scholarly journals Disarrangement and reorganization of the hippocampal functional connectivity during the spatial path adjustment of pigeons

2021 ◽  
Author(s):  
Mengmeng Li
Keyword(s):  
Functional Connectivity ◽  
Total Duration ◽  
Cognitive Learning ◽  
Network Connectivity ◽  
Columba Livia ◽  
Neural Representation ◽  
Neural Activities ◽  
Changing Patterns ◽  
Path Lengths ◽  
Avian Hippocampus

Abstract Background: The hippocampus plays an important role to support path planning and adjustment in goal-directed spatial navigation. While we still only have limited knowledge about how do the hippocampal neural activities, especially the functional connectivity patterns, change during the spatial path adjustment. In this study, we measured the behavioural indicators and local field potentials of the pigeon (Columba livia, male and female) during a goal-directed navigational task with the detour paradigm, exploring the changing patterns of the hippocampal functional network connectivity of the bird during the spatial path learning and adjustment. Results: Our study demonstrates that the pigeons progressively learned to solve the path adjustment task after the preferred path is blocked suddenly. Behavioural results show that both the total duration and the path lengths pigeons completed the task during the phase of adjustment are significantly longer than those during the acquisition and recovery phases. Furthermore, neural results show that hippocampal functional connectivity selectively changed during path adjustment. Specifically, we identified depressed connectivity in lower bands (delta and theta) and elevated connectivity in higher bands (slow-gamma and fast-gamma).Conclusions: These results feature both the behavioural response and neural representation of the avian spatial cognitive learning process, suggesting that the functional disarrangement and reorganization of the connectivity in the avian hippocampus during different phases may contribute to our further understanding of the potential mechanism of path learning and adjustment.

scholarly journals Glutamate connectivity associations converge upon the salience network in schizophrenia and healthy controls

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Robert A. McCutcheon ◽  
Toby Pillinger ◽  
Maria Rogdaki ◽  
Juan Bustillo ◽  
Oliver D. Howes
Keyword(s):  
Functional Connectivity ◽  
Frontal Cortex ◽  
Network Connectivity ◽  
Resonance Spectroscopy ◽  
Salience Network ◽  
Healthy Controls ◽  
1H Mrs ◽  
Before And After ◽  
Functional Brain Networks ◽  
The Relationship

AbstractAlterations in cortical inter-areal functional connectivity, and aberrant glutamatergic signalling are implicated in the pathophysiology of schizophrenia but the relationship between the two is unclear. We used multimodal imaging to identify areas of convergence between the two systems. Two separate cohorts were examined, comprising 195 participants in total. All participants received resting state functional MRI to characterise functional brain networks and proton magnetic resonance spectroscopy (1H-MRS) to measure glutamate concentrations in the frontal cortex. Study A investigated the relationship between frontal cortex glutamate concentrations and network connectivity in individuals with schizophrenia and healthy controls. Study B also used 1H-MRS, and scanned individuals with schizophrenia and healthy controls before and after a challenge with the glutamatergic modulator riluzole, to investigate the relationship between changes in glutamate concentrations and changes in network connectivity. In both studies the network based statistic was used to probe associations between glutamate and connectivity, and glutamate associated networks were then characterised in terms of their overlap with canonical functional networks. Study A involved 76 individuals with schizophrenia and 82 controls, and identified a functional network negatively associated with glutamate concentrations that was concentrated within the salience network (p < 0.05) and did not differ significantly between patients and controls (p > 0.85). Study B involved 19 individuals with schizophrenia and 17 controls and found that increases in glutamate concentrations induced by riluzole were linked to increases in connectivity localised to the salience network (p < 0.05), and the relationship did not differ between patients and controls (p > 0.4). Frontal cortex glutamate concentrations are associated with inter-areal functional connectivity of a network that localises to the salience network. Changes in network connectivity in response to glutamate modulation show an opposite effect compared to the relationship observed at baseline, which may complicate pharmacological attempts to simultaneously correct glutamatergic and connectivity aberrations.

scholarly journals Cerebral functional networks during sleep in young and older individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Véronique Daneault ◽  
Pierre Orban ◽  
Nicolas Martin ◽  
Christian Dansereau ◽  
Jonathan Godbout ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Plasticity ◽  
Network Connectivity ◽  
Sleep Stages ◽  
Functional Networks ◽  
Older Individuals ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Age Related ◽  
Light Sleep

AbstractEven though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.

scholarly journals Effect of corpus callosum agenesis on the language network in children and adolescents

2021 ◽  
Author(s):  
Lisa Bartha-Doering ◽  
Ernst Schwartz ◽  
Kathrin Kollndorfer ◽  
Florian Ph. S. Fischmeister ◽  
Astrid Novak ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Corpus Callosum ◽  
Network Connectivity ◽  
Functional Language ◽  
Healthy Controls ◽  
Language Abilities ◽  
Verbal Abilities ◽  
Left And Right ◽  
Language Network

AbstractThe present study is interested in the role of the corpus callosum in the development of the language network. We, therefore, investigated language abilities and the language network using task-based fMRI in three cases of complete agenesis of the corpus callosum (ACC), three cases of partial ACC and six controls. Although the children with complete ACC revealed impaired functions in specific language domains, no child with partial ACC showed a test score below average. As a group, ACC children performed significantly worse than healthy controls in verbal fluency and naming. Furthermore, whole-brain ROI-to-ROI connectivity analyses revealed reduced intrahemispheric and right intrahemispheric functional connectivity in ACC patients as compared to controls. In addition, stronger functional connectivity between left and right temporal areas was associated with better language abilities in the ACC group. In healthy controls, no association between language abilities and connectivity was found. Our results show that ACC is associated not only with less interhemispheric, but also with less right intrahemispheric language network connectivity in line with reduced verbal abilities. The present study, thus, supports the excitatory role of the corpus callosum in functional language network connectivity and language abilities.

scholarly journals fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task

2021 ◽  
Vol 11 (11) ◽  
pp. 4922
Author(s):  
Tengfei Ma ◽  
Wentian Chen ◽  
Xin Li ◽  
Yuting Xia ◽  
Xinhua Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Duration ◽  
Signal Classification ◽  
Imagery Task ◽  
Neural Activities ◽  
Computer Interfaces ◽  
Acquisition Device ◽  
The Time Domain ◽  
The Brain

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

CNTM-03. Functional connectivity networks in patients with brain tumors and vascular lesions in the occipital cortex

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi224-vi225
Author(s):  
Katharina Rosengarth ◽  
Katharina Hense ◽  
Tina Plank ◽  
Mark Greenlee ◽  
Christina Wendl ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Tumors ◽  
Visual Processing ◽  
Network Connectivity ◽  
Occipital Cortex ◽  
Brain Lesions ◽  
Vascular Lesions ◽  
Surrounding Tissue ◽  
Tumor Patients ◽  
Default Mode

Abstract OBJECTIVE Space-occupying brain lesions as brain tumors in the occipital lobe have only been sparsely investigated so far, as this localization is extremely rare with only 1% of cases. It is still unclear how this affects the overall organization of the visual system. We investigated functional connectivity of functional networks associated with higher visual processing between patients with occipital space-occupying lesion in the occipital cortex and healthy controls. METHODS 12 patients with brain tumors, 7 patients with vascular lesions in the occipital cortex and 19 healthy subjects matched for age and sex were included. During functional MRI patients and subjects performed a visual excentricity mapping task. Data analysis was done using CONN toolbox based on Matlab. See-to-ROI connectivities of 23 Regions of Interest (ROIs) implemented in the CONN toolbox which were assigned to the Default Mode, Visual, Salience, Dorsal Attention, and Frontoparietal network were assessed. For each subject, connectivity was calculated using Fischer transformed pairwise correlations. These correlations were first considered separately for each group in one-sample analyses and then compared between the groups. RESULTS Main results show, that compared to control subjects and vascular patients, tumor patients showed weaker intra-network connectivity of components of all networks except the default-network. Tumor patients showed even stronger between-network connectivity in the default-mode network compared to the other groups. Weaker connectivity was observed within the salience network in both patient groups compared to controls. CONCLUSION The results indicate that in the course of the disease, compensatory countermeasures take place in the brain against a brain tumor or a space-occupying brain lesion with the aim of maintaining the performance level and cognitive processes for as long as possible. However, more research is needed in this area to understand the mechanisms and effects of brain tumors and space-consuming brain lesions on surrounding tissue.

scholarly journals Selective increase of functional network connectivity in Arc-positive neuronal engrams after long-term potentiation

2020 ◽  
Author(s):  
Yuheng Jiang ◽  
Antonius M.J. VanDongen
Keyword(s):  
Functional Connectivity ◽  
Hippocampal Neurons ◽  
Network Effects ◽  
Network Connectivity ◽  
Long Term Potentiation ◽  
Early Gene ◽  
Memory Traces ◽  
Term Potentiation ◽  
Memory Engram

ABSTRACTNew tools in optogenetics and molecular biology have culminated in recent studies which mark immediate-early gene (IEG)-expressing neurons as memory traces or engrams. Although the activity-dependent expression of IEGs has been successfully utilised to label memory traces, their roles in engram specification is incompletely understood. Outstanding questions remain as to whether expression of IEGs can interplay with network properties such as functional connectivity and also if neurons expressing different IEGs are functionally distinct. We investigated the expression of Arc and c-Fos, two commonly utilised IEGs in memory engram specification, in cultured hippocampal neurons. After pharmacological induction of long-term potentiation (LTP) in the network, we noted an emergent network property of refinement in functional connectivity between neurons, characterized by a global down-regulation of network connectivity, together with strengthening of specific connections. Subsequently, we show that Arc expression correlates with the effects of network refinement, with Arc-positive neurons being selectively strengthened. Arc positive neurons were also found to be located in closer physical proximity to each other in the network. While the expression pattern of IEGs c-Fos and Arc strongly overlaps, Arc was more selectively expressed than c-Fos. These IEGs also act together in coding information about connection strength pruning. These results demonstrate important links between IEG expression and network connectivity, which serve to bridge the gap between cellular correlates and network effects in learning and memory.

scholarly journals Local GABA concentration is related to network-level resting functional connectivity

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Charlotte J Stagg ◽  
Velicia Bachtiar ◽  
Ugwechi Amadi ◽  
Christel A Gudberg ◽  
Andrei S Ilie ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Primary Motor Cortex ◽  
Empirical Studies ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Resonance Spectroscopy ◽  
Resting State Networks ◽  
Inhibitory Neurotransmitter ◽  
Motor Network

Anatomically plausible networks of functionally inter-connected regions have been reliably demonstrated at rest, although the neurochemical basis of these ‘resting state networks’ is not well understood. In this study, we combined magnetic resonance spectroscopy (MRS) and resting state fMRI and demonstrated an inverse relationship between levels of the inhibitory neurotransmitter GABA within the primary motor cortex (M1) and the strength of functional connectivity across the resting motor network. This relationship was both neurochemically and anatomically specific. We then went on to show that anodal transcranial direct current stimulation (tDCS), an intervention previously shown to decrease GABA levels within M1, increased resting motor network connectivity. We therefore suggest that network-level functional connectivity within the motor system is related to the degree of inhibition in M1, a major node within the motor network, a finding in line with converging evidence from both simulation and empirical studies.

scholarly journals Multiple overlapping dynamic patterns of the visual sensory network in schizophrenia

2020 ◽  
Author(s):  
Mohammad S.E Sendi ◽  
Godfrey D Pearlson ◽  
Daniel H Mathalon ◽  
Judith M Ford ◽  
Adrian Preda ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Visual Processing ◽  
Information Transfer ◽  
Pearson Correlation ◽  
Visual Learning ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Connectivity Pattern ◽  
Middle Temporal Gyrus ◽  
Middle Temporal

Although visual processing impairments have been explored in schizophrenia (SZ), their underlying neurobiology of the visual processing impairments has not been widely studied. Also, while some research has hinted at differences in information transfer and flow in SZ, there are few investigations of the dynamics of functional connectivity within visual networks. In this study, we analyzed resting-state fMRI data of the visual sensory network (VSN) in 160 healthy control (HC) subjects and 151 SZ subjects. We estimated 9 independent components within the VSN. Then, we calculated the dynamic functional network connectivity (dFNC) using the Pearson correlation. Next, using k-means clustering, we partitioned the dFNCs into five distinct states, and then we calculated the portion of time each subject spent in each state, that we termed the occupancy rate (OCR). Using OCR, we compared HC with SZ subjects and investigated the link between OCR and visual learning in SZ subjects. Besides, we compared the VSN functional connectivity of SZ and HC subjects in each state. We found that this network is indeed highly dynamic. Each state represents a unique connectivity pattern of fluctuations in VSN FNC, and all states showed significant disruption in SZ. Overall, HC showed stronger connectivity within the VSN in states. SZ subjects spent more time in a state in which the connectivity between the middle temporal gyrus and other regions of VNS is highly negative. Besides, OCR in a state with strong positive connectivity between middle temporal gyrus and other regions correlated significantly with visual learning scores in SZ.

scholarly journals The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

2019 ◽  
Author(s):  
Hannes Almgren ◽  
Frederik Van de Steen ◽  
Adeel Razi ◽  
Karl Friston ◽  
Daniele Marinazzo
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Effective Connectivity ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Measurement Noise ◽  
Resting State Networks ◽  
Cross Sectional ◽  
Global Signal ◽  
Leibler Divergence

AbstractThe influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.

scholarly journals Lynn_etal_MathSkills_CPM_Preprint_V1_05July2021

2021 ◽  
Author(s):  
Andrew Lynn ◽  
Eric D. Wilkey ◽  
Gavin Price
Keyword(s):  
Functional Connectivity ◽  
Predictive Modeling ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Math Skills ◽  
Whole Brain ◽  
Building Models ◽  
State Dependent ◽  
Brain Behavior

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

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