Biology subjects

Walhovd, K. B.

Publications and source records attributed to Walhovd, K. B..

At least 19 recordsLinked to original sources

bioRxiv2026

Punctuated memory change: The temporal dynamics and brain basis of memory stability in aging

Are there individuals who resist episodic memory decline into older age? Analyzing 728,000 memory tests from 80,000 participants with at least 4 assessments, we introduce a simulation-calibrated framework to identify genuine memory stability. Across cohorts and models, [~]10% of adults [≥]70 years showed stable performance over a decade. In an MRI subgroup (n{approx}2,000), stable performers exhibited lower rates of brain atrophy across widespread regions, anchoring cognitive stability in structural brain maintenance. However, stability was often transient rather than trait-like: many individuals followed trajectories with extended plateaus of stable performance punctuated by episodes of accelerated decline. Accordingly, 54% showed at least one period of observed stability, averaging 10 years, whereas only 0.4% upheld stable performance over 24 years under the strictest definition. These findings are consistent with a complex-systems model of cognitive aging in which decline often reflects critical transitions rather than continuous erosion.

neuroscience↗

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.

neuroscience↗

bioRxiv2025

Beyond years of schooling: Shifting genetic influences across educational milestones in two Norwegian cohorts

Although educational attainment is heritable, its conventional measurement in genetic research as years of education (EduYears) is not designed to reveal potential stage-specific genetic influences across discrete milestones. In two Norwegian cohorts (Norwegian Mother, Father and Child Cohort Study, N = 120,527; Norwegian Twin Registry, N = 8,910), we quantified the genetic contributions to completing high school, bachelors, masters and PhD using genome-wide association studies (GWAS), polygenic indices (PGIs) and twin models. Transition-specific analyses, conditioning on prior success, revealed that observed-scale common-variant heritability (h2SNP) and PGI predictability followed an inverse-U pattern, peaking at the transition into higher education (h2SNP {approx} 0.14; R2Tjur {approx} 0.05) before declining for postgraduate degrees. Genetic correlations (rg) with large-scale GWAS of EduYears (EA4) and intelligence (IQ3) were high for early transitions but declined markedly for later ones (e.g., rg with EA4 from {approx} 0.92 to {approx} 0.38). In cumulative analyses, aggregating liability across prior milestones, the gap between twin- and SNP-based heritability narrowed at higher levels of attainment (h2twin {approx} 0.6[->]0.3; h2SNP {approx} 0.22[->]0.19), while the genetic overlap between distant milestones diminished (rg {approx} 0.92[->]0.71). These patterns, obscured by EduYears metrics, highlight a dynamic genetic architecture across educational milestones, refining polygenic prediction and addressing misconceptions about uniform genetic influences on educational progression.

genetics↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2025

How and when can environmental influences change cerebral cortex? An experimental training study of twins with birth weight differences

How environmental variation shapes the human cerebral cortex remains incompletely understood. We compared cortical brainprints based in 210 twins (71 MZ and 34 DZ pairs, age 16-78 years) to distinguish prenatal, adult naturalistic and experimentally induced environmental variation from genetic contributions. Genetic effects were reflected in higher brainprint similarity within monozygotic (MZ) than dizygotic (DZ) pairs. Early environmental contributions were evident in lower brainprint similarity in MZ twin pairs with larger birthweight discordances, driven by area across the cortical ribbon. Later within-pair environmental differences in adult weight and lifestyle had minimal influence. Still, a 10-week virtual-reality navigation intervention revealed training-induced changes in the gray-white interface, with curvature and area changes supported by microstructural reconfigurations. In support of gene-environment interactions, relative brainprint similarity increased in MZ but diverged in DZ pairs following training. The results demonstrate that in adulthood, early life environmental difference persistently contributes to make the cortical architecture of genetically identical twins deviate. Environmental influence in adulthood in the form of training can still affect similarity of twin brainprints at the grey-white-matter boundary. These findings show that distinct environmental influences at prenatal and adult stage are differentially expressed across cortical features within a genetically informed framework.

neuroscience↗

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

neuroscience↗

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.

neuroscience↗

bioRxiv2024

What underlies exceptional memory function in older age? No evidence for aging-specific relationships to hippocampal atrophy and retrieval activity

Some older adults show superior memory performance compared to same-age peers, even performing on par with young participants. These are often referred to as SuperAgers. It is not known whether their superior memory function is caused by special features of their brains in aging, or whether superior memory has the same brain foundation throughout adult life. To address this, we measured hippocampal volume and atrophy, microstructural integrity by diffusion tensor imaging, and activity during an episodic memory encoding and retrieval task, in 277 cognitively healthy adults (age 20.1-81.5 years at baseline, mean 49.2 years). For quantification of hippocampal atrophy, all participants had repeated MRIs, from two to seven examinations, covering a mean of 9.3 years between first and last scan (2.5-17.3 years). 15.7% of the participants above 60 years had episodic memory scores above the mean of the young and middle-aged participants and were classified as SuperAgers. We found that superior memory in older adults was associated with higher retrieval activity in the anterior hippocampus and less hippocampal atrophy. However, there were no significant age-interactions, suggesting that the relationships reflected stable correlates of superior memory function. Although SuperAgers had superior memory compared to their same-age peers, they still performed worse than the best-performing young participants. Further, age-memory performance curves across the full age-range were similar for participants with superior memory performance compared to those with normal and low performance. These trajectories were based on cross-sectional data, but do not indicate preserved memory among the superior functioning older adults. In conclusion, the current results confirm that aspects of hippocampal structure and function are related to superior memory across age, without evidence to suggest that SuperAgers have special features compared to their younger counterparts.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2022

Is short sleep bad for the brain? Brain structure and cognitive function in short sleepers

Many sleep less than recommended without experiencing daytime tiredness. According to prevailing views, short sleep increases risk of lower brain health and cognitive function. Chronic mild sleep deprivation could cause undetected sleep debt, negatively affecting cognitive function and brain health. However, it is possible that some have less sleep need and are more resistant to negative effects of sleep loss. We investigated this question using a combined cross-sectional and longitudinal sample of 47,029 participants (age 20-89 years) with measures of self-reported sleep, including 51,295 MRIs of the brain and cognitive tests. 701 participants who reported to sleep < 6 hours did not experience daytime tiredness or sleep problems. These short sleepers showed significantly larger regional brain volumes than both short sleepers with daytime tiredness and sleep problems (n = 1619) and participants sleeping the recommended 7-8 hours (n = 3754). However, both groups of short sleepers showed slightly lower general cognitive function, 0.16 and 0.19 standard deviations, respectively. Analyses using acelerometer-estimated sleep duration confirmed the findings, and the associations remained after controlling for body mass index, depression symptoms, income and education. The results suggest that some people can cope with less sleep without obvious negative consequences for brain morphometry, in line with a view on sleep need as individualized. Tiredness and sleep problems seem to be more relevant for brain structural differences than sleep duration per se. However, the slightly lower performance on tests of general cognitive function warrants closer examination by experimental designs in natural settings. Significance statementShort habitual sleep is prevalent, with unknown consequences for brain health and cognitive performance. Here we show that daytime tiredness and sleep problems are more important variables for regional brain volumes than sleep duration. However, participants sleeping < 6 hours had slightly lower scores on tests of general cognitive function. This indicates that sleep need is individual, and that sleep duration per se may be a less relevant variable for brain health than daytime tiredness and sleep problems. The association between habitual short sleep and lower scores on tests of general cogntitive function must be further scrutinized in natural settings.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗