scholarly journals Neural Basis of Olfactory and Trigeminal Integration

2020 ◽  
Author(s):  
Prasanna R. Karunanayaka ◽  
Jiaming Lu ◽  
Qing X. Yang ◽  
K. Sathian
Keyword(s):  
Temporal Cortex ◽  
Neural Circuitry ◽  
Brain Regions ◽  
Brain Network ◽  
Neural Basis ◽  
Localization Accuracy ◽  
Inferior Parietal Cortex ◽  
Superior Temporal Cortex ◽  
Connectivity Patterns ◽  
Primary Olfactory Cortex

ABSTRACTHumans naturally integrate signals from the olfactory and intranasal trigeminal systems. A tight interplay has been demonstrated between these two systems, and yet the underlying neural circuitry that mediate olfactory-trigeminal integration remains unclear. Using functional magnetic resonance imaging (fMRI), this study investigated the neural mechanisms underlying olfactory-trigeminal integration. Fifteen subjects with normal olfactory function performed a localization task with weak or strong air-puff stimuli, phenylethyl alcohol (PEA; rose odor), or a combination. Although the ability to localize PEA to either nostril was at chance, its presence significantly improved the localization accuracy of weak, but not strong, air-puffs, relative to the localization of air-puffs without concomitant PEA, when both stimuli were delivered concurrently to the same nostril. This enhancement in localization accuracy was directly correlated with the magnitude of multisensory activity in the primary olfactory cortex (POC). Changes in orbitofrontal cortex (OFC) multisensory activity alone could not predict task performance, but changes in OFC and POC connectivity could. Similar activity and connectivity patterns were observed in the superior temporal cortex (STC), inferior parietal cortex (IPC) and the cerebellum. Taken together, these results suggest that olfactory-trigeminal integration is occurring across multiple brain regions. These findings can be interpreted as an indication that the POC is part of a distributed brain network that mediate the integration between olfactory and trigeminal systems.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

scholarly journals Neurological Soft Signs and Brain Network Abnormalities in Schizophrenia

2019 ◽  
Vol 46 (3) ◽  
pp. 562-571 ◽  
Author(s):  
Li Kong ◽  
Christina J Herold ◽  
Eric F C Cheung ◽  
Raymond C K Chan ◽  
Johannes Schröder
Keyword(s):  
Network Analysis ◽  
Frontal Cortex ◽  
Temporal Cortex ◽  
Brain Network ◽  
Global Network ◽  
Orbital Frontal Cortex ◽  
Neural Basis ◽  
Neurological Soft Signs ◽  
Middle Temporal ◽  
Significant Difference

Abstract Neurological soft signs (NSS) are often found in patients with schizophrenia. A wealth of neuroimaging studies have reported that NSS are related to disturbed cortical-subcortical-cerebellar circuitry in schizophrenia. However, the association between NSS and brain network abnormalities in patients with schizophrenia remains unclear. In this study, the graph theoretical approach was used to analyze brain network characteristics based on structural magnetic resonance imaging (MRI) data. NSS were assessed using the Heidelberg scale. We found that there was no significant difference in global network properties between individuals with high and low levels of NSS. Regional network analysis showed that NSS were associated with betweenness centrality involving the inferior orbital frontal cortex, the middle temporal cortex, the hippocampus, the supramarginal cortex, the amygdala, and the cerebellum. Global network analysis also demonstrated that NSS were associated with the distribution of network hubs involving the superior medial frontal cortex, the superior and middle temporal cortices, the postcentral cortex, the amygdala, and the cerebellum. Our findings suggest that NSS are associated with alterations in topological attributes of brain networks corresponding to the cortical-subcortical-cerebellum circuit in patients with schizophrenia, which may provide a new perspective for elucidating the neural basis of NSS in schizophrenia.

scholarly journals Structural brain network fingerprints of focal dystonia

2019 ◽  
Vol 12 ◽  
pp. 175628641988066 ◽  
Author(s):  
Venkata C. Chirumamilla ◽  
Christian Dresel ◽  
Nabin Koirala ◽  
Gabriel Gonzalez-Escamilla ◽  
Günther Deuschl ◽  
...  
Keyword(s):  
Frontal Cortex ◽  
Sensorimotor Cortex ◽  
Temporal Cortex ◽  
Brain Regions ◽  
Brain Network ◽  
Focal Dystonia ◽  
Global Network ◽  
Grey Matter Volume ◽  
Structural Brain Network ◽  
Structural Brain

Background: Focal dystonias are severe and disabling movement disorders of a still unclear origin. The structural brain networks associated with focal dystonia have not been well characterized. Here, we investigated structural brain network fingerprints in patients with blepharospasm (BSP) compared with those with hemifacial spasm (HFS), and healthy controls (HC). The patients were also examined following treatment with botulinum neurotoxin (BoNT). Methods: This study included matched groups of 13 BSP patients, 13 HFS patients, and 13 HC. We measured patients using structural-magnetic resonance imaging (MRI) at baseline and after one month BoNT treatment, at time points of maximal and minimal clinical symptom representation, and HC at baseline. Group regional cross-correlation matrices calculated based on grey matter volume were included in graph-based network analysis. We used these to quantify global network measures of segregation and integration, and also looked at local connectivity properties of different brain regions. Results: The networks in patients with BSP were more segregated than in patients with HFS and HC ( p < 0.001). BSP patients had increased connectivity in frontal and temporal cortices, including sensorimotor cortex, and reduced connectivity in the cerebellum, relative to both HFS patients and HC ( p < 0.05). Compared with HC, HFS patients showed increased connectivity in temporal and parietal cortices and a decreased connectivity in the frontal cortex ( p < 0.05). In BSP patients, the connectivity of the frontal cortex diminished after BoNT treatment ( p < 0.05). In contrast, HFS patients showed increased connectivity in the temporal cortex and reduced connectivity in cerebellum after BoNT treatment ( p < 0.05). Conclusions: Our results show that BSP patients display alterations in both segregation and integration in the brain at the network level. The regional differences identified in the sensorimotor cortex and cerebellum of these patients may play a role in the pathophysiology of focal dystonia. Moreover, symptomatic reduction of hyperkinesia by BoNT treatment was associated with different brain network fingerprints in both BSP and HFS patients.

scholarly journals Awake fMRI Reveals Brain Regions for Novel Word Detection in Dogs

2017 ◽  
Author(s):  
Ashley Prichard ◽  
Peter F. Cook ◽  
Mark Spivak ◽  
Raveena Chhibber ◽  
Gregory S. Berns
Keyword(s):  
Caudate Nucleus ◽  
Pattern Analysis ◽  
Novelty Detection ◽  
Temporal Cortex ◽  
Brain Regions ◽  
Neural Basis ◽  
Multivoxel Pattern Analysis ◽  
Word Detection ◽  
Left Caudate ◽  
Basic Level

AbstractHow do dogs understand human words? At a basic level, understanding would require the discrimination of words from non-words. To determine the mechanisms of such a discrimination, we trained 12 dogs to retrieve two objects based on object names, then probed the neural basis for these auditory discriminations using awake-fMRI. We compared the neural response to these trained words relative to “oddball” pseudowords the dogs had not heard before. Consistent with novelty detection, we found greater activation for pseudowords relative to trained words bilaterally in the parietotemporal cortex. To probe the neural basis for representations of trained words, searchlight multivoxel pattern analysis (MVPA) revealed that a subset of dogs had clusters of informative voxels that discriminated between the two trained words. These clusters included the left temporal cortex and amygdala, left caudate nucleus, and thalamus. These results demonstrate that dogs’ processing of human words utilizes basic processes like novelty detection, and for some dogs, may also include auditory and hedonic representations.

scholarly journals Heterogeneity of EEG resting-state brain networks in absolute pitch

2020 ◽  
Author(s):  
Marielle Greber ◽  
Carina Klein ◽  
Simon Leipold ◽  
Silvano Sele ◽  
Lutz Jäncke
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Resting State ◽  
Large Scale ◽  
Absolute Pitch ◽  
Brain Regions ◽  
Brain Network ◽  
Higher Order ◽  
Neural Basis ◽  
Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

scholarly journals Two Ways to Build a Thought: Distinct Forms of Compositional Semantic Representation Across Brain Regions

2019 ◽  
Author(s):  
Steven M Frankland ◽  
Joshua D. Greene
Keyword(s):  
Semantic Representation ◽  
Temporal Cortex ◽  
Brain Regions ◽  
Human Mind ◽  
Separation Mechanism ◽  
Central Feature ◽  
Simple Sentence ◽  
Superior Temporal Cortex ◽  
Trial Basis ◽  
Broad Role

To understand a simple sentence such as “the woman chased the dog”, the human mind must dynamically organize the relevant concepts to represent who did what to whom. This structured re-combination of concepts (woman, dog, chased) enables the representation of novel events, and is thus a central feature of intelligence. Here, we use fMRI and encoding models to delineate the contributions of three brain regions to the representation of structured, relational combinations. We identify a region of rostral-medial prefrontal cortex (rmPFC) that shares representations of noun-verb conjunctions across sentences: for example, combining “woman” and “chased” to encode woman-as-chaser, distinct from woman-as-chasee. This PFC region differs from the left-mid superior temporal cortex (lmSTC) and hippocampus, two regions previously implicated in representing relations. lmSTC represents broad role combinations that are shared across verbs (e.g., woman-as-agent), rather than narrow roles, limited to specific actions (woman-as-chaser). By contrast, a hippocampal sub-region represents instances of recurring noun-verb conjunctions as dissimilar to one another, and is anti-correlated with rMPFC on a trial-by-trial basis, consistent with a pattern separation mechanism. These three regions appear to play distinct, but complementary, roles in encoding compositional event structure.

scholarly journals A common neural system mediating two different forms of social judgement

2009 ◽  
Vol 40 (7) ◽  
pp. 1183-1192 ◽  
Author(s):  
J. Hall ◽  
H. C. Whalley ◽  
J. W. McKirdy ◽  
R. Sprengelmeyer ◽  
I. M. Santos ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Temporal Cortex ◽  
Social Function ◽  
Brain Regions ◽  
Neural System ◽  
Neural Basis ◽  
Wide Range ◽  
Social Judgement ◽  
Dorsolateral Prefrontal ◽  
Social Tasks

BackgroundA wide range of neuropsychiatric conditions, including schizophrenia and autistic spectrum disorder (ASD), are associated with impairments in social function. Previous studies have shown that individuals with schizophrenia and ASD have deficits in making a wide range of social judgements from faces, including decisions related to threat (such as judgements of approachability) and decisions not related to physical threat (such as judgements of intelligence). We have investigated healthy control participants to see whether there is a common neural system activated during such social decisions, on the basis that deficits in this system may contribute to the impairments seen in these disorders.MethodWe investigated the neural basis of social decision making during judgements of approachability and intelligence from faces in 24 healthy participants using functional magnetic resonance imaging (fMRI). We used conjunction analysis to identify common brain regions activated during both tasks.ResultsActivation of the amygdala, medial prefrontal cortex, inferior prefrontal cortex and cerebellum was seen during performance of both social tasks, compared to simple gender judgements from the same stimuli. Task-specific activations were present in the dorsolateral prefrontal cortex in the intelligence task and in the inferior and middle temporal cortex in the approachability task.ConclusionsThe present study identified a common network of brain regions activated during the performance of two different forms of social judgement from faces. Dysfunction of this network is likely to contribute to the broad-ranging deficits in social function seen in psychiatric disorders such as schizophrenia and ASD.

scholarly journals Activation in Human MT/MST by Static Images with Implied Motion

2000 ◽  
Vol 12 (1) ◽  
pp. 48-55 ◽  
Author(s):  
Zoe Kourtzi ◽  
Nancy Kanwisher
Keyword(s):  
Visual Motion ◽  
Visual Analysis ◽  
Temporal Cortex ◽  
Brain Regions ◽  
Dynamic Information ◽  
Implied Motion ◽  
Medial Superior Temporal ◽  
Superior Temporal Cortex ◽  
Before And After ◽  
Static Images

A still photograph of an object in motion may convey dynamic information about the position of the object immediately before and after the photograph was taken (implied motion). Medial temporal/medial superior temporal cortex (MT/MST) is one of the main brain regions engaged in the perceptual analysis of visual motion. In two experiments we examined whether MT/MST is also involved in representing implied motion from static images. We found stronger functional magnetic resonance imaging (fMRI) activation within MT/MST during viewing of static photographs with implied motion compared to viewing of photographs without implied motion. These results suggest that brain regions involved in the visual analysis of motion are also engaged in processing implied dynamic information from static images.

scholarly journals Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Satoru Hiwa ◽  
Shogo Obuchi ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Working Memory ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Support Vector ◽  
Neural Basis ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Properties

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

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