FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1775849907> ?p ?o ?g. }

• W1775849907 endingPage "157" @default.
• W1775849907 startingPage "154" @default.
• W1775849907 abstract "A basic tenet of biological psychiatry is that psychiatric disorders are driven by abnormalities in brain function, which in turn reflect abnormalities in the underlying brain circuits, i.e., in the wiring of the brain. These circuit abnormalities presumably reflect a complex interplay between genes and environment. Many psychiatric disorders have strong genetic underpinnings: common or rare variants of genes, individually or in combination, elevate the susceptibility to disorders such as autism 1, schizophrenia 2, and many others. Most psychiatric disorders are thought to be neurodevelopmental in nature, either because symptoms typically arise during childhood (e.g., autism) or because the interactions between genes and environment begin early, even if the onset of the disorder becomes evident only in adolescence or adulthood. To better understand, diagnose, and treat psychiatric disorders, it is crucial to obtain deeper insights into brain circuits in health and disease and in humans and animal models. Here, we focus on the relevance of human in vivo neuroimaging, particularly involving magnetic resonance imaging (MRI). We briefly address three major points. First, recent neuroimaging studies have already provided important insights about abnormalities related to brain structure, function, and connectivity in psychopathology. Second, recent advances in neuroimaging of healthy adults, including many driven by the Human Connectome Project, offer exciting prospects for accelerated progress in characterizing disease-related brain connectivity abnormalities. Third, methodological limitations of each neuroimaging method, some of which are inadequately appreciated, require critical assessments and careful interpretation of research findings, especially when placed in the context of the extraordinary complexity of brain circuits revealed by studies of laboratory animals. The human brain contains about 90 billion neurons and 150 trillion synapses. Physically, the dominant structure is the cerebral cortex, a highly convoluted sheet containing most of the synapses but only ∼20% of the neurons 3. The cortex is a mosaic containing hundreds of distinct areas (parcels), but accurate mapping of their location, function, and connectivity is an ongoing quest. Individual variability in cortical structure, function, and connectivity is likely to underlie much of what determines our unique personalities, including behavioral disorders. However, cortical interactions with a complex array of subcortical nuclei (∼8% of brain volume but only ∼1% of neuronal number) and with the cerebellum (∼10% of brain volume, ∼80% of the neurons) are extremely important as well 3, 4. Data from nonhuman primate studies suggest that there are ∼10,000 long-distance pathways between cortical and subcortical parcels ranging widely in their connection strength 5, 6. Deciphering even a modest fraction of this circuitry in the human brain is truly a daunting endeavor. Four major MRI modalities provide views of human brain structure, function, and connectivity. First, structural MRI uses volume-based analyses to estimate the distribution of gray matter regions, and surface-based analyses to assess cortical thickness and folding patterns. Second, task functional MRI (fMRI) identifies regions of increased or decreased fMRI blood oxygen level dependent (BOLD) signal that in turn reflects brain activity (synaptic currents and neuronal spiking) via a complex and still poorly understood mechanism of neurovascular coupling 7. Third, diffusion imaging (dMRI) and tractography enable characterization of “structural connectivity” using preferential diffusion of water molecules along the length of axons to estimate the dominant fiber orientation(s) in each white matter voxel, then inferring long-distance connectivity based on (deterministic or probabilistic) tractography algorithms. Tractography is conceptually the closest approach to inferring direct anatomical connectivity, but it has significant practical limitations owing to the prevalence of crossing fibers, branching fibers, and other methodological confounds that can give rise to false positives and false negatives 6. Fourth, resting-state fMRI (rfMRI) relies on correlated fluctuations in the BOLD signal to infer “functional connectivity”, that typically reflects brain regions sharing a history of coactivation. This may reflect anatomically direct connectivity, but coactivation may occur instead or in addition through common inputs or indirect connections 8. The spatial resolution of each MRI modality depends on the size of individual volume elements (“voxels”), which in turn reflects the signal to noise constraints of each approach. Voxel dimensions are typically 1 mm for structural MRI, ∼3 mm for fMRI, and ∼2 mm for dMRI, but methodological advances by the Human Connectome Project described below have substantially reduced voxel sizes for each modality. In the remainder of this paper, we focus mainly on advances revealed by analyses of structural and functional connectivity. The past decade has seen a burgeoning literature applying currently available methods for assessing functional and structural connectivity to our understanding of course, outcome, treatment response and heterogeneity in psychiatric disorders, as well as their developmental antecedents 9. In many ways these studies are still in their infancy, but here we describe a few examples that highlight the potential utility and power of these methods for helping us to understand the pathophysiology of a range of psychiatric disorders. Not surprisingly, one major focus has been to assess whether individuals with various forms of psychiatric disorders differ from individuals without psychiatric disorders in either structural and/or functional connectivity. For example, a recent meta-analysis of obsessive-compulsive disorder indicates that this illness is associated with altered structural connectivity between lateral prefrontal and parietal regions 10. Importantly, the field is now moving beyond basic comparisons to healthy individuals by using measures of functional and structural connectivity to elucidate the progression of brain changes across different stages or phases of illness 11, and between individuals who have putatively different psychiatric disorders 12, 13. This offers the prospect of critical insights about potentially dissociable etiological pathways. A growing number of investigators are examining the relationship between individual differences in specific symptom or cognitive domains and structural and functional brain connectivity, both within and across diagnostic categories 14, 15. This aligns well with the Research Domain Criteria (RDoC) initiative 16, 17, which focuses on identifying core brain-behavior systems that may be critical for understanding psychopathology. Another important thrust is to use connectivity measures to understand the predictors and mechanisms of treatment response for psychiatric disorders 18, 19, providing insights into both the types of individual difference characteristics and the types of treatments that may facilitate plasticity. Finally, an exciting new direction is to use structural and functional connectivity measures to elucidate risk factors for and the developmental antecedents of psychiatric disorders 20-22. Such information may provide novel avenues for early intervention or even prevention, as well as clues as to pathophysiology. The efforts summarized above are starting to provide valuable insights into circuit-based mechanisms of psychiatric disorders, but they represent only the tip of an “information iceberg” that can be better exposed using improved neuroimaging methods. Like many other arenas of neuroscience, neuroimaging has benefited from many recent improvements in data acquisition and analysis that hold promise for advancing our understanding of circuit level dysfunction in psychopathology. Here, we illustrate a few of the advances in human neuroimaging enabled by the Human Connectome Project 23. Among the many improvements in data acquisition, the two most significant are “multi-band” pulse sequences, which benefit both fMRI and dMRI data acquisition 24, 25, and customized scanners with increased maximum gradient strength, which benefits dMRI 24, 26. For fMRI, this translates to better resolution in space (2 mm vs. typical ∼3 mm voxels; 1.6 mm voxels at 7 Tesla), which enables accurate mapping of data to the cortical ribbon, and in time (0.7 s vs. typical ∼2 s for each frame, or image volume), which increases sensitivity to dynamic activity patterns and also helps filter out noise. For dMRI, the improvement is in spatial resolution (1.25 mm vs. typical ∼2 mm), which enables better identification of crossing fibers 27, plus the prospect of dealing better with gyral biases 6. Nonetheless, even with improved resolution, given the known densities of neurons and synapses, a fMRI voxel in the Human Connectome Project contains ∼250,000 neurons and ∼250,000,000 synapses, while a dMRI voxel contains hundreds of thousands of axons. Hence, the gulf between the micro- and macro-connectome domains remains enormous. Improvements in Human Connectome Project data analysis include concurrent use of appropriate geometric models for cerebral cortex (as a surface mesh) and subcortical structures (as voxels) 28, and improved intersubject alignment using functionally relevant features as well as folding patterns 29. This is critical, given the dramatic individual differences in the physical pattern of cortical convolutions and the variability in size and location of cortical areas relative to folds. The Human Connectome Project expects to complete data acquisition on 1200 healthy adult twins and nontwin siblings in 2015. Results from the ∼500 subjects released to date are already beginning to emerge. The Connectome DB database serves as a user-friendly workhorse platform (http://www.humanconnectome.org) that to date has enabled sharing of nearly a petabyte (1 million gigabytes) of data with the scientific community. For rfMRI, this includes extensively processed data such as parcellations based on functional connectivity and functional connectivity matrices for individuals as well as population averages. In 2014, the National Institutes of Health announced a Connectomes Related to Human Disease funding opportunity (http://grants.nih.gov/grants/guide/pa-files/PAR-14-281.html) for the study of brain disorders using advanced methods of data acquisition and analysis, with the resultant data to be shared via a Connectome Coordinating Facility that is an extension of Connectome DB. Hopefully, this promotion of common strategies of acquiring and sharing high-quality neuroimaging data will accelerate progress in characterizing circuit abnormalities. While there is reason to be optimistic about continued advances, it is also vital to be realistic about the limits that are attainable using current technologies. Ideally, we would like to see methods that enable diagnosis at the level of individual subjects. Further, it will be increasingly critical in future work to synergistically integrate analyses of both functional and structural connectivity, given their complementary strengths and information content. In addition, it will be important to appreciate relationships in connectivity across spatial scales, such as understanding how disruption of local circuits (i.e., excitation/inhibition imbalance) may influence both larger scale functional connectivity 29, and even potentially structural connectivity over time. The degree to which these aspirations will be feasible remains open to question, but we believe that the advances afforded by endeavors such as the Human Connectome Project and related projects are helping to provide the tools and data that are vital for accelerating progress. This work was funded by the Human Connectome Project (1U54MH091657-01) from the 16 NIH Institutes and Centers that Support the NIH Blueprint for Neuroscience Research, and by the McDonnell Center for Systems Neuroscience at Washington University." @default.
• W1775849907 created "2016-06-24" @default.
• W1775849907 creator A5033965347 @default.
• W1775849907 creator A5072742526 @default.
• W1775849907 date "2015-06-01" @default.
• W1775849907 modified "2023-09-25" @default.
• W1775849907 title "The human connectome in health and psychopathology" @default.
• W1775849907 cites W1590571936 @default.
• W1775849907 cites W1890448769 @default.
• W1775849907 cites W195440345 @default.
• W1775849907 cites W1977465442 @default.
• W1775849907 cites W1983208069 @default.
• W1775849907 cites W1987263024 @default.
• W1775849907 cites W1987323424 @default.
• W1775849907 cites W1991804233 @default.
• W1775849907 cites W1993053013 @default.
• W1775849907 cites W2003321630 @default.
• W1775849907 cites W2003837387 @default.
• W1775849907 cites W2016954751 @default.
• W1775849907 cites W2024729467 @default.
• W1775849907 cites W2067214598 @default.
• W1775849907 cites W2075750433 @default.
• W1775849907 cites W2079702662 @default.
• W1775849907 cites W2096672020 @default.
• W1775849907 cites W2101357408 @default.
• W1775849907 cites W2106922481 @default.
• W1775849907 cites W2112151839 @default.
• W1775849907 cites W2118866938 @default.
• W1775849907 cites W2135780113 @default.
• W1775849907 cites W2145642814 @default.
• W1775849907 cites W2147270524 @default.
• W1775849907 cites W2153847303 @default.
• W1775849907 cites W2157607979 @default.
• W1775849907 cites W2162115602 @default.
• W1775849907 cites W2165552226 @default.
• W1775849907 cites W2170422206 @default.
• W1775849907 cites W2581259096 @default.
• W1775849907 doi "https://doi.org/10.1002/wps.20228" @default.
• W1775849907 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4471963" @default.
• W1775849907 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26043324" @default.
• W1775849907 hasPublicationYear "2015" @default.
• W1775849907 type Work @default.
• W1775849907 sameAs 1775849907 @default.
• W1775849907 citedByCount "40" @default.
• W1775849907 countsByYear W17758499072015 @default.
• W1775849907 countsByYear W17758499072016 @default.
• W1775849907 countsByYear W17758499072017 @default.
• W1775849907 countsByYear W17758499072018 @default.
• W1775849907 countsByYear W17758499072019 @default.
• W1775849907 countsByYear W17758499072020 @default.
• W1775849907 countsByYear W17758499072021 @default.
• W1775849907 countsByYear W17758499072022 @default.
• W1775849907 countsByYear W17758499072023 @default.
• W1775849907 crossrefType "journal-article" @default.
• W1775849907 hasAuthorship W1775849907A5033965347 @default.
• W1775849907 hasAuthorship W1775849907A5072742526 @default.
• W1775849907 hasBestOaLocation W17758499072 @default.
• W1775849907 hasConcept C118552586 @default.
• W1775849907 hasConcept C123273963 @default.
• W1775849907 hasConcept C15744967 @default.
• W1775849907 hasConcept C169760540 @default.
• W1775849907 hasConcept C3018011982 @default.
• W1775849907 hasConcept C45715564 @default.
• W1775849907 hasConcept C71924100 @default.
• W1775849907 hasConceptScore W1775849907C118552586 @default.
• W1775849907 hasConceptScore W1775849907C123273963 @default.
• W1775849907 hasConceptScore W1775849907C15744967 @default.
• W1775849907 hasConceptScore W1775849907C169760540 @default.
• W1775849907 hasConceptScore W1775849907C3018011982 @default.
• W1775849907 hasConceptScore W1775849907C45715564 @default.
• W1775849907 hasConceptScore W1775849907C71924100 @default.
• W1775849907 hasIssue "2" @default.
• W1775849907 hasLocation W17758499071 @default.
• W1775849907 hasLocation W17758499072 @default.
• W1775849907 hasLocation W17758499073 @default.
• W1775849907 hasLocation W17758499074 @default.
• W1775849907 hasOpenAccess W1775849907 @default.
• W1775849907 hasPrimaryLocation W17758499071 @default.
• W1775849907 hasRelatedWork W1965789743 @default.
• W1775849907 hasRelatedWork W1969043257 @default.
• W1775849907 hasRelatedWork W2001498694 @default.
• W1775849907 hasRelatedWork W2008068480 @default.
• W1775849907 hasRelatedWork W2021447765 @default.
• W1775849907 hasRelatedWork W2106068418 @default.
• W1775849907 hasRelatedWork W2124188543 @default.
• W1775849907 hasRelatedWork W2142773772 @default.
• W1775849907 hasRelatedWork W2152835329 @default.
• W1775849907 hasRelatedWork W2261281525 @default.
• W1775849907 hasVolume "14" @default.
• W1775849907 isParatext "false" @default.
• W1775849907 isRetracted "false" @default.
• W1775849907 magId "1775849907" @default.
• W1775849907 workType "article" @default.