Biology subjects

Bijsterbosch, J. D.

Publications and source records attributed to Bijsterbosch, J. D..

At least 19 recordsLinked to original sources

bioRxiv2026

HARMONY: A large-scale harmonized neuroimaging dataset for research on anxious misery disorders

Patterns of brain circuit dysfunction underlying depression and anxiety have been increasingly characterized, including dimensional and subtype variation. A key challenge is determining how such patterns generalize across populations and measurement frameworks. Here, we introduce HARMONY, a harmonized multimodal neuroimaging dataset supporting large-scale investigation of brain-behavior associations across symptom-defined dimensions. HARMONY integrates four Human Connectome Project-style Connectomes Related to Human Disease cohorts spanning adolescence to later adulthood and capturing anxious misery symptoms. The resource combines standardized HCP-style preprocessing, quality control, imaging-derived phenotypes, and harmonized symptom measures into a clinically enriched public dataset. Proof-of-concept analyses using HARMONY showed that pooling heterogeneous cohorts increased statistical power for detecting associations between imaging-derived phenotypes and anhedonia and depression severity. Effect sizes remained modest, consistent with symptom-based measures across heterogeneous samples. Functional imaging-derived phenotypes showed the strongest multivariate predictive performance. In summary, HARMONY provides a large multi-cohort resource for reproducible mental health neuroimaging research.

neuroscience↗

bioRxiv2026

Do Symptoms Matter? Investigating Symptom-Based Lesion Network Mapping.

Lesion network mapping (LNM) describes a group of methods using normative functional connectivity data to map disparate brain lesions and stimulation sites onto common brain networks. Van den Heuvel and colleagues recently showed that these methods lack disease specificity, instead producing maps that converge toward intrinsic properties of the normative connectome dataset. Here, we investigate symptom LNM (sLNM), a recent advancement in the method which attempts to increase the robustness of results by incorporating symptom severity and incorporating replication across multiple datasets and prediction of clinical outcomes. Using clinical datasets of depression and Brocas aphasia, we show that sLNM maps from unrelated disorders nonetheless converge despite using null models which break the specific lesion-symptom structure in the datasets. Using simulated datasets with a known ground-truth disease network, we show that sLNM results are systematically biased towards the normative connectomes first principal component (PC1), which drives spurious convergence across unrelated datasets. We further show that the apparent clinical predictive capability of these maps are non-specific: network maps derived from unrelated disorders such as migraine and aphasia predict brain stimulation improvement in depression as well as -- or better than -- the cohorts own sLNM map. However, controlling for PC1 reduces spurious convergence across unrelated datasets and improves clinical prediction specificity, supporting the notion that disease-specific signal exists within sLNM but is confounded by the globally present PC1 signal in the normative connectome. These findings offer a practical correction applicable to existing and future sLNM studies.

neuroscience↗

bioRxiv2025

Profiles of Aging Based on Cognition, Affect, and Brain Reserve

The aging paradox describes improvements in emotional wellbeing as a function of aging, despite declines in cognition. Conversely, late life depression has been associated with increased cognitive decline in aging. We sought to understand these seemingly contradictory patterns of cognitive and mental health in older age. Building on cognitive reserve, affective reserve, and brain reserve models of aging, we developed three alternative algorithmic approaches to group N=22,686 participants from the UK Biobank into different profiles of aging. Our results revealed that aging profiles identified using our data-driven brain reserve model, which incorporated measures of cognition, neuroticism, and brain volume, achieved the highest validation results. Importantly, only two of the four aging profiles were characterized by the aging paradox (i.e., improved emotionality and decreased cognition with age). We identified one profile characterized by particularly low levels of neuroticism and relative resilience to cognitive decline. Another profile benefited from relatively preserved brain volumes, potentially driven by younger ages and/or higher socioeconomic status. Conversely, we identified two profiles with poorer health characteristics, including one profile with elevated cardiovascular risk. Taken together, these findings enrich our understanding of the emotion paradox and highlight the value of taking a nuanced and stratified approach when studying aging. In the future, aging profiles could be used to target preventative strategies to address modifiable risk factors and improve lifespan and healthspan.

neuroscience↗

bioRxiv2025

Investigating Neural Correlates of Emotional Regulation as a Function of Age, Race, and Socioeconomic Status

Older adults often show improved emotional regulation with age, a phenomenon known as the aging paradox. This age-related increase in emotional regulation capacity is attributed to enhanced prefrontal cortex control over amygdala reactivity. However, because racial discrimination and economic disadvantage cause chronic stress, typical age-related neural associations may be altered in marginalized groups. Using task-functional MRI data from 8,711 UK Biobank participants aged 50-78, we investigated whether age-related associations in emotion-related brain function, specifically amygdala activation and vmPFC-amygdala connectivity, varied across racial and socioeconomic status (SES) groups. We found that older age was associated with decreased amygdala activation, which is consistent with improved emotional regulation. Yet, lower socioeconomic status was associated with increased amygdala activation, suggesting heightened stress-related reactivity. No significant age-related effects on vmPFC-amygdala connectivity were observed at the population level. Black participants showed a stronger age-related decline in functional connectivity compared to other racial groups. These findings call for more inclusive and diverse neuroimaging studies to better understand brain health across marginalized groups.

neuroscience↗

bioRxiv2025

The neuroimaging correlates of depression established across six large-scale datasets

Neuroimaging data offers noninvasive insights into the structural and functional organization of the brain and is therefore commonly used to study the neuroimaging correlates of depression. To date, a substantial body of literature has suggested reduced size of subcortical regions and abnormal functional connectivity in frontal and default mode networks linked to depression. However, recent meta analyses have failed to identify significant converging correlates of depression across the literature such that a conclusive mapping of the neuroimaging correlates of depression remains elusive. Here we leveraged 23,417 participants across six datasets to comprehensively establish the neuroimaging correlates of depression. We found reductions in gray matter volume/ cortical surface area associated with depression in the frontal cortex, anterior cingulate, and insula, confirming prior studies showing the importance of prefrontal and default mode regions in depression. Our findings demonstrate multiple surprising results, including a lack of depression correlates in subcortical brain regions, significant depression correlates in somatomotor and visual regions, and limited functional connectivity findings. Overall, these results shed new light on key brain regions involved in the pathophysiology of depression, updating our understanding of the neuroimaging correlates of depression. We anticipate that these insights will inform further research into the role of sensorimotor and visual regions in depression and into the impact of heterogeneity on functional connectivity correlates of depression.

neuroscience↗

bioRxiv2025

Automatic Individual Cortical Parcellation for the Human Connectome Project

The original Human Connectome Project multimodal cortical parcellation (HCP_MMP1.0) used MRI-derived local features and long-distance functional connectivity measures to define a multimodal parcellation at the group level, accompanied by an automated areal classifier, to create subject-specific mappings of the human cerebral cortex. These mappings, referred to as individual (cortex) parcellations, aim to capture individual variability in areal organization by learning from both structural and functional data. However, a strict supervised learning approach using the group parcellation as labels would have no incentive to learn individual differences that registration is unable to reconcile (e.g., atypical 55b topologies). Furthermore, there are many types of resting state network (RSN) feature maps, and it is unclear which type would most accurately or effectively classify areas, or even what should be the primary criteria for evaluating classification performance. Here, we introduce an Areal Recognition Ensemble with Nested Approach (ARENA) classifier that learns from uncertain labels, using a novel application of weakly supervised learning to this type of problem. Additionally, in comparing multiple candidate RSN decompositions, temporal ICA and PROFUMO maps outperformed the original spatial ICA-based approach based on objective criteria. With these refinements, the ensemble classifier achieved a reliable individual variability score of 8380, an average areal detection rate of 97.8%, and test-retest reproducibility of 73.3%, outperforming a retrained version of the original Multi-layer Perceptron (MLP) model (whose reliable individual variability score was 3128, average areal detection rate was 97.2%, and test-retest reproducibility was 71.6% on the same dataset). Furthermore, the ARENA classifier demonstrated stronger generalization for all three measures when applied to task fMRI data that were not part of the training dataset. Using the refined classifier and leveraging all 1071 HCP-Young Adult subjects, we identified new types of atypical organization of language-related area 55b. Here we provide the fully data-driven HCP_MMP1.0_1071_MPM (Maximum Probability Map) group parcellation and a summary of area 55b organization in both hemispheres. Our automated individual parcellation pipeline powered by the novel ARENA classifier is now integrated into the HCP pipelines, offering a user-friendly tool for the neuroimaging community.

neuroscience↗

bioRxiv2024

Multiscale Modes of Functional Brain Connectivity

Information processing in the brain spans from localised sensorimotor processes to higher-level cognition that integrates across multiple regions. Interactions between and within these subsystems enable multiscale information processing. Despite this multiscale characteristic, functional brain connectivity is often either estimated based on 10-30 distributed modes or parcellations with 100-1000 localised parcels, both missing across-scale functional interactions. We present Multiscale Probabilistic Functional Modes (mPFMs), a new mapping which comprises modes over various scales of granularity, thus enabling direct estimation of functional connectivity within- and across-scales. Crucially, mPFMs were not formulated, but emerged from data-driven multilevel Bayesian modelling of large functional MRI (fMRI) populations and every individual. We demonstrate that mPFMs capture both distributed brain modes and their co-existing subcomponents. In addition to validating mPFMs using simulations and real data, we show that mPFMs can predict [~]900 personalised traits from UK Biobank more accurately than current standard techniques. Therefore, mPFMs can offer a new basis for functional connectivity modelling and yield enhanced fMRI biomarkers for traits and diseases.

neuroscience↗

bioRxiv2024

Opaque Ontology: Neuroimaging Classification of ICD-10 Diagnostic Groups in the UK Biobank

BackgroundThe use of machine learning to classify diagnostic cases versus controls defined based on diagnostic ontologies such as the ICD-10 from neuroimaging features is now commonplace across a wide range of diagnostic fields. However, transdiagnostic comparisons of such classifications are lacking. Such transdiagnostic comparisons are important to establish the specificity of classification models, set benchmarks, and assess the value of diagnostic ontologies. ResultsWe investigated case-control classification accuracy in 17 different ICD-10 diagnostic groups from Chapter V (mental and behavioral disorders) and Chapter VI (diseases of the nervous system) using data from the UK Biobank. Classification models were trained using either neuroimaging (structural or functional brain MRI feature sets) or socio-demographic features. Random forest classification models were adopted using rigorous shuffle splits to estimate stability as well as accuracy of case-control classifications. Diagnostic classification accuracies were benchmarked against age classification (oldest versus youngest) from the same feature sets and against additional classifier types (K-nearest neighbors and linear support vector machine). In contrast to age classification accuracy, which was high for all feature sets, few ICD-10 diagnostic groups were classified significantly above chance (namely, demyelinating diseases based on structural neuroimaging features, and depression based on socio-demographic and functional neuroimaging features). ConclusionThese findings highlight challenges with the current disease classification system, leading us to recommend caution with the use of ICD-10 diagnostic groups as target labels in brain-based disease prediction studies.

neuroscience↗

bioRxiv2024

Higher amplitudes of visual networks are associated with trait but not state- depression.

Despite depression being a leading cause of global disability, neuroimaging studies have struggled to identify replicable neural correlates of depression or explain limited variance. This challenge may, in part, stem from the intertwined state (current symptoms; variable) and trait (general propensity; stable) experiences of depression. Here, we sought to disentangle state from trait experiences of depression by leveraging a longitudinal cohort and stratifying individuals into four groups: those in remission ( trait depression group), those with large longitudinal severity changes in depression symptomatology ( state depression group), and their respective matched control groups (total analytic n=1,030). We hypothesized that spatial network organization would be linked to trait depression due to its temporal stability, whereas functional connectivity between networks would be more sensitive to state-dependent depression symptoms due to its capacity to fluctuate. We identified 15 large-scale probabilistic functional networks from resting-state fMRI data and performed group comparisons on the amplitude, connectivity, and spatial overlap between these networks, using matched control participants as reference. Our findings revealed higher amplitude in visual networks for the trait depression group at the time of remission, in contrast to controls. This observation may suggest altered visual processing in individuals predisposed to developing depression over time. No significant group differences were observed in any other network measures for the trait-control comparison, nor in any measures for the state-control comparison. These results underscore the overlooked contribution of visual networks to the psychopathology of depression and provide evidence for distinct neural correlates between state and trait experiences of depression.

neuroscience↗

bioRxiv2023

The most prominent electrophysiological effect of ketamine is not sufficient to cause anesthesia

Electrophysiological recordings during ketamine anesthesia have revealed a slow alternating pattern of high- and low- frequency activity (a "gamma-burst" pattern) that develops with the onset of general anesthesia. We examine the role of NMDA receptor antagonism in generating the gamma-burst pattern and the link between gamma-bursts and dissociative anesthesia. We compare the effects of ketamine with those of the highly selective NMDA receptor antagonist CGS 19755 on multi-site intracranial electrophysiology and behavior in rhesus macaques. Remarkably, we find that animals given a moderate dose of CGS 19755 are able to perform a difficult memory task, while at the same time showing electrophysiological activity similar to ketamine anesthesia, with one key difference: a lack of delta-band LFP modulation. This difference demonstrates that ketamines ability to drive strong delta-band oscillations relies on additional mechanisms beyond NMDA receptor antagonism alone, and points to a key role for the activity underlying delta-band oscillations in causing anesthesia.

neuroscience↗

bioRxiv2023

Evaluating functional brain organization in individuals and identifying contributions to network overlap

Individual differences in the spatial organization of resting state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting state networks can be derived using high quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that network overlap is indicative of linear additive coupling. These results suggest that regions of network overlap concurrently process information from all contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

neuroscience↗

bioRxiv2023

No evidence for the association between COVID-19 and neuroinflammation: A diffusion basis spectrum imaging study.

COVID-19 remains a significant international public health concern. Yet, the mechanisms through which symptomatology emerges remain poorly understood. While SARS-CoV-2 infection may induce prolonged inflammation within the central nervous system, the evidence primarily stems from limited small-scale case investigations. To address this gap, our study capitalized on longitudinal UK Biobank neuroimaging data acquired prior to and following COVID-19 testing (N=416 including n=224 COVID-19 cases; Mage=58.6). Putative neuroinflammation was assessed in gray matter structures and white matter tracts using non-invasive Diffusion Basis Spectrum Imaging (DBSI), which estimates inflammation-related cellularity (DBSI-restricted fraction; DBSI-RF) and vasogenic edema (DBSI-hindered fraction; DBSI-HF).We hypothesized that COVID-19 case status would be associated with increases in DBSI markers after accounting for potential confound (age, sex, race, body mass index, smoking frequency, and data acquisition interval) and multiple testing. COVID-19 case status was not significantly associated with DBSI-RF (|{beta}|s<0.28, pFDR >0.05), but with greater DBSI-HF in left pre- and post-central gyri and right middle frontal gyrus ({beta}s>0.3, all pFDR=0.03). Intriguingly, the brain areas exhibiting increased putative vasogenic edema had previously been linked to COVID-19-related functional and structural alterations, whereas brain regions displaying subtle differences in cellularity between COVID-19 cases and controls included regions within or functionally connected to the olfactory network, which has been implicated in COVID-19 psychopathology. Nevertheless, our study might not have captured acute and transitory neuroinflammatory effects linked to SARS-CoV-2 infection, possibly due to symptom resolution before the imaging scan. Future research is warranted to explore the potential time- and symptom-dependent neuroinflammatory relationship with COVID-19.

neuroscience↗

bioRxiv2022

Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average "hard" parcellations (Schaefer et al., 2018), individual-specific "hard" parcellations (Kong et al., 2021a), and an individual-specific "soft" parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average "hard" parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.

neuroscience↗

bioRxiv2022

Population modeling with machine learning can enhance measures of mental health - Open-Data Replication

Efforts to predict trait phenotypes based on functional MRI data from large cohorts have been hampered by low prediction accuracy and/or small effect sizes. Although these findings are highly replicable, the small effect sizes are somewhat surprising given the presumed brain basis of phenotypic traits such as neuroticism and fluid intelligence. We aim to replicate previous work and additionally test multiple data manipulations that may improve prediction accuracy by addressing data pollution challenges. Specifically, we added additional fMRI features, averaged the target phenotype across multiple measurements to obtain more accurate estimates of the underlying trait, balanced the target phenotypes distribution through undersampling of majority scores, and identified data-driven subtypes to investigate the impact of between-participant heterogeneity. Our results replicated prior results from Dadi et a (2021) in a larger sample. Each data manipulation further led to small but consistent improvements in prediction accuracy, which were largely additive when combining multiple data manipulations. Combining data manipulations (i.e., extended fMRI features, averaged target phenotype, balanced target phenotype distribution) led to a three-fold increase in prediction accuracy for fluid intelligence compared to prior work. These findings highlight the benefit of several relatively easy and low-cost data manipulations, which may positively impact future work.

neuroscience↗

bioRxiv2021

Mental health explains individual deviations from normative range in cognition-associated brain states

Links between cognitive deficits and psychiatric disorders have been studied predominantly at the group level, leaving unique individual characteristics largely elusive. Here, we applied normative modeling to UK Biobank data (N=18,634) and estimated the individual-level interplay of large-scale brain networks over time (i.e., dynamic brain state) as a function of general cognitive ability. Abnormality in such brain states was linked to individual variation in mental health. Specifically, brain state measures including fractional occupancy that indicates the brain state probability over time were estimated using a Hidden Markov Model, followed by a Gaussian process regression to estimate the normative range of these brain state measures from general cognitive ability. Abnormality scores per participant were quantified to represent the degree of deviations relative to the estimated population norm. We found significant associations between the abnormality scores of several brain states and individuals overall mental health. Our findings suggest potential impact of mental health on dynamic brain states that subserve cognitive functions and shed light on the relevant brain mechanisms underlying cognitive deficits in mental illness.

neuroscience↗

bioRxiv2021

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the models utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data. HighlightsO_LIWe introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data C_LIO_LIsPROFUMO hierarchically estimates fMRI networks for the population and every individual C_LIO_LIWe characterised high dimensional resting state fMRI networks from UK Biobank C_LIO_LIModel outperforms ICA and dual regression for estimation of individual-specific network topography C_LIO_LIWe demonstrate the models utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity C_LI

neuroscience↗

bioRxiv2019

Modelling Subject Variability in the Spatial and Temporal Characteristics of Functional Modes

Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) [Harrison et al. 2015] to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. Using simulated data, resting-state data from 1,000 subjects collected as part of the Human Connectome Project [Van Essen et al. 2013], and an analysis of 14 subjects in a variety of continuous task-states [Kieliba et al. 2019], we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.

neuroscience↗

bioRxiv2019

The relationship between spatial configuration and functional connectivity of brain regions revisited

In our previous paper (Bijsterbosch et al., 2018), we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.\n\nImpact statementWe show that functional connectivity network matrices as estimated from resting state functional MRI are biased by spatially overlapping network structure.

neuroscience↗