitive states, which improved our understanding of the aging process and/or neuropathology of brain disease. This review concludes that IRTFs are important properties of the brain functional system and IRTFs should be considered in a brain-wide manner.

Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing

Using group-level functional parcellations and constant-length sliding window analysis, dynamic functional connectivity studies have revealed network-specific impairment and compensation in healthy ageing. However, functional parcellation and dynamic time windows vary across individuals; individual-level ageing-related brain dynamics are uncertain. Here, we performed individual parcellation and individual-length sliding window clustering to characterize ageing-related dynamic network changes. Healthy participants (n = 637, 18-88 years) from the Cambridge Centre for Ageing and Neuroscience dataset were included. An individual seven-network parcellation, varied from group-level parcellation, was mapped for each participant. For each network, strong and weak cognitive brain states were revealed by individual-length sliding window clustering and canonical correlation analysis. The results showed negative linear correlations between age and change ratios of sizes in the default mode, frontoparietal, and salience networks and a positive linear correlation between age and change ratios of size in the limbic network (LN). With increasing age, the occurrence and dwell time of strong states showed inverted U-shaped patterns or a linear decreasing pattern in most networks but showed a linear increasing pattern in the LN. Overall, this study reveals a compensative increase in emotional networks (i.e., the LN) and a decline in cognitive and primary sensory networks in healthy ageing. These findings may provide insights into network-specific and individual-level targeting during neuromodulation in ageing and ageing-related diseases.

Signal Variability and Cognitive Function in Older Long-Term Survivors of Breast Cancer with Exposure to Chemotherapy: A Prospective Longitudinal Resting-State fMRI Study

The purpose of this study was to assess the effect of chemotherapy on brain functional resting-state signal variability and cognitive function in older long-term survivors of breast cancer. This prospective longitudinal study enrolled women age ≥ 65 years of age who were breast cancer survivors after exposure to chemotherapy (CH), age-matched survivors not exposed to chemotherapy, and healthy controls. Participants completed resting-state functional brain MRI and neurocognitive testing upon enrollment (timepoint 1, TP1) and again two years later (timepoint 2, TP2). There were 20 participants in each of the three groups at TP1. The CH group showed a significant decrease in SD_BOLD (blood-oxygen-level-dependent signal variability in standard deviation) in the right middle occipital gyrus (ΔSD_BOLD = -0.0018, p = 0.0085, q (p_FDR) = 0.043 at MNI (42, -76, 17)) and right middle temporal gyrus (ΔSD_BOLD = -0.0021, p = 0.0006, q (p_FDR) = 0.001 at MNI (63, -39, -12)). There were negative correlations between the crystallized composite scores and SD_BOLD values at the right inferior occipital gyrus (correlation coefficient r = -0.84, p = 0.001, q (p_FDR) = 0.016) and right middle temporal gyrus (r = -0.88, p = 0.000, q (p_FDR) = 0.017) for the CH group at TP1. SD_BOLD could be a potentially useful neuroimaging marker for older long-term survivors of breast cancer with exposure to chemotherapy.

Within-individual BOLD signal variability in the N-back task and its associations with vigilance and working memory

In a group of healthy adults (N = 48), this study evaluated how fMRI Blood Oxygen Level-Dependent (BOLD) signal variability differed across letter n-back task load and quantified the extent to which BOLD signal variability was associated with in-scanner accuracy and reaction time as well as out-of-scanner measures of vigilance and working memory (WM). Within-individual BOLD signal variability in regions of interest (ROIs, identified as peak coordinates in an attention/vigilance and WM network using Neurosynth) was differentially modulated across vigilance and WM trials. Within-individual BOLD signal variability was significantly greater across the majority of the ROIs in the working memory trials (2- and 3-back trials) compared to 0-back trials. Notably, this increased variability across the network was accompanied by significantly less variability in the left cingulate gyrus and left inferior temporal lobe during the working memory trials. Significantly fewer differences in within-individual BOLD signal variability were identified for vigilance trials (0- and 1-back trials) compared to crosshair. We hypothesized that increased BOLD signal variability would be associated with n-back task performance and with out-of-scanner measures of vigilance (Digit Span Forward) and WM (Auditory Consonant Trigrams and Digit Span Backward). These results were non-significant after correcting for multiple comparisons. Furthermore, using multivariate analyses (partial least squares regression; PLS-R), within-individual BOLD signal variability in regions associated with a WM-vigilance network did not significantly predict out-of-scanner test performance after appropriate cross validation, yet provided a promising trend for WM trials; greater within-individual BOLD signal variability during WM n-back trials was associated with decreased performance on all included neuropsychological measures, which provides partial support for previous findings. This study demonstrates that patterns of variability differ based on task load in the scanner and illustrates an intriguing association between within-individual BOLD signal variability and out-of-scanner behavioral performance that may be better explored in future studies with a larger sample size.

Psychological Resilience and Neurodegenerative Risk: A Connectomics-Transcriptomics Investigation in Healthy Adolescent and Middle-Aged Females

Adverse life events can inflict substantial long-term damage, which, paradoxically, has been posited to stem from initially adaptative responses to the challenges encountered in one's environment. Thus, identification of the mechanisms linking resilience against recent stressors to longer-term psychological vulnerability is key to understanding optimal functioning across multiple timescales. To address this issue, our study tested the relevance of neuro-reproductive maturation and senescence, respectively, to both resilience and longer-term risk for pathologies characterised by accelerated brain aging, specifically, Alzheimer's Disease (AD). Graph theoretical and partial least squares analyses were conducted on multimodal imaging, reported biological aging and recent adverse experience data from the Lifespan Human Connectome Project (HCP). Availability of reproductive maturation/senescence measures restricted our investigation to adolescent (N =178) and middle-aged (N=146) females. Psychological resilience was linked to age-specific brain senescence patterns suggestive of precocious functional development of somatomotor and control-relevant networks (adolescence) and earlier aging of default mode and salience/ventral attention systems (middle adulthood). Biological aging showed complementary associations with the neural patterns relevant to resilience in adolescence (positive relationship) versus middle-age (negative relationship). Transcriptomic and expression quantitative trait locus data analyses linked the neural aging patterns correlated with psychological resilience in middle adulthood to gene expression patterns suggestive of increased AD risk. Our results imply a partially antagonistic relationship between resilience against proximal stressors and longer-term psychological adjustment in later life. They thus underscore the importance of fine-tuning extant views on successful coping by considering the multiple timescales across which age-specific processes may unfold.