Longitudinal relationships among depressive symptoms, cortisol, and brain atrophy in the neocortex and the hippocampus DOI
A. Lebedeva, Anna Sundström, Lenita Lindgren

и другие.

Acta Psychiatrica Scandinavica, Год журнала: 2018, Номер 137(6), С. 491 - 502

Опубликована: Фев. 18, 2018

Objective Depression is associated with accelerated aging and age‐related diseases. However, mechanisms underlying this relationship remain unclear. The aim of study was to longitudinally assess the link between depressive symptoms, brain atrophy, cortisol levels. Method Participants from Betula prospective cohort (mean age = 59 years, SD 13.4 years) underwent clinical, neuropsychological 3T MRI assessments at baseline a 4‐year follow‐up. Cortisol levels were measured in four saliva samples. Cortical hippocampal atrophy rates estimated compared participants without symptoms ( n 81) correlated 49). Results Atrophy left superior frontal gyrus right lingual developed parallel temporal pole, cortex, supramarginal cortex after onset symptom. Depression‐related significantly elevated Elevated also widespread prefrontal, parietal, lateral, medial atrophy. Conclusion Depressive are prefrontal limbic areas brain.

Язык: Английский

Linking Structure and Function in Macroscale Brain Networks DOI Creative Commons
Laura E. Suárez, Ross D. Markello, Richard F. Betzel

и другие.

Trends in Cognitive Sciences, Год журнала: 2020, Номер 24(4), С. 302 - 315

Опубликована: Фев. 25, 2020

The emergence of network neuroscience allows researchers to quantify the link between organizational features neuronal networks and spectrum cortical functions.Current models indicate that structure function are significantly correlated, but correspondence is not perfect because reflects complex multisynaptic interactions in structural networks.Function cannot be directly estimated from structure, must inferred by higher-order interactions. Statistical, communication, biophysical have been used translate brain function.Structure–function coupling regionally heterogeneous follows molecular, cytoarchitectonic, functional hierarchies. Structure–function relationships a fundamental principle many naturally occurring systems. However, research suggests there an imperfect connectivity brain. Here, we synthesize current state knowledge linking macroscale discuss different types assess this relationship. We argue do include requisite biological detail completely predict function. Structural reconstructions enriched with local molecular cellular metadata, concert more nuanced representations functions properties, hold great potential for truly multiscale understanding structure–function relationship central concept natural sciences engineering. Consider how conformation protein determines its chemical properties and, ultimately, folding into 3D promotes among amino acids, allowing chemically interact other molecules endowing it Conversely, disruption protein's results loss Tellingly, said denatured, highlighting idea changing has fundamentally altered nervous system analogously shaped arrangement neurons populations. synaptic projections forms hierarchy (see Glossary) nested increasingly polyfunctional neural circuits support perception, cognition, action. Modern imaging technology permits high-throughput reconstruction across spatiotemporal scales species (Box 1). Through extensive international data sharing efforts, detailed system's connection patterns available humans multiple model organisms, including invertebrate [1.Chiang A-S. et al.Three-dimensional brain-wide wiring Drosophila at single-cell resolution.Curr. Biol. 2011; 21: 1-11Abstract Full Text PDF PubMed Scopus (324) Google Scholar], avian [2.Shanahan M. al.Large-scale organization forebrain: matrix theoretical analysis.Front. Comput. Neurosci. 2013; 7: 89Crossref (111) rodent [3.Oh S.W. al.A mesoscale connectome mouse brain.Nature. 2014; 508: 207Crossref (777) Scholar,4.Bota al.Architecture cerebral association underlying cognition.Proc. Natl. Acad. Sci. U.S.A. 2015; 112: E2093-E2101Crossref (90) Scholar, primate [5.Markov N.T. weighted directed interareal macaque cortex.Cereb. Cortex. 2012; 24: 17-36Crossref (272) Scholar,6.Majka P. al.Towards comprehensive atlas connections brain: Mapping tracer injection studies common marmoset reference digital template.J. Com. Neurol. 2016; 524: 2161-2181Crossref (33) Scholar. These diagrams system, termed (SC) or connectomes, represent physical elements [7.Sporns O. al.The human connectome: description brain.PLoS 2005; 1: e42Crossref (1521) Scholar]. offers opportunity articulate functions. SC possess distinctive nonrandom attributes, high clustering short path length, characteristic small-world architecture [8.Watts D.J. Strogatz S.H. Collective dynamics networks.Nature. 1998; 393: 440Crossref (0) Populations similar tend cluster together, forming specialized modules crosslinked hub nodes diverse connectional fingerprints [9.Young M.P. systems cortex.J. Roy. Soc. Lond. B. 1993; 252: 13-18Crossref Scholar,10.Kötter R. al.Connectional characteristics areas Walker's map prefrontal cortex.Neurocomputing. 2001; 38: 741-746Crossref (20) hubs disproportionately interconnected each other, putative core [11.Hagmann al.Mapping cortex.PLoS 2008; 6: e159Crossref (2384) Scholar] 'rich club' [12.van den Heuvel al.High-cost, high-capacity backbone global communication.Proc. 109: 11372-11377Crossref (361) architectural feature potentially signals sampled integrated [13.Zamora-López G. al.Cortical form module multisensory integration on top networks.Front. Neuroinform. 2010; 4: 1PubMed Finally, spatially embedded, finite metabolic material resources [14.Bullmore E. Sporns economy organization.Nat. Rev. 13: 336Crossref (1282) resulting increased prevalence shorter, low-cost [15.Horvát S. al.Spatial embedding cost constrain layout rodents primates.PLoS 14: e1002512Crossref Scholar,16.Roberts J.A. contribution geometry connectome.NeuroImage. 124: 379-393Crossref (60) attributes replicated range tracing techniques, suggesting principles phylogeny [17.Van al.Comparative connectomics.Trends. Cogn. 20: 345-361Abstract (118) imparts distinct signature coactivation patterns. Inter-regional promote signaling synchrony distant populations, giving rise coherent dynamics, measured as regional time series electromagnetic hemodynamic activity. Systematic pairs regions can (FC) networks. Over past decade, these recorded without task instruction stimulation; 'intrinsic' ' resting-state' FC thought reflect spontaneous activity [18.Biswal al.Functional motor cortex resting using echo-planar MRI.Magn. Reson. Med. 1995; 34: 537-541Crossref (5503) Intrinsic highly organized [19.Damoiseaux J. al.Consistent resting-state healthy subjects.Proc. 2006; 103: 13848-13853Crossref (2665) 20.Bellec al.Multi-level bootstrap analysis stable clusters fMRI.NeuroImage. 51: 1126-1139Crossref (170) 21.Thomas Yeo intrinsic connectivity.J. Neurophysiol. 106: 1125-1165Crossref (2040) reproducible [22.Gordon E.M. al.Precision mapping individual brains.Neuron. 2017; 95: 791-807Abstract (140) Scholar,23.Noble decade test-retest reliability connectivity: systematic review meta-analysis.NeuroImage. 2019; : 116157Crossref (5) comparable task-driven [24.Smith S.M. al.Correspondence brain's during activation rest.Proc. 2009; 13040-13045Crossref (2684) Scholar,25.Cole M.W. al.Intrinsic task-evoked architectures brain.Neuron. 83: 238-251Abstract (516) persistent nature rest makes ideal starting point study [26.Honey C.J. al.Can brain?.NeuroImage. 52: 766-776Crossref (291) Scholar,27.Damoiseaux J.S. Greicius M.D. Greater than sum parts: combining connectivity.Brain Struct. Funct. 213: 525-533Crossref Here first show direct one-to-one links limited inherently obscured networked survey modern quantitative methods move away correlations conceptualizing emerging focus strengths, limitations, commonalities. posit next steps network-level take account heterogeneity enriching microscale transcriptomic, neuromodulatory information. close theories uniform brain, vary parallel cytoarchitectonic representational Early emphasized weights. weights correlated [28.Honey C. al.Predicting connectivity.Proc. 2035-2040Crossref (1543) also Furthermore, structurally connected display greater unconnected Scholar,29.Shen K. al.Information processing functionally defined 32: 17465-17476Crossref (63) Scholar (Figure 1A). More globally, networks, particularly visual somatomotor circumscribed dense anatomical [29.Shen 30.Van Den al.Functionally linked brain.Hum. Brain Mapp. 30: 3127-3141Crossref (625) 31.Alves P.N. al.An improved neuroanatomical default-mode reconciles previous neuroimaging neuropathological findings.Commun. 2: 1-14Crossref While perfect. Even best-case estimates place correlation R2 ≈ 0.5 which means considerable variance (at least half) unexplained simple 1:1 structure. discrepancy widens case 1B ). A salient example homotopic corresponding structures two hemispheres. typically strongest subset [32.Mišić landscape One. 9: e111007Crossref (14) all supported callosal projection [33.Shen al.Stable long-range interhemispheric coordination projections.Proc. 6473-6478Crossref (52) strong may observed even individuals no [34.Uddin L.Q. al.Residual split-brain revealed fMRI.Neuroreport. 19: 703Crossref (96) 35.O'Reilly J.X. al.Causal effect disconnection lesions rhesus monkeys.Proc. 110: 13982-13987Crossref (106) 36.Layden E.A. al.Interhemispheric zebra finch absent corpus callosum normal ontogeny.NeuroImage. Crossref (1) examples illustrate sustained communication via indirect manifest FC. discordance pronounced mesoscopic scale. commonly meta-analytic recovered [37.Mišić al.Cooperative competitive spreading connectome.Neuron. 86: 1518-1529Abstract Scholar,38.Betzel R.F. al.Diversity meso-scale non-human connectomes.Nat. Commun. 2018; 346Crossref (21) 1C). reproducibly independent component community detection [39.Power J.D. 72: 665-678Abstract (1499) data-driven [20.Bellec Scholar,21.Thomas both recordings application diffusion-weighted covariance yields contiguous Scholar,40.Betzel modular networks: accounting wiring.Net. 42-68Crossref For example, fail identify default mode-like network, perhaps parts anatomically inter-connected differences. evidence assortative mixing, whereby (e.g., degrees) likely connected, whereas same true [50.Lim al.Discordant two-layer multiplex network.Sci. Rep. 2885Crossref (2) At scale, communities assortative, while disassortative [38.Betzel In words, affinity dissimilar attributes. As result, tuning algorithms sensitive improves match Altogether, rich body work demonstrates spans scales, edges their arrangement. Why FC? Functional arise connections, courses synapses removed other. propensity correlate driven only them, inputs they receive sensory organs entire [27.Damoiseaux Scholar,51.Bettinardi R.G. al.How sculpts function: unveiling structure.Chaos. 27: 047409Crossref (12) corollary much less distance-dependent connections. Anatomical subject material, spatial, constraints Scholar]; pressures reduced probability weight increasing spatial separation Although distance-dependence FC, weaker, ensuring differences configurations. section consider emergent property links. seen so far, exists nontrivial perfectly aligned. number emerged embody link, statistical [41.Mišić al.Network-level structure-function neocortex.Cereb. 26: 3285-3296Crossref (153) Scholar,42.Messé A. al.Relating relative contributions anatomy, stationary non-stationarities.PLoS. 10: e1003530Crossref Scholar,43.Graham D. Rockmore packet switching brain.J. 23: 267-276Crossref (18) 44.Goñi al.Resting-brain predicted analytic measures 111: 833-838Crossref (208) 45.Crofts J.J. Higham communicability measure applied networks.J. Interf. 411-414Crossref (61) [46.Honey al.Network shapes scales.Proc. 2007; 104: 10240-10245Crossref (941) 47.Breakspear Dynamic large-scale activity.Nat. 340Crossref (147) 48.Sanz-Leon al.Mathematical framework modeling Virtual Brain.NeuroImage. 385-430Crossref 49.Deco al.Key role coupling, delay, noise fluctuations.Proc. 10302-10307Crossref (372) Though implementation assumptions, emphasize collective, transcends geometric dependence dyadic relationships. briefly strategies, interpretation predictive utility, most importantly, what teach us about Perhaps simplest way statistically. Varying rank regression useful, canonical [52.Deligianni F. al.NODDI tensor-based microstructural indices predictors connectivity.PLoS 11: e0153404Crossref (13) partial squares objective simultaneously combinations maximally [53.McIntosh A.R. Mišić Multivariate analyses data.Annu. Psychol. 64: 499-525Crossref (73) 2). An appealing such modes. particular configuration subnetwork give Taking further, artificial learn recent variant word2vec algorithm build low-dimensional representation train deep edge-wise [54.Rosenthal relations embedded vector 2178Crossref (3) offer associate assuming specific mode interaction Communication science telecommunication engineering conceptualize superposition elementary events [43.Graham Scholar,55.Avena-Koenigsberger al.Communication networks.Nat. 17Crossref (92) By explicitly formulating inter-regional signaling, open important questions, namely: biologically realistic model, well does fit network? focused centralized shortest routing, discrete travel set source node prespecified target node. recently, attention shifted decentralized mechanisms where diffuse through [56.Mišić convergence zone hippocampus.PLoS. e1003982Crossref Scholar,57.Atasoy al.Human connectome-specific harmonic waves.Nat. 10340Crossref often broadcast fronts Scholar,58.Abdelnour diffusion accurately networks.NeuroImage. 90: 335-347Crossref (71) Scholar,59.Worrell J.C. al.Optimized sensory-motor integration.Net. 415-430Crossref Others considered neither fully nor decentralized, ensembles [45.Crofts Scholar,60.Avena-Koenigsberger al.Path tradeoff efficiency resilience connectome.Brain 222: 603-618Crossref (17) multiplexed strategies involving [44.Goñi Scholar,61.Avena-Koenigsberger routing networks.PLoS 15: e1006833Crossref 62.Betzel al.Structural, genetic factors interregional probed electrocorticography.Nat. Biomed. Eng. 63.Vazquez-Rodriguez al.Gradients tethering neocortex.Proc. 116: 21219-21227Crossref (6) consensus that, given topological proximity possible utilize either al.Re

Язык: Английский

Процитировано

677

Interpreting and Utilising Intersubject Variability in Brain Function DOI Creative Commons
Mohamed L. Seghier, Cathy J. Price

Trends in Cognitive Sciences, Год журнала: 2018, Номер 22(6), С. 517 - 530

Опубликована: Март 30, 2018

A wealth of scientifically and clinically relevant information is hidden, potentially invalidated, when data are averaged across subjects. There growing interest in using neuroimaging to explain differences human abilities disabilities. Progress this endeavour requires us treat intersubject variability as rather than noise. Our plastic noisy brains intrinsically change the parameterisation each individual's brain, providing a rich opportunity understand brain function. Normal can be used decode different neural pathways that sustain same task (degeneracy). This paramount importance for understanding why patients have variable outcomes after damage seemingly similar regions. We consider between-subject variance function describe natural output system (the brain) where subject embodies particular system. In context, becomes an to: (i) better characterise typical versus atypical functions; (ii) reveal cognitive strategies processing networks tasks; (iii) predict recovery capacity by taking into account both damaged spared pathways. has many ramifications individual learning preferences explaining wide Understanding boosts translational potential findings, clinical educational neuroscience. No two identical, with anatomy emerging from how genetically built shaped its intimate interaction environment. Most functional imaging studies discount establish subjects typically execute given task, assumption activations spatially temporally Methods measuring mainly focused on developing models normal (i.e., norms) allow abnormality quantified patient populations. quantification norms relies reductionist framework aims collapse dimension focus significant common overlapping) or mean effects (Box 1). Such over-reliance aggregate statistics (see Glossary) pursuit norm invalidate some conclusions drawn group level analyses.Box 1Variability along Sixth DimensionA experiment (e.g., fMRI) analysis multidimensional data. Each defined 6D dataset (Figure I): (i–iii) three space dimensions, (iv) time, (v) experimentally manipulated 'task', (vi) 'subject', repeated multiple Other extra dimensions may include (vii) 'session' longitudinal studies, (viii) 'group' healthy The here about sixth 'subject'.In treated 'nuisance' deliberately 'compressed' 'reduced' make inferences. approach hides other sources variability. Different facets manifest including intertrial between events items within run might linked changes strategy [105Coste C.P. et al.Ongoing activity fluctuations directly indirectly Stroop performance.Cereb. Cortex. 2011; 21: 2612-2629Crossref PubMed Scopus (0) Google Scholar]; inter-regional (or spatial variability) neurovascular coupling BOLD sensitivity [106Conner C.R. al.Variability relationship electrophysiology BOLD-fMRI cortical regions humans.J. Neurosci. 31: 12855-12865Crossref (84) intrasubject intersession) related reliability reproducibility fMRI test–retest [91Bennett C.M. Miller M.B. How reliable results magnetic resonance imaging?.Ann. N. Y. Acad. Sci. 2010; 1191Crossref (247) interindividual variability, also known intersubject, between-subject, across-subject (focus current review); intersite scanning environments, which sometimes concern large databases scans laboratories [107Brown G.G. al.Multisite data.Neuroimage. 54: 2163-2175Crossref (44) methodology: contextual situational factors, instance, experimental design, acquisition sequences, methods [108Carp J. secret lives experiments: reporting literature.Neuroimage. 2012; 63: 289-300Crossref (160) Scholar].We argue reflects behaviour under specific individual. Accordingly, more differ their parameterisation, will differ. main structural physiological parameters (measurable at mesoscopic macroscopic level) govern such individual-specific are: grey matter density [43Josse G. al.Predicting language lateralization gray matter.J. 2009; 29: 13516-13523Crossref thickness [53Saggar M. al.Estimating contribution group-based correlation networks.Neuroimage. 2015; 120: 274-284Crossref (9) morphological [109Juch H. al.Anatomical lateral frontal lobe surface: implication neuroimaging.Neuroimage. 2005; 24: 504-514Crossref Scholar, 110Cachia A. al.How shape reading accuracy.Brain Struct. Funct. 2018; 223: 701-712Crossref layers [111Deshpande al.Tissue specificity nonlinear dynamics baseline fMRI.Magn. Reson. Med. 2006; 55: 626-632Crossref white circuitry (tracts pathways) [1Miller al.Individual style similarities patterns individuals.Neuroimage. 59: 83-93Crossref (35) 47Golestani A.M. al.Constrained our connections: matter's key role visual working memory capacity.J. 2014; 34: 14913-14918Crossref myelination [44Hunt B.A. al.Relationships myeloarchitecture electrophysiological networks.Proc. Natl. U. S. 2016; 113: 13510-13515Crossref (10) callosal topography [112Josse al.Explaining anatomy: corpus callosum size.J. 2008; 28: 14132-14139Crossref Scholar] influences degree lateralisation subjects; connectivity [113Xu T. al.Assessing variations areal organization intrinsic brain: fingerprints reliability.Cereb. 26: 4192-4211Crossref (14) association task-related [114Wig G.S. al.Medial temporal rest predicts ability young adults.Proc. 105: 18555-18560Crossref (38) 115Stevens A.A. al.Functional network modularity captures inter- intra-individual variation capacity.PLoS One. 7e30468Crossref (97) 116Mennes al.Inter-individual resting-state task-induced activity.Neuroimage. 50: 1690-1701Crossref (217) divergence [45Park H.J. Friston K. Structural networks: connections cognition.Science. 2013; 342: 1238411Crossref (545) oscillations rhythms [13Basile L.F. topographic inherent physiology but noise.PLoS 10e0128343Crossref (1) 117Başar E. al.Gamma, alpha, delta, theta processes.Int. Psychophysiol. 2001; 39: 241-248Crossref (577) 118Haegens al.Inter- alpha peak frequency.Neuroimage. 92CGoogle Scholar], metabolism [119Wang G.J. al.Intersubject glucose metabolic measurements males.J. Nucl. 1994; 35: 1457-1466PubMed vasculature [120van der Zwan al.A quantitative investigation major cerebral arterial territories.Stroke. 1993; 1951-1959Crossref neurotransmitters hormones [121Shafir al.Postmenopausal hormone use impact emotion circuitry.Behav. Brain Res. 226: 147-153Crossref 122van den Brink R.L. al.Catecholaminergic neuromodulation shapes MRI brain.J. 36: 7865-7876Crossref Scholar]. 'subject'. search effect central tendency), ultimate representative subject, implicitly treats cannot explained any manipulation nuisance, noise, measurement error. ignores 2Seghier M.L. al.Inter-subject neuronal aloud familiar words.Neuroimage. 42: 1226-1236Crossref 3Iaria al.Cognitive dependent hippocampus caudate nucleus navigation: practice.J. 2003; 23: 5945-5952Crossref 2), subjective judgment [4Sanfratello L. al.Same strategies: influenced strategy.Hum. Mapp. 5127-5140Crossref (5) 5MacNamara al.Neural correlates fear learning.Behav. 287: 34-41Crossref [6Vogel E.K. Machizawa M.G. Neural capacity.Nature. 2004; 428 (784–751)Crossref (823) More critically, meaningful error, estimates not actually anyone well [7Heun R. al.Interindividual activation during encoding retrieval words.Eur. Psychiatry. 2000; 15: 470-479Crossref 8Miller al.Extensive associated episodic over time.J. Cogn. 2002; 14: 1200-1214Crossref (106) 9Ganis al.Understanding task-specific practice insights individual-differences analyses.Cogn. Affect. Behav. 5: 235-245Crossref (13) 10Seghier phonological semantic subjects.Hum. 140-155Crossref 11Vindras P. al.When one size does fit all: simple statistical method deal across-individual effects.PLoS 7e39059Crossref 12Stelzer al.Deficient approaches neuroimaging.Front. Hum. 8: 462Crossref (27) 13Basile there been appeals, field psychology, noise [14Underwood B.J. Individual crucible theory construction.Am. Psychol. 1975; 30: 128-134Crossref 15Kosslyn S.M. al.Bridging psychology biology. individuals groups.Am. 57: 341-351Crossref 16Thompson-Schill S.L. differences.Cogn. 115-116Crossref 17Vogel Awh exploit diversity scientific gain: constrain theory.Curr. Dir. 17: 171-176Crossref (86) 18Sauce B. Matzel L.D. causes behavior: matter.Front. 4: 395Crossref 19Wilmer J.B. isolate organization, biology, utility illustrative proposals stereopsis.Spat. Vis. 561-579Crossref (56) However, treating yet widely embraced community most software packages only provide effects. part because characterisation number observations individuals, challenge methodologies analysing, interpreting, [12Stelzer 20Dubois Adolphs Building science fMRI.Trends 20: 425-443Abstract Full Text PDF (107) Scholar].Box 2Intersubject Variability Cognitive StrategyIn multisubject tasks assumed performed way single [123Hedge C. al.The paradox: robust do produce differences.Behav. Methods. (Published online July 19, 2017)https://doi.org/10.3758/s13428-017-0935-1Crossref (80) unconstrained, allowing adopt own strategy. Figure I intuitively visualises problem averaging maps features [124Thirion al.Analysis cohort: methodological issues analyses.Neuroimage. 2007; 105-120Crossref (333) strategies. resulting average toy example depicts hybrid image differs encoded original images. contains false negatives (where vary images) positives feature combinations create new features).A hypothetical hold manipulate numbers performing serial addition successive integers. Practically, inside scanner, shown Arabic digit 8) his/her verbally say exact sum all 1 up presented (sum 8). If researchers limited knowledge ways strategies) executed, they assume exactly way. least illustrated II.Obviously, involves processes, distinct patterns. yields high weak sums contrast, paying attention pattern help exist, was using. For priori reading), clever manipulations push participant (implicitly explicitly) towards tasks, inferred looking structure data.Perhaps importantly, types intimately connected. example, slow (density volume), connectivity, underpin faster, dynamic endogenous rhythms, turn influence 125Tavor I. al.Task-free performance.Science. 352: 216-220Crossref (170) 126Barnes K.A. decision strategy.J. Neurophysiol. 112: 1838-1848Crossref 127Xie al.Whole-brain reflect modulation: multitask study.Neuroimage. 2017; May 23, 2017)https://doi.org/10.1016/j.neuroimage.2017.05.050Crossref Conversely, strategies, styles, expectation, decisions modulate underlying [27Sampaio-Baptista al.Motor skill induces microstructure myelination.J. 33: 19499-19503Crossref (165) 48Kohno links function, white-matter risky behavior.Neuroimage. 149: 15-22Crossref Scholar].Figure IICumulative Sums Integers via Three Possible Strategies.View Large Image ViewerDownload Hi-res Download (PPT) features). numb

Язык: Английский

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268

Consciousness-specific dynamic interactions of brain integration and functional diversity DOI Creative Commons
Andrea I. Luppi, Michael M Craig, Ioannis Pappas

и другие.

Nature Communications, Год журнала: 2019, Номер 10(1)

Опубликована: Окт. 10, 2019

Prominent theories of consciousness emphasise different aspects neurobiology, such as the integration and diversity information processing within brain. Here, we combine graph theory dynamic functional connectivity to compare resting-state MRI data from awake volunteers, propofol-anaesthetised patients with disorders consciousness, in order identify consciousness-specific patterns brain function. We demonstrate that cortical networks are especially affected by loss during temporal states high integration, exhibiting reduced compromised informational capacity, whereas thalamo-cortical disconnections emerge higher segregation. Spatially, posterior regions brain's default mode network exhibit reductions both rest unconsciousness. These results show human relies on spatio-temporal interactions between diversity, whose breakdown may represent a generalisable biomarker potential relevance for clinical practice.

Язык: Английский

Процитировано

223

LSD alters dynamic integration and segregation in the human brain DOI Creative Commons
Andrea I. Luppi,

Robin Carhart‐Harris,

Leor Roseman

и другие.

NeuroImage, Год журнала: 2020, Номер 227, С. 117653 - 117653

Опубликована: Дек. 17, 2020

Investigating changes in brain function induced by mind-altering substances such as LSD is a powerful method for interrogating and understanding how mind interfaces with brain, connecting novel psychological phenomena their neurobiological correlates. known to increase measures of complexity, potentially reflecting correlate the especially rich phenomenological content psychedelic-induced experiences. Yet although subjective stream consciousness constant ebb flow, no studies date have investigated influences dynamics functional connectivity human brain. Focusing on two fundamental network properties integration segregation, here we combined graph theory dynamic from resting-state MRI examine time-resolved effects networks Our main finding that experience are non-uniform time: makes globally segregated sub-states more complex, weakens relationship between anatomical connectivity. On regional level, reduces anterior medial prefrontal cortex, specifically during states high segregation. Time-specific were correlated different aspects experiences; particular, ego dissolution was predicted increased small-world organisation state global integration. These results reveal nuanced, temporally-specific picture altered complexity under psychedelics than has previously been reported.

Язык: Английский

Процитировано

150

Local structure-function relationships in human brain networks across the lifespan DOI Creative Commons
Farnaz Zamani Esfahlani, Joshua Faskowitz,

Jonah Slack

и другие.

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Апрель 19, 2022

Abstract A growing number of studies have used stylized network models communication to predict brain function from structure. Most focused on a small set applied globally. Here, we compare large at both global and regional levels. We find that globally most predictors perform poorly. At the level, performance improves but heterogeneously, in terms variance explained optimal model. Next, expose synergies among by using pairs jointly FC. Finally, assess age-related differences coupling across human lifespan. decreases magnitude structure-function with age. these are driven reduced sensorimotor regions, while higher-order cognitive systems preserve local Our results describe patterns cortex how this may change

Язык: Английский

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118

Distributed harmonic patterns of structure-function dependence orchestrate human consciousness DOI Creative Commons
Andrea I. Luppi, Jakub Vohryzek, Morten L. Kringelbach

и другие.

Communications Biology, Год журнала: 2023, Номер 6(1)

Опубликована: Янв. 28, 2023

Abstract A central question in neuroscience is how consciousness arises from the dynamic interplay of brain structure and function. Here we decompose functional MRI signals pathological pharmacologically-induced perturbations into distributed patterns structure-function dependence across scales: harmonic modes human structural connectome. We show that coupling a generalisable indicator under bi-directional neuromodulatory control. find increased scales during loss consciousness, whether due to anaesthesia or injury, capable discriminating between behaviourally indistinguishable sub-categories brain-injured patients, tracking presence covert consciousness. The opposite signature characterises altered state induced by LSD ketamine, reflecting psychedelic-induced decoupling function correlating with physiological subjective scores. Overall, connectome decomposition reveals neuromodulation network architecture jointly shape activation scales.

Язык: Английский

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69

Controversies and progress on standardization of large-scale brain network nomenclature DOI Creative Commons
Lucina Q. Uddin, Richard F. Betzel, Jessica R. Cohen

и другие.

Network Neuroscience, Год журнала: 2023, Номер 7(3), С. 864 - 905

Опубликована: Янв. 1, 2023

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level macroscale organization brain, beginning to confront challenges associated with developing a taxonomy its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy NETworks (WHATNET) was formed 2020 as an Organization Human Brain Mapping (OHBM)-endorsed best practices committee provide recommendations on points consensus, identify open questions, and highlight areas ongoing debate service moving field toward standardized reporting network neuroscience results. conducted survey catalog current large-scale brain nomenclature. A few well-known names (e.g., default mode network) dominated responses survey, number illuminating disagreement emerged. We summarize results initial considerations from workgroup. This perspective piece includes selective review this enterprise, including (1) scale, resolution, hierarchies; (2) interindividual variability networks; (3) dynamics nonstationarity (4) consideration affiliations subcortical structures; (5) multimodal information. close minimal guidelines cognitive communities adopt.

Язык: Английский

Процитировано

69

Applications of generative adversarial networks in neuroimaging and clinical neuroscience DOI Creative Commons
Rongguang Wang, Vishnu Bashyam, Zhijian Yang

и другие.

NeuroImage, Год журнала: 2023, Номер 269, С. 119898 - 119898

Опубликована: Янв. 24, 2023

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called generative methods, which generate new data with probabilistic model by sample distribution from real examples. In the clinical context, GANs shown enhanced capabilities capturing spatially complex, nonlinear, and potentially subtle disease effects compared traditional methods. This review appraises existing literature on applications imaging studies various neurological conditions, including Alzheimer's disease, brain tumors, aging, multiple sclerosis. We provide an intuitive explanation GAN methods for each application further discuss main challenges, open questions, promising future directions leveraging neuroimaging. aim bridge gap between advanced neurology research highlighting how can be leveraged support decision making contribute better understanding structural functional patterns diseases.

Язык: Английский

Процитировано

58

A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains DOI Creative Commons
Milou Straathof, Michel R.T. Sinke, Rick M. Dijkhuizen

и другие.

Journal of Cerebral Blood Flow & Metabolism, Год журнала: 2018, Номер 39(2), С. 189 - 209

Опубликована: Окт. 30, 2018

The mammalian brain is composed of densely connected and interacting regions, which form structural functional networks. An improved understanding the structure–function relation crucial to understand underpinnings function plasticity after injury. It currently unclear how connectivity strength relates strength. We obtained an overview recent papers that report on correspondences between quantitative measures in brain. included network studies was measured with resting-state fMRI, either diffusion-weighted MRI or neuronal tract tracers. Twenty-seven 28 showed a positive relationship. Large inter-study variations were found comparing diffusion-based (correlation coefficient (r) ranges: 0.18–0.82) tracer-based (r = 0.24–0.74). Two datasets demonstrated lower correlations 0.22 r 0.30) than 0.49 0.65). robust relationship supports hypothesis provides hardware from emerges. However, methodological differences complicate comparison across studies, emphasize need for validation standardization studies.

Язык: Английский

Процитировано

109

Connectome analysis of functional and structural hemispheric brain networks in major depressive disorder DOI Creative Commons
Xueyan Jiang,

Yuedi Shen,

Jiashu Yao

и другие.

Translational Psychiatry, Год журнала: 2019, Номер 9(1)

Опубликована: Апрель 12, 2019

Abstract Neuroimaging studies have shown topological disruptions of both functional and structural whole-brain networks in major depressive disorder (MDD). This study examined common specific alterations between these two types whether the were differentially involved hemispheres. Multimodal MRI data collected from 35 MDD patients healthy controls, whose hemispheric constructed, characterized, compared. We found that brain profoundly altered at multiple levels, while largely intact with MDD. Specifically, included decreases intra-hemispheric (left right) inter-hemispheric (heterotopic) connectivity; local, global normalized efficiency for networks; increases local left integration communication dorsolateral superior frontal gyrus, anterior cingulate gyrus hippocampus. Regarding asymmetry, similar patterns observed networks: right hemisphere was over-connected more efficient than globally; occipital partial regions exhibited leftward temporal sites showed rightward lateralization regard to regional connectivity profiles locally. Finally, functional–structural coupling connections significantly decreased correlated disease severity patients. Overall, this demonstrates modality- hemisphere-dependent invariant network MDD, which are helpful understanding elaborate characteristic integrative dysfunction disease.

Язык: Английский

Процитировано

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