Neurological Soft Signs and Brain Network Abnormalities in Schizophrenia

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.

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Structural brain network fingerprints of focal dystonia

Therapeutic Advances in Neurological Disorders ◽

10.1177/1756286419880664 ◽

2019 ◽

Vol 12 ◽

pp. 175628641988066 ◽

Cited By ~ 1

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.

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The neural basis underlying the relation between the action identification level and delay discounting: The medial and orbital frontal cortex functional connectivity with the precuneus

International Journal of Psychophysiology ◽

10.1016/j.ijpsycho.2020.11.014 ◽

2021 ◽

Vol 159 ◽

pp. 74-82

Author(s):

Rong Zhang ◽

Zhiyi Chen ◽

Ting Xu ◽

Tingyong Feng

Keyword(s):

Functional Connectivity ◽

Frontal Cortex ◽

Delay Discounting ◽

Action Identification ◽

Orbital Frontal Cortex ◽

Neural Basis

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Bridging global and local topology in whole-brain networks using the network statistic jackknife

Network Neuroscience ◽

10.1162/netn_a_00109 ◽

2020 ◽

Vol 4 (1) ◽

pp. 70-88 ◽

Cited By ~ 1

Author(s):

Teague R. Henry ◽

Kelly A. Duffy ◽

Marc D. Rudolph ◽

Mary Beth Nebel ◽

Stewart H. Mostofsky ◽

...

Keyword(s):

Network Analysis ◽

Network Structure ◽

R Package ◽

Brain Network ◽

Global Network ◽

Local Network ◽

Network Organization ◽

Whole Brain ◽

Global Efficiency ◽

Global Topology

Whole-brain network analysis is commonly used to investigate the topology of the brain using a variety of neuroimaging modalities. This approach is notable for its applicability to a large number of domains, such as understanding how brain network organization relates to cognition and behavior and examining disrupted brain network organization in disease. A benefit to this approach is the ability to summarize overall brain network organization with a single metric (e.g., global efficiency). However, important local differences in network structure might exist without any corresponding observable differences in global topology, making a whole-brain analysis strategy unlikely to detect relevant local findings. Conversely, using local network metrics can identify local differences, but are not directly informative of differences in global topology. Here, we propose the network statistic (NS) jackknife framework, a simulated lesioning method that combines the utility of global network analysis strategies with the ability to detect relevant local differences in network structure. We evaluate the NS jackknife framework with a simulation study and an empirical example comparing global efficiency in children with attention-deficit/hyperactivity disorder (ADHD) and typically developing (TD) children. The NS jackknife framework has been implemented in a public, open-source R package, netjack, available at https://cran.r-project.org/package=netjack .

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Bridging Global and Local Topology in Whole-brain Networks using the Network Statistic Jackknife

10.31234/osf.io/qejmg ◽

2018 ◽

Author(s):

Teague R Henry ◽

Kelly A. Duffy ◽

Marc D. Rudolph ◽

Mary Beth Nebel ◽

Stewart H. Mostofsky ◽

...

Keyword(s):

Network Analysis ◽

Network Structure ◽

Developmental Trajectories ◽

R Package ◽

Brain Network ◽

Global Network ◽

Group Differences ◽

Network Organization ◽

Whole Brain ◽

Global Efficiency

Whole-brain network analysis is commonly used to investigate the topology of the brain in a variety of neuroimaging modalities. This approach is notable for its applicability to a large number of domains, such as understanding how brain network organization relates to cognition and behavior, examining disrupted brain network organization in disease, and assessing developmental trajectories across the lifespan. A benefit to this approach is the ability to summarize overall brain network organization with a single number (e.g., global efficiency). However, important local differences in network structure might exist without any corresponding observable differences in overall topology, making a whole-brain analysis strategy unlikely to detect relevant local findings. Here, we propose the network-based statistic (NBS) jackknife as a means of combining the utility of global network analysis strategies with the ability to detect relevant local differences in network structure. We describe the NBS jackknife framework, and demonstrate three specific testing scenarios in a series of examples. Finally, we provide an empirical example comparing global efficiency between children with ADHD and typically developing (TD) children. We demonstrate using functional connectivity data that there are no group differences in whole-brain global efficiency. Using the NBS jackknife, however, we identify group differences in global efficiency specific to the salience and subcortical subnetworks. The NBS jackknife framework has been implemented in a public, open source R package, netjack, available at https://cran.r-project.org/package=netjack.

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Neural Basis of Olfactory and Trigeminal Integration

10.1101/2020.06.24.168989 ◽

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.

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Cognitive and neural processes in non-clinical auditory hallucinations

The British Journal of Psychiatry ◽

10.1192/bjp.191.51.s76 ◽

2007 ◽

Vol 191 (S51) ◽

pp. s76-s81 ◽

Cited By ~ 63

Author(s):

Emma Barkus ◽

John Stirling ◽

Richard Hopkins ◽

Shane McKie ◽

Shôn Lewis

Keyword(s):

Temporal Cortex ◽

Auditory Hallucinations ◽

Clinical Samples ◽

False Alarms ◽

Functional Magnetic Resonance ◽

Neural Basis ◽

Cerebral Activation ◽

Middle Temporal ◽

Neural Processes ◽

Stimulus Degradation

BackgroundThe nosological status of auditory hallucinations in non-clinical samples is unclearAimsTo investigate the functional neural basis of non-clinical hallucinationsMethodAfter selection from 1206 people, 68 participants of high, medium and low hallucination proneness completed a task designed to elicit verbal hallucinatory phenomena under conditions of stimulus degradation. Eight subjects who reported hearing a voice when none was present repeated the task during functional imagingResultsDuring the signal detection task, the high hallucination-prone participants reported a voice to be present when it was not (false alarms) significantly more often than the average or low participants (P<0.03, d.f. =2). On functional magnetic resonance imaging, patterns of activation during these false alarms showed activation in the superior and middle temporal cortex (P<0.001)ConclusionsAuditory hallucinatory experiences reported in non-clinical samples appear to be mediated by similar patterns of cerebral activation as found during hallucinations in schizophrenia

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A survey of brain network analysis by electroencephalographic signals

Cognitive Neurodynamics ◽

10.1007/s11571-021-09689-8 ◽

2021 ◽

Author(s):

Cuihua Luo ◽

Fali Li ◽

Peiyang Li ◽

Chanlin Yi ◽

Chunbo Li ◽

...

Keyword(s):

Network Analysis ◽

Brain Network ◽

Electroencephalographic Signals

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Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

Scientific Reports ◽

10.1038/s41598-020-78899-7 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Rieke Fruengel ◽

Timo Bröhl ◽

Thorsten Rings ◽

Klaus Lehnertz

Keyword(s):

Large Scale ◽

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Theoretical Concept ◽

Global Network ◽

Time Resolved ◽

Functional Connections ◽

Node Centrality ◽

Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

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Neural basis of auditory expectation within temporal cortex

Neuropsychologia ◽

10.1016/j.neuropsychologia.2013.07.019 ◽

2013 ◽

Vol 51 (11) ◽

pp. 2245-2250 ◽

Cited By ~ 12

Author(s):

J.M. Nazimek ◽

M.D. Hunter ◽

R. Hoskin ◽

I. Wilkinson ◽

P.W. Woodruff

Keyword(s):

Temporal Cortex ◽

Neural Basis

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Structural brain network topology underpinning ADHD and response to methylphenidate treatment

Translational Psychiatry ◽

10.1038/s41398-021-01278-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kristi R. Griffiths ◽

Taylor A. Braund ◽

Michael R. Kohn ◽

Simon Clarke ◽

Leanne M. Williams ◽

...

Keyword(s):

Brain Network ◽

Global Network ◽

Support Vector ◽

Structural Topology ◽

Local Efficiency ◽

Structural Brain Network ◽

Structural Architecture ◽

The Right ◽

Tractography Data ◽

Treatment Prognosis

AbstractBehavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder.

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