scholarly journals The architecture of functional lateralisation and its relationship to callosal connectivity in the human brain

2018 ◽  
Author(s):  
VR Karolis ◽  
M Corbetta ◽  
M Thiebaut de Schotten
Keyword(s):  
Human Brain ◽  
Fundamental Principle ◽  
Dimensional Structure ◽  
Opposite Hemisphere ◽  
Symbolic Communication ◽  
Brain Functions ◽  
Evoked Activity ◽  
Cortical Regions ◽  
Low Dimensional ◽  
The Brain

AbstractFunctional lateralisation is a fundamental principle of the human brain. However, a comprehensive taxonomy of functional lateralisation and its organisation in the brain is missing. We report the first complete map of functional hemispheric asymmetries in the human brain, reveal its low dimensional structure, and its relationship with structural inter-hemispheric connectivity. Our results suggest that the lateralisation of brain functions is distributed along four functional axes: symbolic communication, perception/action, emotion, and decision-making, and that cortical regions showing asymmetries in task-evoked activity have reduced connections with the opposite hemisphere.

On the Limits of Time in the Brain

KronoScope ◽  
2013 ◽  
Vol 13 (2) ◽  
pp. 228-239
Author(s):  
Rémy Lestienne
Keyword(s):  
Human Brain ◽  
Brain Functions ◽  
Inner Sense ◽  
The Past ◽  
Intellectual Abilities ◽  
The Future ◽  
Flow Of Time ◽  
Nested Hierarchy ◽  
Future Outcomes ◽  
The Brain

Abstract J.T. Fraser used to emphasize the uniqueness of the human brain in its capacity for apprehending the various dimensions of “nootemporality” (Fraser 1982 and 1987). Indeed, our brain allows us to sense the flow of time, to measure delays, to remember past events or to predict future outcomes. In these achievements, the human brain reveals itself far superior to its animal counterpart. Women and men are the only beings, I believe, who are able to think about what they will do the next day. This is because such a thought implies three intellectual abilities that are proper to mankind: the capacity to take their own thoughts as objects of their thinking, the ability of mental time travels—to the past thanks to their episodic memory or to the future—and the possibility to project very far into the future, as a consequence of their enlarged and complexified forebrain. But there are severe limits to our timing abilities of which we are often unaware. Our sensibility to the passing time, like other of our intellectual abilities, is often competing with other brain functions, because they use at least in part the same neural networks. This is particularly the case regarding attention. The deeper the level of attention required, the looser is our perception of the flow of time. When we pay attention to something, when we fix our attention, then our inner sense of the flux of time freezes. This limitation should not sound too unfamiliar to the reader of J.T. Fraser who wrote in his book Time, Conflict, and Human Values (1999) about “time as a nested hierarchy of unresolvable conflicts.”

scholarly journals Evolutionary modifications in human brain connectivity associated with schizophrenia

Brain ◽  
2019 ◽  
Vol 142 (12) ◽  
pp. 3991-4002 ◽  
Author(s):  
Martijn P van den Heuvel ◽  
Lianne H Scholtens ◽  
Siemon C de Lange ◽  
Rory Pijnenburg ◽  
Wiepke Cahn ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Connectivity ◽  
Brain Evolution ◽  
Higher Order ◽  
Brain Functions ◽  
Human Brain Evolution ◽  
The Brain

See Vértes and Seidlitz (doi:10.1093/brain/awz353) for a scientific commentary on this article. Is schizophrenia a by-product of human brain evolution? By comparing the human and chimpanzee connectomes, van den Heuvel et al. demonstrate that connections unique to the human brain show greater involvement in schizophrenia pathology. Modifications in service of higher-order brain functions may have rendered the brain more vulnerable to dysfunction.

scholarly journals Distributed context-dependent choice information in mouse dorsal-parietal cortex

2021 ◽  
Author(s):  
Javier Orlandi ◽  
Mohammad Adbolrahmani ◽  
Ryo Aoki ◽  
Dmitry Lyamzin ◽  
Andrea Benucci
Keyword(s):  
Decision Making ◽  
Decision Variable ◽  
Visual Stream ◽  
Posterior Cortex ◽  
Neural Data ◽  
Context Dependent ◽  
Cortical Regions ◽  
Low Dimensional ◽  
Independent Decision ◽  
The Brain

Abstract Choice information appears in the brain as distributed signals with top-down and bottom-up components that together support decision-making computations. In sensory and associative cortical regions, the presence of choice signals, their strength, and area specificity are known to be elusive and changeable, limiting a cohesive understanding of their computational significance. In this study, examining the mesoscale activity in mouse posterior cortex during a complex visual discrimination task, we found that broadly distributed choice signals defined a decision variable in a low-dimensional embedding space of multi-area activations, particularly along the ventral visual stream. The subspace they defined was near-orthogonal to concurrently represented sensory and motor-related activations, and it was modulated by task difficulty and contextually by the animals’ attention state. To mechanistically relate choice representations to decision-making computations, we trained recurrent neural networks with the animals’ choices and found an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. In conclusion, our results demonstrated an independent decision variable broadly represented in the posterior cortex, controlled by task features and cognitive demands. Its dynamics reflected decision computations, possibly linked to context-dependent feedback signals used for probabilistic-inference computations in variable animal-environment interactions.

scholarly journals STAB: a spatio-temporal cell atlas of the human brain

2020 ◽  
Vol 49 (D1) ◽  
pp. D1029-D1037
Author(s):  
Liting Song ◽  
Shaojun Pan ◽  
Zichao Zhang ◽  
Longhao Jia ◽  
Wei-Hua Chen ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Brain Regions ◽  
Molecular Characteristics ◽  
Great Effort ◽  
Brain Functions ◽  
Spatio Temporal ◽  
The Brain

Abstract The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

scholarly journals Facephenes and rainbows: Causal evidence for functional and anatomical specificity of face and color processing in the human brain

2017 ◽  
Vol 114 (46) ◽  
pp. 12285-12290 ◽  
Author(s):  
Gerwin Schalk ◽  
Christoph Kapeller ◽  
Christoph Guger ◽  
Hiroshi Ogawa ◽  
Satoru Hiroshima ◽  
...  
Keyword(s):  
Human Brain ◽  
Mental Process ◽  
Color Processing ◽  
Face Area ◽  
Neurosurgical Patient ◽  
Patient Reported ◽  
Stimulus Classes ◽  
Cortical Regions ◽  
The Brain

Neuroscientists have long debated whether some regions of the human brain are exclusively engaged in a single specific mental process. Consistent with this view, fMRI has revealed cortical regions that respond selectively to certain stimulus classes such as faces. However, results from multivoxel pattern analyses (MVPA) challenge this view by demonstrating that category-selective regions often contain information about “nonpreferred” stimulus dimensions. But is this nonpreferred information causally relevant to behavior? Here we report a rare opportunity to test this question in a neurosurgical patient implanted for clinical reasons with strips of electrodes along his fusiform gyri. Broadband gamma electrocorticographic responses in multiple adjacent electrodes showed strong selectivity for faces in a region corresponding to the fusiform face area (FFA), and preferential responses to color in a nearby site, replicating earlier reports. To test the causal role of these regions in the perception of nonpreferred dimensions, we then electrically stimulated individual sites while the patient viewed various objects. When stimulated in the FFA, the patient reported seeing an illusory face (or “facephene”), independent of the object viewed. Similarly, stimulation of color-preferring sites produced illusory “rainbows.” Crucially, the patient reported no change in the object viewed, apart from the facephenes and rainbows apparently superimposed on them. The functional and anatomical specificity of these effects indicate that some cortical regions are exclusively causally engaged in a single specific mental process, and prompt caution about the widespread assumption that any information scientists can decode from the brain is causally relevant to behavior.

scholarly journals Primacy model and the evolution of the olfactory receptor repertoire

2018 ◽  
Author(s):  
Hamza Giaffar ◽  
Dmitry Rinberg ◽  
Alexei A. Koulakov
Keyword(s):  
Olfactory Receptor ◽  
Olfactory Receptors ◽  
Dimensional Structure ◽  
Sufficient Information ◽  
High Affinity ◽  
Receptor Repertoire ◽  
Wide Range ◽  
Evoked Activity ◽  
Low Dimensional ◽  
The Impact

For many animals, the neural activity in early olfactory circuits during a single sniff cycle contains sufficient information for fine odor discrimination. Whilst much is known about the transformations of neural representations in early olfactory circuits, exactly how odorant evoked activity in the main olfactory bulb shapes the perception of odors remains largely unknown. In olfaction, odorant identity is generally conserved over a wide range of conditions, including concentration. We present a theory of identity assignment in the olfactory system that accounts for this invariance with respect to stimulus intensity. We suggest that the identities of relatively few high affinity olfactory receptor types determine an odorant's perceived identity. This set of high-affinity receptors is defined as the primary set and the coding model based on their responses is called the primacy theory. In this study, we explore the impact that primacy coding may have on the evolution of the ensemble of olfactory receptors. A primacy coding mechanism predicts the arrangement of different receptor types in a low-dimensional structure that we call a primacy hull. We present several statistical analyses that can detect the presence of this structure, allowing the predictions of the primacy model to be tested experimentally.

scholarly journals Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain

2020 ◽  
Author(s):  
Sreejan Kumar ◽  
Cameron T. Ellis ◽  
Thomas O’Connell ◽  
Marvin M Chun ◽  
Nicholas B. Turk-Browne
Keyword(s):  
Human Brain ◽  
Language Processing ◽  
Brain Function ◽  
Computational Models ◽  
Brain Activity ◽  
Functional Space ◽  
Semantic Features ◽  
Brain Functions ◽  
Auditory Features ◽  
The Brain

AbstractThe extent to which brain functions are localized or distributed is a foundational question in neuroscience. In the human brain, common fMRI methods such as cluster correction, atlas parcellation, and anatomical searchlight are biased by design toward finding localized representations. Here we introduce the functional searchlight approach as an alternative to anatomical searchlight analysis, the most commonly used exploratory multivariate fMRI technique. Functional searchlight removes any anatomical bias by grouping voxels based only on functional similarity and ignoring anatomical proximity. We report evidence that visual and auditory features from deep neural networks and semantic features from a natural language processing model are more widely distributed across the brain than previously acknowledged. This approach provides a new way to evaluate and constrain computational models with brain activity and pushes our understanding of human brain function further along the spectrum from strict modularity toward distributed representation.

The Double Take of Expertise: Neural Expansion Is Associated With Outstanding Performance

2018 ◽  
Vol 27 (6) ◽  
pp. 462-469 ◽  
Author(s):  
Merim Bilalić
Keyword(s):  
Pattern Recognition ◽  
Human Brain ◽  
Brain Activation ◽  
Cognitive System ◽  
Brain Areas ◽  
Opposite Hemisphere ◽  
Computational Burden ◽  
Complex Skills ◽  
Neural Efficiency ◽  
The Brain

The performance of experts seems almost effortless. The neural-efficiency hypothesis takes this into account, suggesting that because of practice and automatization of procedures, experts require fewer brain resources. Here, I argue that the way the brain accommodates complex skills does indeed have to do with the nature of experts’ performance. However, instead of exhibiting less brain activation, experts’ performance actually engages more brain areas. Behind the seemingly effortless performance of experts lies a complex cognitive system that relies on knowledge about the domain of expertise. Unlike novices, who need to execute one process at a time, experts are able to recognize an object, retrieve its function, and connect it to another object simultaneously. The expert brain deals with this computational burden by engaging not only specific brain areas in one hemisphere but also the same (homologous) area in the opposite hemisphere. This phenomenon, which I call the double take of expertise, has been observed in a number of expertise domains. I describe it here in object- and pattern-recognition tasks in the domain of chess. I also discuss the importance of the study of expertise for our understanding of the human brain in general.

scholarly journals Distributed context-dependent choice information in mouse dorsal-parietal cortex

2021 ◽  
Author(s):  
Javier G. Orlandi ◽  
Mohammad Abdolrahmani ◽  
Ryo Aoki ◽  
Dmitry R. Lyamzin ◽  
Andrea Benucci
Keyword(s):  
Decision Making ◽  
Decision Variable ◽  
Visual Stream ◽  
Posterior Cortex ◽  
Neural Data ◽  
Context Dependent ◽  
Cortical Regions ◽  
Low Dimensional ◽  
Independent Decision ◽  
The Brain

Choice information appears in the brain as distributed signals with top-down and bottom-up components that together support decision-making computations. In sensory and associative cortical regions, the presence of choice signals, their strength, and area specificity are known to be elusive and changeable, limiting a cohesive understanding of their computational significance. In this study, examining the mesoscale activity in mouse posterior cortex during a complex visual discrimination task, we found that broadly distributed choice signals defined a decision variable in a low-dimensional embedding space of multi-area activations, particularly along the ventral visual stream. The subspace they defined was near-orthogonal to concurrently represented sensory and motor-related activations, and it was modulated by task difficulty and contextually by the animals’ attention state. To mechanistically relate choice representations to decision-making computations, we trained recurrent neural networks with the animals’ choices and found an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. In conclusion, our results demonstrated an independent decision variable broadly represented in the posterior cortex, controlled by task features and cognitive demands. Its dynamics reflected decision computations, possibly linked to context-dependent feedback signals used for probabilistic-inference computations in variable animal-environment interactions.

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