Biology subjects

Bzdok, D.

Publications and source records attributed to Bzdok, D..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Trips and Neurotransmitters: Discovering Principled Patterns across 6,850 Hallucinogenic Experiences

Psychedelics are thought to alter states of consciousness by disrupting how the higher association cortex governs bottom-up sensory signals. Individual hallucinogenic drugs are usually studied in participants in controlled laboratory settings. Here, we have explored word usage in 6,850 free-form testimonials with 27 drugs through the prism of 40 neurotransmitter receptor subtypes, which were then mapped to 3D coordinates in the brain via their gene transcription levels from invasive tissue probes. Despite the variable subjective nature of hallucinogenic experiences, our pattern-learning approach delineated how drug-induced changes of conscious awareness (e.g., dissolving self-world boundaries or fractal distortion of visual perception) are linked to cortex-wide anatomical distributions of receptor density proxies. The dominant explanatory factor related ego-dissolution-like phenomena to a constellation of 5-HT2A, D2, KOR, and NMDA receptors, anchored especially in the brains deep hierarchy (epitomized by the associative higher-order cortex) and shallow hierarchy (epitomized by the visual cortex). Additional factors captured psychological phenomena in which emotions (5-HT2A and Imidazoline1) were in tension with auditory (SERT, 5-HT1A) or visual (5-HT2A) sensations. Each discovered receptor-experience factor spanned between a higher-level association pole and a sensory input pole, which may relate to the previously reported collapse of hierarchical order among large-scale networks. Simultaneously considering many psychoactive molecules and thousands of natural language descriptions of drug experiences our framework finds the underlying semantic structure and maps it directly to the brain. These advances could assist in unlocking their wide-ranging potential for medical treatment.

neuroscience↗

bioRxiv2021

Large-Scale Intrinsic Functional Brain Organization Emerges from Three Canonical Spatiotemporal Patterns

Resting-state functional MRI has yielded seemingly disparate insights into large-scale organization of the human brain. The brains large-scale organization can be divided into two broad categories - zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. Here we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern, and the functional connectome network structure are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses, and unifies phenomena in resting-state functional MRI previously thought distinct.

neuroscience↗

bioRxiv2021

The Cost of Untracked Diversity in Brain-Imaging Prediction

Brain-imaging research enjoys increasing adoption of supervised machine learning for singlesubject disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population stratification. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in two separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n=297) and the Healthy Brain Network (HBN, n=551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially to the default mode network. Our collective findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

neuroscience↗

bioRxiv2021

Functional architecture of the aging brain

The intrinsic functional organization of the brain changes into older adulthood. Age differences are observed at multiple spatial scales, from global reductions in modularity and segregation of distributed brain systems, to network-specific patterns of dedifferentiation. Whether dedifferentiation reflects an inevitable, global shift in brain function with age, circumscribed, experience dependent changes, or both, is uncertain. We employed a multi-method strategy to interrogate dedifferentiation at multiple spatial scales. Multi-echo (ME) resting-state fMRI was collected in younger (n=181) and older (n=120) healthy adults. Cortical parcellation sensitive to individual variation was implemented for precision functional mapping of each participant, while preserving group-level parcel and network labels. ME-fMRI processing and gradient mapping identified global and macroscale network differences. Multivariate functional connectivity methods tested for microscale, edge-level differences. Older adults had lower BOLD signal dimensionality, consistent with global network dedifferentiation. Gradients were largely age-invariant. Edge-level analyses revealed discrete, network-specific dedifferentiation patterns in older adults. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal control network regions showed greater connectivity; and the dorsal attention network was more integrated with heteromodal regions. These findings highlight the importance of multi-scale, multi-method approaches to characterize the architecture of functional brain aging.

neuroscience↗

bioRxiv2021

Non-linearity matters: a deep learning solution to the generalization of hidden brain patterns across population cohorts

Finding an interpretable and compact representation of complex neuroimage data can be extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. Hand-crafted representations, as well as linear transformations, may not accurately reflect the significant variability across individuals. Here, we applied a data-driven approach to learn interpretable and generalizable latent representations that link cognition with underlying brain systems; we applied a three-dimensional autoencoder to two large-scale datasets to find an interpretable latent representation of high dimensional task fMRI image data. This representation also accounts for demographic characteristics, achieved by solving a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics ( latent indices) to find a multivariate mapping to non-imaging measures. We trained our model with multi-task fMRI data derived from the Human Connectome Project (HCP) that provides whole-brain coverage across a range of cognitive tasks. Next, in a transfer learning setting, we tested the generalization of our latent space on UK Biobank data as an independent dataset. Our model showed high performance in terms of age and predictions and was capable of capturing complex behavioral characteristics and preserving the individualized variabilities using a highly interpretable latent representation.

neuroscience↗

bioRxiv2021

Interacting brains revisited: A cross-brain network neuroscience perspective

Elucidating the neural basis of social behavior is a long-standing challenge in neuroscience. Such endeavors are driven by attempts to extend the isolated perspective on the human brain by considering interacting persons brain activities, but a theoretical and computational framework for this purpose is still in its infancy. Here, we posit a comprehensive framework based on bipartite graphs for interbrain networks and address whether they provide meaningful insights into the neural underpinnings of social interactions. First, we show that the nodal density of such graphs exhibits nonrandom properties. While the current analyses mostly rely on global metrics, we encode the regions roles via matrix decomposition to obtain an interpretable network representation yielding both global and local insights. With Bayesian modeling, we reveal how synchrony patterns seeded in specific brain regions contribute to global effects. Beyond inferential inquiries, we demonstrate that graph representations can be used to predict individual social characteristics, outperforming functional connectivity estimators for this purpose. In the future, this may provide a means of characterizing individual variations in social behavior or identifying biomarkers for social interaction and disorders.

neuroscience↗

bioRxiv2020

Dissecting the midlife crisis: Disentangling social, personality and demographic determinants in social brain anatomy

In any stage of life, humans crave social connection with other people. In midlife, transitions in social networks can be related to new leadership roles at work or becoming a caregiver for aging parents. Previous neuroimaging studies have reported that during midlife, especially the medial prefrontal cortex (mPFC) undergoes structural remodeling changes. Social behavior, personality predisposition, and demographic profile all bear intimate relation with the mPFC according to separate literature streams. To integrate these three areas commonly studied in isolation, we explicitly modeled their unique links with brain structure using a fully probabilistic framework. We weighed against each other a rich collection of 40 traits with their interindividual variation in social brain morphology in ~10,000 middle-aged UK Biobank participants (40-69 years at recruitment). Across conducted analyses, household size and daily routine schedules showed several of the largest effects in explaining variation in social brain regions. We revealed male-biased effects in the dorsal mPFC and amygdala for job income, and a female-biased effect in the ventral mPFC for health satisfaction. Our population investigation offers a more complete perspective into how adults at the midlife milestone may navigate life depending on their identity and status.

neuroscience↗

bioRxiv2020

Functional idiosyncrasy has a shared topography with group-level connectivity alterations in autism

Autism spectrum disorder (ASD) is commonly understood as a network disorder, yet case-control analyses against typically-developing controls (TD) have yielded somewhat inconsistent patterns of results. The current work was centered on a novel approach to profile functional network idiosyncrasy, the inter-individual variability in the association between functional network organization and brain anatomy, and we tested the hypothesis that idiosyncrasy contributes to connectivity alterations in ASD. Studying functional network idiosyncrasy in a multi-centric dataset with 157 ASD and 172 TD, our approach revealed higher idiosyncrasy in ASD in the default mode, somatomotor and attention networks together with reduced idiosyncrasy in the lateral temporal lobe. Idiosyncrasy was found to increase with age in both ASD and TD, and was significantly correlated with symptom severity in the former group. Association analysis with structural and molecular brain features indicated that patterns of functional network idiosyncrasy were not correlated with ASD-related cortical thickness alterations, but closely with the spatial expression patterns of intracortical ASD risk genes. In line with our main hypothesis, we could demonstrate that idiosyncrasy indeed plays a strong role in the manifestation of connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between the heterogeneity of individuals with an autism diagnosis and group-level functional signatures, and help to consolidate prior research findings on the highly variable nature of the functional connectome in ASD. Our study promotes idiosyncrasy as a potential individualized diagnostic marker of atypical brain network development.

neuroscience↗

bioRxiv2020

Historical and cross-disciplinary trends in the biological and social sciences reveal an accelerating adoption of advanced analytics

Methods for data analysis in the biomedical, life and social sciences are developing at a rapid pace. At the same time, there is increasing concern that education in quantitative methods is failing to adequately prepare students for contemporary research. These trends have led to calls for educational reform to undergraduate and graduate quantitative research method curricula. We argue that such reform should be based on data-driven insights into within- and cross-disciplinary use of research methods. Our survey of peer-reviewed literature screened [~]3.5 million openly available research articles to monitor the cross-disciplinary usage of research methods in the past decade. We applied data-driven text-mining analyses to the methods and materials section of a large subset of this corpus to identify method trends shared across disciplines, as well as those unique to each discipline. As a whole, usage of T-test, analysis of variance, and other classical regression-based methods has declined in the published literature over the past 10 years. Machine-learning approaches, such as artificial neural networks, have seen a significant increase in the total share of scientific publications. We find unique groupings of research methods associated with each biomedical, life and social science discipline, such as the use of structural equation modeling in psychology, survival models in oncology, and manifold learning in ecology. We discuss the implications of these findings for education in statistics and research methods, as well as within- and cross-disciplinary collaboration.

scientific communication and education↗

bioRxiv2020

Neuroimaging: Into the Multiverse

AO_SCPLOWBSTRACTC_SCPLOWFor most neuroimaging questions the huge range of possible analytic choices leads to the possibility that conclusions from any single analytic approach may be misleading. Examples of possible choices include the motion regression approach used and smoothing and threshold factors applied during the processing pipeline. Although it is possible to perform a multiverse analysis that evaluates all possible analytic choices, this can be computationally challenging and repeated sequential analyses on the same data can compromise inferential and predictive power. Here, we establish how active learning on a low-dimensional space that captures the inter-relationships between analysis approaches can be used to efficiently approximate the whole multiverse of analyses. This approach balances the benefits of a multiverse analysis without the accompanying cost to statistical power, computational power and the integrity of inferences. We illustrate this approach with a functional MRI dataset of functional connectivity across adolescence, demonstrating how a multiverse of graph theoretic and simple pre-processing steps can be efficiently navigated using active learning. Our study shows how this approach can identify the subset of analysis techniques (i.e., pipelines) which are best able to predict participants ages, as well as allowing the performance of different approaches to be quantified.

neuroscience↗

bioRxiv2020

Population variability in social brain morphology:links to socioeconomic status and health disparity

Health disparity across layers of society involves reasons beyond the healthcare system. Socioeconomic status (SES) shapes peoples daily interaction with their social environment, and is known to impact various health outcomes. Using generative probabilistic modeling, we investigated health satisfaction and complementary indicators of socioeconomic lifestyle in the human social brain. In a population cohort of ~10,000 UK Biobank participants, our first analysis probed the relationship between health status and subjective social standing (i.e., financial satisfaction). We identified volume effects in participants unhappy with their health in regions of the higher associative cortex, especially the dorsomedial prefrontal cortex (dmPFC) and bilateral temporo-parietal junction (TPJ). Specifically, participants in poor subjective health showed deviations in dmPFC and TPJ volume as a function of financial satisfaction. The second analysis on health status and objective social standing (i.e., household income) revealed volume deviations in regions of the limbic system for individuals feeling unhealthy. In particular, low-SES participants dissatisfied with their health showed deviations in volume distributions in the amygdala and hippocampus bilaterally. Thus, our population-level evidence speaks to the possibility that health status and socioeconomic position have characteristic imprints in social brain differentiation.

neuroscience↗

bioRxiv2020

Beyond brain age: Empirically-derived proxy measures of mental health

BackgroundBiological aging is revealed by physical measures, e.g., DNA probes or brain scans. Instead, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? ResultsHere, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures when modeling from brain signals or sociodemographic data, capturing multiple health-related constructs. ConclusionsPopulation modeling with machine learning can derive measures of mental health from brain signals and questionnaire data, which may complement or even substitute for psychometric assessments in clinical populations. Key PointsO_LIWe applied machine learning on more than 10.000 individuals from the general population to define empirical approximations of health-related psychological measures that do not require human judgment. C_LIO_LIWe found that machine-learning enriched the given psychological measures via approximation from brain and sociodemographic data: Resulting proxy measures related as well or better to real-world health behavior than the original measures. C_LIO_LIModel comparisons showed that sociodemographic information contributed most to characterizing psychological traits beyond aging. C_LI

neuroscience↗

bioRxiv2020

Generative lesion pattern decomposition of cognitive impairment after stroke

Cognitive impairment is a frequent and disabling sequela of stroke. There is however incomplete understanding of how lesion topographies in the left and right cerebral hemisphere brain interact to cause distinct cognitive deficits. We integrated machine learning and Bayesian hierarchical modeling to enable hemisphere-aware analysis of 1080 subacute ischemic stroke patients with deep profiling [~]3 months after stroke. We show relevance of the left hemisphere in the prediction of language and memory assessments, while global cognitive impairments were equally well predicted by lesion topographies from both sides. Damage to the hippocampal and occipital regions on the left were particularly informative about lost naming and memory function. Global cognitive impairment was predominantly linked to lesioned tissue in supramarginal and angular gyrus, the postcentral gyrus as well as the lateral occipital and opercular cortices of the left hemisphere. Hence, our analysis strategy uncovered that lesion patterns with unique hemispheric distributions are characteristic of how cognitive capacity is lost due to ischemic brain tissue damage.

neuroscience↗

bioRxiv2020

Meta-matching: a simple framework to translate phenotypic predictive models from big to small data

There is significant interest in using brain imaging data to predict non-brain-imaging phenotypes in individual participants. However, most prediction studies are underpowered, relying on less than a few hundred participants, leading to low reliability and inflated prediction performance. Yet, small sample sizes are unavoidable when studying clinical populations or addressing focused neuroscience questions. Here, we propose a simple framework - "meta-matching" - to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in boutique studies. The key observation is that many large-scale datasets collect a wide range inter-correlated phenotypic measures. Therefore, a unique phenotype from a boutique study likely correlates with (but is not the same as) some phenotypes in some large-scale datasets. Meta-matching exploits these correlations to boost prediction in the boutique study. We applied meta-matching to the problem of predicting non-brain-imaging phenotypes using resting-state functional connectivity (RSFC). Using the UK Biobank (N = 36,848), we demonstrated that meta-matching can boost the prediction of new phenotypes in small independent datasets by 100% to 400% in many scenarios. When considering relative prediction performance, meta-matching significantly improved phenotypic prediction even in samples with 10 participants. When considering absolute prediction performance, meta-matching significantly improved phenotypic prediction when there were least 50 participants. With a growing number of large-scale population-level datasets collecting an increasing number of phenotypic measures, our results represent a lower bound on the potential of meta-matching to elevate small-scale boutique studies.

neuroscience↗

bioRxiv2020

Hidden population modes in social brain morphology: Its parts are more than its sum

The complexity of social interactions is a defining property of the human species. Many social neuroscience experiments have sought to map perspective taking, empathy, and other canonical psychological constructs to distinguishable brain circuits. This predominant research paradigm was seldom complemented by bottom-up studies of the unknown sources of variation that add up to measures of social brain structure; perhaps due to a lack of large population datasets. We aimed at a systematic de-construction of social brain morphology into its elementary building blocks in the UK Biobank cohort (n=~10,000). Coherent patterns of structural co-variation were explored within a recent atlas of social brain locations, enabled through translating autoencoder algorithms from deep learning. The artificial neural networks learned rich subnetwork representations that became apparent from social brain variation at population scale. The learned subnetworks carried essential information about the co-dependence configurations between social brain regions, with the nucleus accumbens, medial prefrontal cortex, and temporoparietal junction embedded at the core. Some of the uncovered subnetworks contributed to predicting examined social traits in general, while other subnetworks helped predict specific facets of social functioning, such as feelings of loneliness. Our population-level evidence indicates that hidden subsystems of the social brain underpin interindividual variation in dissociable aspects of social lifestyle.

neuroscience↗

bioRxiv2020

Molecular signatures of cognition and affect

Regulation of gene expression drives protein interactions that govern synaptic wiring and neuronal activity. The resulting coordinated activity among neuronal populations supports complex psychological processes, yet how gene expression shapes cognition and emotion remains unknown. Here we directly bridge the microscale and macroscale by mapping gene expression patterns to functional activation patterns across the cortical sheet. Applying unsupervised learning to the Allen Human Brain Atlas and Neurosynth databases, we identify a ventromedial-dorsolateral gradient of gene assemblies that separate affective and cognitive domains. This topographic molecular-psychological signature reflects the hierarchical organization of the neocortex, including systematic variations in cell type, myeloarchitecture, laminar differentiation, and intrinsic network affiliation. In addition, this molecular-psychological signature is related to individual differences in cognitive performance, strengthens over neurodevelopment, and can be replicated in two independent repositories. Collectively, our results reveal spatially covarying transcriptomic and cognitive architectures, highlighting the influence that molecular mechanisms exert on psychological processes.

neuroscience↗

bioRxiv2020

Hemispheric specialization within the inferior parietal lobe across cognitive domains

The inferior parietal lobe (IPL) is a key neural substrate underlying diverse mental processes, from basic attention to language and social cognition, that define human interactions. Its putative domain-global role appears to tie into poorly understood differences between cognitive domains in both hemispheres. Across attentional, semantic, and social cognitive tasks, our study explored functional specialization within the IPL. The task specificity of IPL subregion activity was substantiated by distinct predictive signatures identified by multivariate pattern-learning algorithms. Moreover, the left and right IPL exerted domain-specific modulation of effective connectivity among their subregions. Task-evoked functional interactions of the anterior and posterior IPL subregions involved recruitment of distributed cortical partners. While anterior IPL subregions were engaged in strongly lateralized coupling links, both posterior subregions showed more symmetric coupling patterns across hemispheres. Our collective results shed light on how under-appreciated functional specialization in the IPL supports some of the most distinctive human mental capacities.

neuroscience↗

bioRxiv2020

Shared and unique brain network features predict cognition, personality and mental health in childhood

The manner through which individual differences in brain network organization track population-level behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, the focus of most studies on single behavioral traits has come at the expense of capturing broader relationships across behaviors. Here, we utilized a large-scale dataset of 1858 typically developing children to estimate whole-brain functional network organization that is predictive of individual differences in cognition, impulsivity-related personality, and mental health during rest and task states. Predictive network features were distinct across the broad behavioral domains: cognition, personality and mental health. On the other hand, traits within each behavioral domain were predicted by highly similar network features. This is surprising given decades of research emphasizing that distinct brain networks support different mental processes. Although tasks are known to modulate the functional connectome, we found that predictive network features were similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood, yet are unique to different behavioral domains.

neuroscience↗