Biology subjects

Fjell, A. M.

Publications and source records attributed to Fjell, A. M..

At least 19 recordsLinked to original sources

bioRxiv2025

Phase-Specific Hippocampal and Cortical Medial Temporal Lobe Involvement in Allocentric Working Memory

The hippocampus and cortices of the medial temporal lobe (MTL) are increasingly implicated in working memory, particularly for tasks requiring complex, high-resolution relational binding, but their precise contributions--particularly during delay maintenance--remain debated. Using functional magnetic resonance imaging (fMRI), we investigated the involvement of hippocampal and cortical MTL in a demanding allocentric working memory task requiring high-resolution, relational binding. During fMRI, 128 healthy human adults (92 females) aged 20-83 (mean 39) years performed a task in which object-location bindings had to be learned, maintained, and manipulated across an 8 second delay. The design included a passive viewing condition to control for perceptual and attentional demands and a staircase procedure to balance task difficulty across participants. Across the full sample, anterior and mid hippocampal subregions and cortical MTL areas were engaged during encoding. In the delay phase, we observed a non-uniform pattern, with hippocampal and entorhinal deactivation alongside perirhinal and parahippocampal activation. During test, activation occurred in posterior hippocampus and several cortical MTL regions. Contrary to predictions, hippocampal and cortical MTL activation did not vary with performance among younger adults. Instead, differences emerged in a left temporoparietal cluster, potentially reflecting verbal encoding strategies. Older adults, relative to younger adults with the most comparable performance levels, showed lower anterior hippocampal activation and smaller correct-passive viewing differences in perirhinal and parahippocampal cortices. Taken together, these findings demonstrate distinct phase-specific hippocampal and cortical MTL involvement across the temporal unfolding of allocentric working memory, with evidence of hippocampal engagement even during the delay, yet the absence of MTL-performance associations among younger adults suggests this involvement, while robust, may play a more auxiliary role.

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bioRxiv2025

Memory-Related Default-Executive Coupling Across the Lifespan and Associations with Changes in Cognitive Control

Episodic memory and cognitive control declines in aging. The default-executive coupling hypothesis of aging (DECHA) suggests that a neural correlate of cognitive decline in aging is increased functional connectivity (FC) between lateral prefrontal areas and the default mode network. Here, in a lifespan sample (n=552, 6-81years), we tested FC between the left dorsolateral prefrontal cortex (dLPFC) and the default mode network (DMN) during the encoding and retrieval phases of an episodic memory fMRI task. We created two age groups based on evidence for episodic memory decline after 30 years: a youth group encompassing childhood, adolescence, and young adulthood (6-29 years) and an aging group (30-81 years). To test if the dLPFC-DMN FC was associated with changes in cognitive control, we used longitudinal change (up to 10 years) in a Stroop inhibition/switching task linked to the prefrontal cortex. Results showed (i) lower dLPFC and DMN connectivity with age in the youth group and higher connectivity in the aging group, and (ii) this FC was associated with an age-related increase in the inhibition task completion time, particularly in the aging group. However, dLPFC showed similar relationships with other networks, particularly salient attentional and control subnetworks, and despite decline in cognitive control associated with memory performance, memory-related FC between dLPFC and DMN did not. Although this link with memory performance remains unclear, the results using longitudinal cognitive data align with the DECHA mechanisms and extends the current proposal by indicating inverse relationships in development and the relevance of additional attentional and control network coupling.

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bioRxiv2025

Cortical thickness changes precede high levels of amyloid by at least seven years

Alzheimers disease (AD) is now defined based on its underlying brain pathology1, with the presence of amyloid (A{beta}) plaques at high enough levels sufficient to warrant a diagnosis in the absence of cognitive symptoms. High levels of PET-detectable A{beta} are widely thought to be the first imaging marker, with structural brain changes detectable on MRI scans thought to occur later. We combined 4570 longitudinal MRIs and 1684 A{beta} PET scans from three cognitively healthy cohorts to test the difference in cortical thickness and its change between those that subsequently converted to be A{beta}-positive or stayed A{beta}-negative, using MRIs acquired exclusively in the years before conversion. We found those that subsequently developed elevated A{beta} levels show both thicker cortex and less cortical thinning, even when the last MRI used to estimate their thickness trajectories was acquired at least seven years before conversion. Many effects remained when accounting for quantitative A{beta} levels, suggesting some cortical thickness effects may be partly independent of A{beta}. Differences in cortical thickness and its change between converters and A{beta}-negative individuals showed moderate alignment with patterns of A{beta} deposition, and the timing of thickness changes tracked the temporal progression of A{beta} accumulation. Thus, if amyloid is AD1, we show that high levels of PET-detectable amyloid are not the first imaging marker of AD, as cortical thickness changes can be traced years before pathological amyloid. This has implications for understanding the sequence of events leading up to the earliest stages of AD.

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bioRxiv2025

Distinguishing Lifelong Individual Differences from Divergent Aging Trajectories of Adult Brain Volumes

Individual differences in the volumes of brain structures are often linked to various conditions, including Alzheimers disease, schizophrenia, and overall brain health. However, it remains unclear to what extent these differences reflect individual levels present from young adulthood or diverging aging trajectories from later ages. In this study, we analyze the aging dynamics of the volumes of six brain structures based on magnetic resonance imaging (MRI) scans from a large cross-cohort longitudinal sample of cognitively healthy adults (n = 8,311 with 18,520 MRIs, ages from 18 to 97 years). From general assumptions about structural brain dynamics and measurement noise, a stochastic dynamical model was fitted to the data to estimate both the variability and persistence of structural changes across adulthood. Using this model, we calculated how much of the variance of volumetric differences between individuals can be attributed to stable levels from young adulthood versus systematic changes at older ages, as well as the theoretical sensitivity of longitudinal studies to detect individual differences in change. The findings were as follows: 1) Before age 60 years, inter-individual differences in neuroanatomical volumes almost exclusively reflect stable differences between individuals, while the influence from systematic differences in rate-of-change increases thereafter; up to 50 % of the variation being due to differences in change at 80 years. In contrast, ventricular volume reflects differences in change from early adulthood. 2) Current brain-age models are unlikely to be sensitive to detect differences in aging trajectories. 3) Imaging studies have low reliability in detecting inter-individual brain changes before age 60. After 60 years, the study reliability increases sharply with longer intervals between scans and more modestly with additional intermediate observations. In conclusion, our results reinforce the view that it is critical to distinguish stable early-adulthood levels from systematic differences in change when studying adult brain aging.

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bioRxiv2025

Differential expression of spatiotemporal sleep spindle clusters in ageing

ObjectivesSleep spindles are potential biomarkers for memory decline in aging. However, significant within-person variability in spindle attributes complicates their utility in predicting cognitive deterioration. This study aimed to uncover distinct spindle types and their relevance to memory decline using data-driven clustering. MethodsPolysomnography was collected from younger (n = 43, ages 20-45 years) and older cognitively healthy adults (n = 34, ages 60-81 years). Spindles were clustered into four groups using multiple features and spatiotemporal context, irrespective of participant age. ResultsResulting clusters were hierarchically defined by the sleep stage, slow oscillation concurrence, and hemisphere. Stage N3 spindles (15%; predominantly coinciding with slow oscillations) formed a distinct group, followed by N2 spindles coinciding with slow oscillations (27%). Remaining N2 spindles were categorized into unilateral (41%) and bilateral clusters (17%). In older adults, there was a reduced proportion of N2 bilateral spindles and an increased proportion of N2 spindles concurrent with slow oscillations. Reduced proportion of N2 bilateral spindles was associated with better composite memory performance in younger adults, whereas higher spindle power, regardless of cluster belonging, was associated with reduced memory benefit from sleep compared with wakefulness. ConclusionsOur results indicate differing expression of spatiotemporal spindle clusters in older age, as well as intertwined dynamics between spindle propagation, SO concurrence, and frequency shifts in ageing. In addition, spindle heterogeneity aligned with global sleep stage dynamics. These results emphasize the interconnectedness of spindle activity with overall sleep patterns, underscoring the importance of spatiotemporal context within and across sleep stages. Statement of significanceThis study used data-driven clustering to explore sleep spindles as potential markers for age-related memory decline. We identified spindle clusters determined by sleep stage, slow oscillation concurrence, and hemisphere propagation. Notably, older adults showed altered expression of these clusters, indicating age-specific dynamics. Further research should focus on distinguishing spindle deterioration from broader sleep changes in older age. Such insights could pave the way for early detection and intervention strategies in cognitive decline, highlighting sleeps crucial role in maintaining cognitive health and resilience in aging populations. These findings hold promise for developing targeted approaches to enhance mental wellness and quality of life in older adults.

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bioRxiv2025

Age- and Sex-Related Differences in Sleep Patterns and Their Relations to Self-Reported Sleep and Mood

Robust age- and sex-related differences in sleep were identified (n=38,546) and replicated (n=38,547) from week-long passive actigraphy data in UK Biobank participants ages 44-82. Sleep patterns reflected reliable non-linear interactions between age and sex. Younger women slept about 17 min more than their male counterparts, though this difference diminished with age, with both sexes reducing total sleep duration in later life. Middle-aged individuals exhibited shorter sleep durations during the week, with weekend sleep increasing by as much as 50 min. Participants in their seventh and eighth decades showed more consistent sleep patterns throughout the week. Sleep patterns also suggest maintenance of total sleep duration: individuals reporting waking too early maintain sleep duration by going to sleep earlier, while individuals reporting sleeping too much fall asleep later but also wake later, again maintaining sleep duration. Self-reported depression and anhedonia were associated with reduced total sleep duration across multiple age groups and both sexes. These collective results indicate that the timing, consistency, and overall amount of sleep differs by age and is affected by individual factors.

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bioRxiv2025

Vulnerability to memory decline in aging. A mega-analysis of structural brain change.

Brain atrophy is a key factor behind episodic memory loss in aging, but the nature and ubiquity of this relationship remains poorly understood. This study leveraged 13 longitudinal datasets, including 3,737 cognitively healthy adults (10,343 MRI scans; 13,460 memory assessments), to determine whether brain change-memory change associations are more pronounced with age and genetic risk for Alzheimers Disease. Both factors are associated with accelerated brain decline, yet it remains unclear whether memory loss is exacerbated beyond what atrophy alone would predict. Additionally, we assessed whether memory decline aligns with a global pattern of atrophy or stems from distinct regional contributions. Our mega-analysis revealed a nonlinear relationship between memory decline and brain atrophy, primarily affecting individuals with above-average brain structural decline. The associations were stronger in the hippocampus but also spread across diverse cortical and subcortical regions. The associations strengthened with age, reaching moderate associations in participants in their eighties. While APOE {varepsilon}4 carriers exhibited steeper brain and memory loss, genetic risk had no effect on the change-change associations. These findings support the presence of common biological macrostructural substrates underlying memory function in older age which are vulnerable to multiple age-related factors, even in the absence of overt pathological changes.

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bioRxiv2024

Network Segregation During Episodic Memory Shows Age-Invariant Relations with Memory Performance From 7 to 82 Years

Lower episodic memory capability, as seen in development and aging compared with younger adulthood, may partly depend on lower brain network segregation. Here, our objective was twofold: (1) test this hypothesis using within- and between-network functional connectivity (FC) during episodic memory encoding and retrieval, in two independent samples (n=734, age 7-82 years). (2) Assess associations with age and the ability to predict memory comparing task-general FC and memory-modulated FC. In a multiverse-inspired approach, we performed tests across multiple analytic choices. Results showed that relationships differed based on these analytic choices, were often weak, and mainly present in the cohort with the most data. Significant relationships indicated that (i) memory-modulated FC predicted memory performance and associated with memory in an age-invariant manner. (ii) In line with the so- called neural dedifferentiation view, task-general FC showed lower segregation with higher age in adults which was associated with worse memory performance. In development, although there were only weak signs of a neural differentiation, that is, gradually higher segregation with higher age, we observed similar lower segregation-worse memory relationships. This age-invariant relationships between FC and episodic memory suggest that network segregation is pivotal for memory across the healthy lifespan. Declarations of interestnone. HighlightsO_LIWithin- and between network functional connectivity predict memory performance. C_LIO_LIMultiverse-inspired analyses showed varying results depending on analytic choices. C_LIO_LIMemory associations particularly in the cohort with most data were age-invariant across the lifespan. C_LIO_LIDedifferentiation might be better characterized as degrees of differentiation. C_LI

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bioRxiv2024

Reliability of structural brain change in cognitively healthy adult samples.

In neuroimaging research, tracking individuals over time is key to understanding the interplay between brain changes and genetic, environmental, or cognitive factors across the lifespan. Yet, the extent to which we can estimate the individual trajectories of brain change over time with precision remains uncertain. In this study, we estimated the reliability of structural brain change in cognitively healthy adults from multiple samples and assessed the influence of follow-up time and number of observations. Estimates of cross-sectional measurement error and brain change variance were obtained using the longitudinal FreeSurfer processing stream. Our findings showed, on average, modest longitudinal reliability with two years of follow-up. Increasing the follow-up time was associated with a substantial increase in longitudinal reliability while the impact of increasing the number of observations was comparatively minor. On average, 2-year follow-up studies require {approx}2.7 and {approx}4.0 times more individuals than designs with follow-ups of 4 and 6 years to achieve comparable statistical power. Subcortical volume exhibited higher longitudinal reliability compared to cortical area, thickness, and volume. The reliability estimates were comparable to those estimated from empirical data. The reliability estimates were affected by both the cohorts age where younger adults had lower reliability of change, and the preprocessing pipeline where the FreeSurfers longitudinal stream was notably superior than the cross-sectional. Suboptimal reliability inflated sample size requirements and compromised the ability to distinguish individual trajectories of brain aging. This study underscores the importance of long-term follow-ups and the need to consider reliability in longitudinal neuroimaging research.

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bioRxiv2024

Subtypes of brain change in aging and their associations with cognition and Alzheimer's disease biomarkers

Structural brain changes underly cognitive changes in older age and contribute to inter-individual variability in cognition. Here, we assessed how changes in cortical thickness, surface area, and subcortical volume, are related to cognitive change in cognitively unimpaired older adults using structural magnetic resonance imaging (MRI) data-driven clustering. Specifically, we tested (1) which brain structural changes over time predict cognitive change in older age (2) whether these are associated with core cerebrospinal fluid (CSF) Alzheimers disease (AD) biomarkers phosphorylated tau (p-tau) and amyloid-{beta} (A{beta}42), and (3) the degree of overlap between clusters derived from different structural features. In total 1899 cognitively healthy older adults (50 - 93 years) were followed up to 16 years with neuropsychological and structural MRI assessments, a subsample of which (n = 612) had CSF p-tau and A{beta}42 measurements. We applied Monte-Carlo Reference-based Consensus clustering to identify subgroups of older adults based on structural brain change patterns over time. Four clusters for each brain feature were identified, representing the degree of longitudinal brain decline. Each brain feature provided a unique contribution to brain aging as clusters were largely independent across modalities. Cognitive change and baseline cognition were best predicted by cortical area change, whereas higher levels of p-tau and A{beta}42 were associated with changes in subcortical volume. These results provide insights into the link between changes in brain morphology and cognition, which may translate to a better understanding of different aging trajectories.

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bioRxiv2023

Accelerated brain change in healthy adults is associated with genetic risk for Alzheimer's disease and uncovers adult lifespan memory decline

Across healthy adult life our brains undergo gradual structural change in a pattern of atrophy that resembles accelerated brain changes in Alzheimers disease (AD). Here, using four polygenic risk scores for AD (PRS-AD) in a longitudinal adult lifespan sample aged 30 to 89 years (2-7 timepoints), we show that healthy individuals who lose brain volume faster than expected for their age, have a higher genetic AD risk. We first demonstrate PRS-AD associations with change in early Braak regions, namely hippocampus, entorhinal cortex, and amygdala, and find evidence these extend beyond that predicted by APOE genotype. Next, following the hypothesis that brain changes in ageing and AD are largely shared, we performed machine learning classification on brain change trajectories conditional on age in longitudinal AD patient-control data, to obtain a list of AD-accelerated features and model change in these in adult lifespan data. We found PRS-AD was associated with a multivariate marker of accelerated change in many of these features in healthy adults, and that most individuals above [~]50 years of age are on an accelerated change trajectory in AD-accelerated brain regions. Finally, high PRS-AD individuals also high on a multivariate marker of change showed more adult lifespan memory decline, compared to high PRS-AD individuals with less brain change. Our results support a dimensional account linking normal brain ageing with AD, suggesting AD risk genes speed up the shared pattern of ageing- and AD-related neurodegeneration that starts early, occurs along a continuum, and tracks memory change in healthy adults.

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bioRxiv2022

Prenatal development has stable and consistent effects on the human brain throughout the lifespan

Human fetal development has been associated with brain health at later stages. It is unknown whether growth in utero, as indexed by birth weight (BW), relates consistently to lifespan brain characteristics and changes, and to what extent these influences are of a genetic or environmental nature. Here we show remarkably stable and life-long positive associations between BW and cortical surface area and volume across and within developmental, aging and lifespan longitudinal samples (N = 5794, 4-82 years of age, w/ 386 monozygotic twins, followed for up to 8.3 years w/12,088 brain MRIs). In contrast, no consistent effect of BW on brain changes was observed. Partly environmental effects were indicated by analysis of twin BW discordance. In conclusion, the influence of prenatal growth on cortical topography is stable and reliable through the lifespan. This early life factor appears to influence the brain by association of brain reserve, rather than brain maintenance. Thus, fetal influences appear omnipresent in the spacetime of the human brain throughout the human lifespan. Optimizing fetal growth may increase brain reserve for life, also in aging.

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bioRxiv2022

Hippocampal-cortical functional connectivity during memory encoding and retrieval

Memory encoding and retrieval are critical sub-processes of episodic memory. While the hippocampus is involved in both, its connectivity with the neocortex during memory processing in humans has been elusive. This is partially due to variations in demands in common memory tasks, which inevitably recruit cognitive processes other than episodic memory. Conjunctive analysis of data from different tasks with the same core elements of encoding and retrieval can reduce the intrusion of patterns related to subsidiary perceptual and cognitive processing. Leveraging data from two large-scale functional resonance imaging studies with different episodic memory tasks (514 and 237 participants), we identified core hippocampal-cortical networks active during memory processing. Anterior and posterior hippocampus had distinct connectivity profiles, which were stable across resting state and memory tasks. Whereas no encoding-specific connectome emerged across tasks, during retrieval hippocampal connectivity was increased with areas known to be active during recollection, including medial prefrontal, inferior parietal, and parahippocampal cortices. This indicates that the stable functional connectivity of the hippocampus along its longitudinal axis is superposed by increased functional connectivity with the recollection network during retrieval, while encoding connectivity likely reflects contextual factors.

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bioRxiv2022

Normalization of MRI T1w between-scan effects for improved longitudinal volumetric estimates

Variations in image intensities and contrasts between magnetic resonance imaging (MRI) acquisitions affect the subsequent image processing and its derived outcomes. Therefore, comparability between acquisitions is improved if we reduce these variations. This is especially relevant for longitudinal studies where a change of scanner or acquisition protocol often happens between subsequent examinations. In this study, we use a robust intensity distribution alignment (RIDA) method to reduce between-scan effects and improve longitudinal volume change estimates between two MRI scanners - Siemens 1.5T Avanto and 3T Skyra. The method is based on MRI T1w images acquired in close succession and robustly aligns two cumulative distribution functions of voxel intensities to harmonize image intensities and improve image-derived outcomes of a range of subcortical brain. We compare RIDA with volume-based correction - a simple linear regression model. In both cases, we derive intensity and volume transformations from a training dataset of 20 participants scanned on both scanners on the same day and apply to an independent longitudinal test dataset of 243 participants. All participants in the test set were scanned at the Avanto scanner at the baseline and then at the Avanto and Skyra scanners on the same day at the follow-up, on average 4.4 years (sd = 0.5 years) later. This allowed us to directly assess the effect of scanner and protocol change on the longitudinal change estimates. Eight subcortical brain regions were segmented using SAMSEG, and annualized symmetrized percent change in volume between time points was calculated. We find that RIDA significantly reduces between-scan effects and improves longitudinal volume estimates for the amygdala and lateral ventricles. It also reduces between-scan effects for caudate, putamen, and thalamus, but not as much as linear regression models. Whether the method will be useful for a particular study will depend on the image intensity profiles of the scans. Therefore, a pilot study of double-scanned participants is recommended to assess the advantages of the method for the analysis in question.

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bioRxiv2022

A robust intensity distribution alignment for harmonization of T1w intensity values

Variations in image intensities between magnetic resonance imaging (MRI) acquisitions affect the subsequent image processing and its derived outcomes. Therefore, it is necessary to normalize images of different scanners/acquisitions, especially for longitudinal studies where a change of scanner or pulse sequence often happens. Here, we propose a robust intensity distribution alignment (RIDA) method to remove between-scan effects. The method is based on MRI T1w images acquired in close succession and robustly aligns two cumulative distribution functions (CDF) of voxel intensities to improve image-derived outcomes of a range of subcortical brain structures with different acquisition parameters. We compare RIDA with the other image harmonization methods: mica and RAVEL. We study three intra-scanner and three inter-scanner protocol variations among the same 20 participants scanned with Siemens 1.5T Avanto, 3T Skyra, and 3T Prisma scanners on the same day and use image-derived volumetric outputs from the Sequence Adaptive Multimodal Segmentation (SAMSEG) method. We find that CDF-based intensity harmonization (mica and RIDA) significantly reduces intensity differences, improves consistency in volume quantification, and increases spatial overlap between two images acquired in close succession. The improvements are most considerable if the intensity normalization is based on subcortical structures only (RIDA), excluding cortical regions, instead of the whole brain. However, the effect of the corrections varies considerably as a function of the compared scanners and sequences. In conclusion, the RIDA scaneffect normalization improves the consistency of image-derived measures, but its performance depends on several factors.

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bioRxiv2022

Sustained upregulation of widespread hippocampal-neocortical coupling following memory encoding

Systems consolidation of new experiences into lasting episodic memories involves interactions between hippocampus and the neocortex. Evidence of this process is seen already during early awake post-encoding rest periods. Functional MRI (fMRI) studies have demonstrated increased hippocampal coupling with task-relevant perceptual regions and reactivation of stimulus-specific encoding patterns following intensive encoding tasks. Here we investigate the spatial and temporal characteristics of these hippocampally anchored post-encoding neocortical modulations. Eighty-nine adults participated in an experiment consisting of interleaved memory task- and resting-state periods. As expected, we observed increased post-encoding functional connectivity between hippocampus and individually localized neocortical regions responsive to stimulus categories encountered during memory encoding. Post-encoding modulations were however not restricted to stimulus-selective cortex, but manifested as a nearly system-wide upregulation in hippocampal coupling with all major functional networks. The spatial configuration of these extensive modulations resembled hippocampal-neocortical interaction patterns estimated from active encoding operations, suggesting hippocampal post-encoding involvement by far exceeds reactivation of perceptual aspects. This reinstatement of encoding patterns during immediate post-encoding rest was not observed in resting-state scans collected 12 hours later, nor in control analyses estimating post-encoding neocortical modulations in functional connectivity using other candidate seed regions. The broad similarity in hippocampal functional coupling between online memory encoding and offline post-encoding rest suggests reactivation in humans may involve a spectrum of cognitive processes engaged during experience of an event. Significance statementStabilization of newly acquired information into lasting memories occurs through systems consolidation - a process which gradually spreads the locus of memory traces from hippocampus to more distributed neocortical representations. One of the earliest signs of consolidation is the upregulation of hippocampal-neocortical interactions during periods of awake rest following an active encoding task. We here show that these modulations involve much larger parts of the brain than previously reported in humans. Comparing changes in hippocampal coupling during post-encoding rest with those observed under active encoding, we find evidence for encoding-like hippocampal reinstatement throughout cortex during task-free periods. This suggests early systems consolidation of an experience involves reactivating not only core sensory details but multiple additional aspects of the encoding event.

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bioRxiv2021

Population-level asymmetry of the cerebral cortex: reproducibility, lifespan changes, heritability, and individual differences

Cortical asymmetry is a ubiquitous feature of brain organization that is subtly altered in some neurodevelopmental disorders, yet we lack knowledge of how its development proceeds across life in health. Achieving consensus on the precise cortical asymmetries in humans is necessary to uncover the genetic and later influences that shape them, such as age. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in 7 datasets and chart asymmetry trajectories longitudinally across life (4-89 years; observations = 3937; 70% longitudinal). We find replicable asymmetry interrelationships, heritability maps, and test asymmetry associations in large-scale data. Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in childhood and peaks in early adulthood. Areal asymmetry correlates phenotypically and genetically in specific regions, and is low-moderately heritable (max h2SNP [~]19%). In contrast, thickness asymmetry is globally interrelated across the cortex in a pattern suggesting highly left-lateralized individuals tend towards left-lateralization also in population-level right-asymmetric regions (and vice versa), and exhibits low or absent heritability. We find less areal asymmetry in the most consistently lateralized region in humans associates with subtly lower cognitive ability, and confirm small handedness and sex effects. Results suggest areal asymmetry is developmentally stable and arises in early life through genetic but mainly subject-specific stochastic effects, whereas childhood developmental growth shapes thickness asymmetry and may lead to directional variability of global thickness lateralization in the population.

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bioRxiv2021

Whole-brain connectivity during encoding: age-related differences and associations with cognitive and brain structural decline

There is a limited understanding of age differences in functional connectivity during memory encoding. In the present study, a sample of cognitively healthy adult participants (n=488), a subsample of whom had longitudinal cognitive and brain structural data spanning 8 years back, underwent fMRI while performing an associative memory encoding task. We investigated 1) age changes in whole-brain connectivity during memory encoding; whether 2) encoding connectivity patterns overlap with the activity signatures of specific cognitive processes and whether 3) connectivity changes associated with memory encoding related to longitudinal brain structural and cognitive changes. Age was associated with decreased intranetwork connectivity and increased connectivity during encoding. Task-connectivity between mediotemporal and posterior parietal regions - which overlapped with areas involved in mental imagery - was related to better memory performance only in older age. The connectivity patterns supporting memory performance in older age reflected preservation of thickness of the medial temporal cortex. These investigations collectively indicate that functional patterns of connectivity should be interpreted in accordance with a maintenance rather than a compensation account.

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