scholarly journals Neural Networks Involved in Spatial and Temporal Pattern Separation

2021 ◽  
Author(s):  
Meera Paleja
Keyword(s):  
Neural Networks ◽  
Multivariate Analysis ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Spatial Task ◽  
Pattern Separation ◽  
Processing Stage ◽  
Information Type ◽  
The Right ◽  
Prefrontal Cortices

Critical to episodic memory is pattern separation (PS), the storage of similar inputs as distinct and nonoverlapping. Spatial and temporal PS have been shown to be related to disparate subfields of the hippocampus (HC) in rodents. Extra-HC structures involved have not yet been elucidated. The current work provides an exploratory investigation into the neural correlates of spatial and temporal PS, employing functional magnetic resonance imaging and univariate and multivariate analysis techniques. In Experiment 1, behavioural spatial and temporal memory tasks were developed that assess varying PS demands. Objectives for the experiment were met, in that accuracy was lower and reaction time higher for conditions requiring more engagement of PS. In Experiment 2, whole-brain regions as well as the neural networks involved in spatial and temporal PS were examined, and functional connectivity of the HC was observed. Univariate data revealed unique areas of activation based on information type being encoded (i.e., spatial vs. temporal). The cuneus and HC were uniquely involved in the spatial task, while a wider area of regions including middle occipital and medial frontal areas were activated in the temporal task. Multivariate analyses were convergent with the spatial and temporal context memory literature. The HC, parahippocampal gyri, prefrontal cortices, and precuneus were part of a correlated network in the spatial task. Bilateral prefrontal cortices, including the orbitofrontal cortex were involved in the temporal task. Further, the multivariate analysis revealed qualitatively distinct networks based on memory processing stage (i.e., encoding vs. retrieval). Interestingly, the network included anterior HC in spatial encoding, and posterior HC in spatial and temporal retrieval, consistent with an influential theory positing a rostrocaudal gradient along the HC for encoding and retrieval. Functional connectivity analyses revealed connectivity of the posterior HC seed with temporal and superior parietal areas in the spatial task, and with frontal areas in the temporal task, suggesting the right posterior HC interacts with regions differently based on information type. Results confirm and extend findings from previous literature demonstrating HC involvement in PS, and also suggest HC and extra-HC involvement varies based on processing stage and information type.

scholarly journals Neural Networks Involved in Spatial and Temporal Pattern Separation

2021 ◽  
Author(s):  
Meera Paleja
Keyword(s):  
Neural Networks ◽  
Multivariate Analysis ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Spatial Task ◽  
Pattern Separation ◽  
Processing Stage ◽  
Information Type ◽  
The Right ◽  
Prefrontal Cortices

Critical to episodic memory is pattern separation (PS), the storage of similar inputs as distinct and nonoverlapping. Spatial and temporal PS have been shown to be related to disparate subfields of the hippocampus (HC) in rodents. Extra-HC structures involved have not yet been elucidated. The current work provides an exploratory investigation into the neural correlates of spatial and temporal PS, employing functional magnetic resonance imaging and univariate and multivariate analysis techniques. In Experiment 1, behavioural spatial and temporal memory tasks were developed that assess varying PS demands. Objectives for the experiment were met, in that accuracy was lower and reaction time higher for conditions requiring more engagement of PS. In Experiment 2, whole-brain regions as well as the neural networks involved in spatial and temporal PS were examined, and functional connectivity of the HC was observed. Univariate data revealed unique areas of activation based on information type being encoded (i.e., spatial vs. temporal). The cuneus and HC were uniquely involved in the spatial task, while a wider area of regions including middle occipital and medial frontal areas were activated in the temporal task. Multivariate analyses were convergent with the spatial and temporal context memory literature. The HC, parahippocampal gyri, prefrontal cortices, and precuneus were part of a correlated network in the spatial task. Bilateral prefrontal cortices, including the orbitofrontal cortex were involved in the temporal task. Further, the multivariate analysis revealed qualitatively distinct networks based on memory processing stage (i.e., encoding vs. retrieval). Interestingly, the network included anterior HC in spatial encoding, and posterior HC in spatial and temporal retrieval, consistent with an influential theory positing a rostrocaudal gradient along the HC for encoding and retrieval. Functional connectivity analyses revealed connectivity of the posterior HC seed with temporal and superior parietal areas in the spatial task, and with frontal areas in the temporal task, suggesting the right posterior HC interacts with regions differently based on information type. Results confirm and extend findings from previous literature demonstrating HC involvement in PS, and also suggest HC and extra-HC involvement varies based on processing stage and information type.

scholarly journals A Connectome-Wide Functional Signature of Trait Anger

2021 ◽  
pp. 216770262110302
Author(s):  
M. Justin Kim ◽  
Maxwell L. Elliott ◽  
Annchen R. Knodt ◽  
Ahmad R. Hariri
Keyword(s):  
Functional Connectivity ◽  
Visual Information ◽  
Past Research ◽  
Brain Regions ◽  
Small Sample ◽  
Trait Anger ◽  
Frontal Pole ◽  
Brain Correlates ◽  
Small Sample Sizes ◽  
The Right

Past research on the brain correlates of trait anger has been limited by small sample sizes, a focus on relatively few regions of interest, and poor test–retest reliability of functional brain measures. To address these limitations, we conducted a data-driven analysis of variability in connectome-wide functional connectivity in a sample of 1,048 young adult volunteers. Multidimensional matrix regression analysis showed that self-reported trait anger maps onto variability in the whole-brain functional connectivity patterns of three brain regions that serve action-related functions: bilateral supplementary motor areas and the right lateral frontal pole. We then demonstrate that trait anger modulates the functional connectivity of these regions with canonical brain networks supporting somatomotor, affective, self-referential, and visual information processes. Our findings offer novel neuroimaging evidence for interpreting trait anger as a greater propensity to provoked action, which supports ongoing efforts to understand its utility as a potential transdiagnostic marker for disordered states characterized by aggressive behavior.

scholarly journals Effects of amnesia on processing in the hippocampus and default mode network during a naturalistic memory task: a case study

2018 ◽  
Author(s):  
Christiane Oedekoven ◽  
James L. Keidel ◽  
Stuart Anderson ◽  
Angus Nisbet ◽  
Chris Bird
Keyword(s):  
Functional Connectivity ◽  
Episodic Memory ◽  
Memory Performance ◽  
Memory Task ◽  
Structural Mri ◽  
Brain Regions ◽  
Left Hippocampus ◽  
Cortical Regions ◽  
The Right ◽  
Encoding And Retrieval

Despite their severely impaired episodic memory, individuals with amnesia are able to comprehend ongoing events. Online representations of a current event are thought to be supported by a network of regions centred on the posterior midline cortex (PMC). By contrast, episodic memory is widely believed to be supported by interactions between the hippocampus and these cortical regions. In this MRI study, we investigated the encoding and retrieval of lifelike events (video clips) in a patient with severe amnesia likely resulting from a stroke to the right thalamus, and a group of 20 age-matched controls. Structural MRI revealed grey matter reductions in left hippocampus and left thalamus in comparison to controls. We first characterised the regions activated in the controls while they watched and retrieved the videos. There were no differences in activation between the patient and controls in any of the regions. We then identified a widespread network of brain regions, including the hippocampus, that were functionally connected with the PMC in controls. However, in the patient there was a specific reduction in functional connectivity between the PMC and a region of left hippocampus when both watching and attempting to retrieve the videos. A follow up analysis revealed that in controls the functional connectivity between these regions when watching the videos was correlated with memory performance. Taken together, these findings support the view that the interactions between the PMC and the hippocampus enable the encoding and retrieval of multimodal representations of the contents of an event.

scholarly journals Altered Intra- and Interregional Synchronization in Resting-State Cerebral Networks Associated with Chronic Tinnitus

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Chen Chen ◽  
Jian Zhang ◽  
Xiao-Wei Li ◽  
Wenqing Xia ◽  
Xu Feng ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Chronic Tinnitus ◽  
Healthy Controls ◽  
Tinnitus Distress ◽  
The Right

Objective. Subjective tinnitus is hypothesized to arise from aberrant neural activity; however, its neural bases are poorly understood. To identify aberrant neural networks involved in chronic tinnitus, we compared the resting-state functional magnetic resonance imaging (fMRI) patterns of tinnitus patients and healthy controls.Materials and Methods. Resting-state fMRI measurements were obtained from a group of chronic tinnitus patients (n=29) with normal hearing and well-matched healthy controls (n=30). Regional homogeneity (ReHo) analysis and functional connectivity analysis were used to identify abnormal brain activity; these abnormalities were compared to tinnitus distress.Results. Relative to healthy controls, tinnitus patients had significant greater ReHo values in several brain regions including the bilateral anterior insula (AI), left inferior frontal gyrus, and right supramarginal gyrus. Furthermore, the left AI showed enhanced functional connectivity with the left middle frontal gyrus (MFG), while the right AI had enhanced functional connectivity with the right MFG; these measures were positively correlated with Tinnitus Handicap Questionnaires (r=0.459,P=0.012andr=0.479,P=0.009, resp.).Conclusions. Chronic tinnitus patients showed abnormal intra- and interregional synchronization in several resting-state cerebral networks; these abnormalities were correlated with clinical tinnitus distress. These results suggest that tinnitus distress is exacerbated by attention networks that focus on internally generated phantom sounds.

scholarly journals An investigation of resting state functional connectivity of the anterior and posterior Hippocampal subregions in posttraumatic stress disorder and its relationship with symptoms

2021 ◽  
Author(s):  
Bailee L. Malivoire
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Functional Connectivity ◽  
Posttraumatic Stress ◽  
Resting State ◽  
Stress Disorder ◽  
Brain Regions ◽  
Imaging Biomarkers ◽  
Resting State Functional Connectivity ◽  
Hippocampal Activity ◽  
The Right

Posttraumatic stress disorder (PTSD) is associated with abnormal hippocampal activity; however, the functional connectivity (FC) of the hippocampus with other brain regions and its relations with symptoms warrants further attention. I investigated FC of the hippocampus at a subregional level in PTSD during a resting state compared to trauma exposed controls (TECs). Based on imaging literature in PTSD, I targeted the FCs of the hippocampal head and tail subregions with the amygdala, medial prefrontal cortex (mPFC), and the posterior cingulate (PCC). The PTSD group had significantly greater FC compared to the TEC group between the left hippocampal head and the right amygdala, and for the left hippocampal tail with bilateral PCC. Resting state FC predicted symptom severity at time of scan and 4-months post-scan. These results highlight abnormal illness-related FC with both the hippocampal head and tail and provide support for future investigations of imaging biomarkers predictive of disease progression.

scholarly journals Functional Connectivity of Nucleus Accumbens and Medial Prefrontal Cortex With Other Brain Regions During Early-Abstinence Is Associated With Alcohol Dependence and Relapse: A Resting-Functional Magnetic Resonance Imaging Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Xia Yang ◽  
Ya-jing Meng ◽  
Yu-jie Tao ◽  
Ren-hao Deng ◽  
Hui-yao Wang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prefrontal Cortex ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Anterior Cortex ◽  
The Right

Background: Alcohol dependence (AD) is a chronic recurrent brain disease that causes a heavy disease burden worldwide, partly due to high relapse rates after detoxification. Verified biomarkers are not available for AD and its relapse, although the nucleus accumbens (NAc) and medial prefrontal cortex (mPFC) may play important roles in the mechanism of addiction. This study investigated AD- and relapse-associated functional connectivity (FC) of the NAc and mPFC with other brain regions during early abstinence.Methods: Sixty-eight hospitalized early-abstinence AD male patients and 68 age- and education-matched healthy controls (HCs) underwent resting-functional magnetic resonance imaging (r-fMRI). Using the NAc and mPFC as seeds, we calculated changes in FC between the seeds and other brain regions. Over a follow-up period of 6 months, patients were measured with the Alcohol Use Disorder Identification Test (AUDIT) scale to identify relapse outcomes (AUDIT ≥ 8).Results: Thirty-five (52.24%) of the AD patients relapsed during the follow-up period. AD displayed lower FC of the left fusiform, bilateral temporal superior and right postcentral regions with the NAc and lower FC of the right temporal inferior, bilateral temporal superior, and left cingulate anterior regions with the mPFC compared to controls. Among these FC changes, lower FC between the NAc and left fusiform, lower FC between the mPFC and left cingulate anterior cortex, and smoking status were independently associated with AD. Subjects in relapse exhibited lower FC of the right cingulate anterior cortex with NAc and of the left calcarine sulcus with mPFC compared to non-relapsed subjects; both of these reductions in FC independently predicted relapse. Additionally, FC between the mPFC and right frontal superior gyrus, as well as years of education, independently predicted relapse severity.Conclusion: This study found that values of FC between selected seeds (i.e., the NAc and the mPFC) and some other reward- and/or impulse-control-related brain regions were associated with AD and relapse; these FC values could be potential biomarkers of AD or for prediction of relapse. These findings may help to guide further research on the neurobiology of AD and other addictive disorders.

scholarly journals Comparing the functional structure of neural networks from representational similarity analysis with those from functional connectivity and univariate analyses

2018 ◽  
Author(s):  
Ineke Pillet ◽  
Hans Op de Beeck ◽  
Haemy Lee Masson
Keyword(s):  
Neural Networks ◽  
Functional Connectivity ◽  
Univariate Analysis ◽  
Brain Regions ◽  
Functional Structure ◽  
Functional Networks ◽  
Similarity Analysis ◽  
Network Nodes ◽  
Representational Similarity Analysis ◽  
The Neural Network

AbstractThe invention of representational similarity analysis (RSA, following multi-voxel pattern analysis (MVPA)) has allowed cognitive neuroscientists to identify the representational structure of multiple brain regions, moving beyond functional localization. By comparing these structures, cognitive neuroscientists can characterize how brain areas form functional networks. Univariate analysis (UNIVAR) and functional connectivity analysis (FCA) are two other popular methods to identify the functional structure of brain networks. Despite their popularity, few studies have examined the relationship between the structure of the networks from RSA with those from UNIVAR and FCA. Thus, the aim of the current study is to examine the similarities between neural networks derived from RSA with those from UNIVAR and FCA to explore how these methods relate to each other. We analyzed the data of a previously published study with the three methods and compared the results by performing (partial) correlation and multiple regression analysis. Our findings reveal that neural networks resulting from RSA, UNIVAR, and FCA methods are highly similar to each other even after ruling out the effect of anatomical proximity between the network nodes. Nevertheless, the neural network from each method shows idiosyncratic structure that cannot be explained by any of the other methods. Thus, we conclude that the RSA, UNIVAR and FCA methods provide similar but not identical information on how brain regions are organized in functional networks.

scholarly journals Oxytocin enhances basolateral amygdala activation and functional connectivity in autistic women while processing emotional faces

2021 ◽  
Author(s):  
Tanya Procyshyn ◽  
MIchael Lombardo ◽  
Meng-Chuan Lai ◽  
Bonnie Auyeung ◽  
Sarah Crockford ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Basolateral Amygdala ◽  
Brain Regions ◽  
Placebo Condition ◽  
Emotional Faces ◽  
Amygdala Activation ◽  
Within Subjects ◽  
Emotional Information Processing ◽  
Face Stimuli ◽  
The Right

Background: Oxytocin is hypothesized to promote positive social interactions by enhancing the salience of social stimuli, which may be reflected by altered amygdala activation. While previous neuroimaging studies have reported that oxytocin enhances amygdala activation to emotional face stimuli in autistic men, effects in autistic women remain unclear. Methods: The influence of intranasal oxytocin on neural response to emotional faces vs. shapes were tested in 16 autistic and 21 non-autistic women by fMRI in a placebo-controlled, within-subjects, cross-over design. Effects of group (autistic vs. non-autistic) and drug condition (oxytocin vs. placebo) on the activation and functional connectivity of the basolateral amygdala, the brain’s “salience detector”, were assessed. Relationships between individual differences in autistic-like traits, social anxiety, salivary oxytocin levels, and amygdala activation were also explored.Results: Autistic and non-autistic women showed minimal activation differences in the placebo condition. Significant drug × group interactions were observed for both amygdala activation and functional connectivity. Oxytocin increased left basolateral amygdala activation among autistic women (35 voxel cluster, MNI coordinates of peak voxel = -22 -10 -28; mean change=+0.079%, t=3.159, ptukey=0.0166), but not non-autistic women (mean change =+0.003%, t=0.153, ptukey=0.999). Furthermore, oxytocin increased functional connectivity of the right basolateral amygdala with brain regions associated with socio-emotional information processing in autistic women, but not non-autistic women, thereby attenuating group connectivity differences observed in the placebo condition. Conclusions: This work demonstrates that intranasal oxytocin increases basolateral amygdala activation and connectivity in autistic women while processing emotional faces, which extends and specifies previous findings in autistic men.

scholarly journals Functional connectivity of dorsolateral prefrontal cortex predicts cocaine relapse

2020 ◽  
Author(s):  
Tianye Zhai ◽  
Betty Jo Salmeron ◽  
Hong Gu ◽  
Bryon Adinoff ◽  
Elliot A. Stein ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Dorsolateral Prefrontal Cortex ◽  
Addiction Treatment ◽  
Right Hemisphere ◽  
Cox Regression ◽  
Brain Regions ◽  
Psychosocial Treatment ◽  
Dorsolateral Prefrontal ◽  
The Right

AbstractBackgroundRelapse is one of the most perplexing problems of addiction. The dorsolateral prefrontal cortex (DLPFC) is crucially involved in numerous cognitive and affective processes that are implicated in phenotypes of addiction, and is one of the most frequently reported brain regions with aberrant functionality in substance use disorders. However, the DLPFC is an anatomically large and functionally heterogeneous region, and the specific DLPFC-based circuits that contribute to drug relapse remain unknown.MethodsWe systematically investigated the relationship of cocaine relapse with 98 DLPFC functional circuits defined by evenly sampling the entire bilateral DLPFC in a cohort of cocaine dependent patients (n=43, 5F) following a psychosocial treatment intervention. A Cox regression model was utilized to predict relapse likelihood based on DLPFC functional connectivity strength.ResultsFunctional connectivity from 3 of the 98 DLPFC loci, one on the left and two on the right hemisphere, significantly predicted cocaine relapse with an accuracy of 83.9%, 84.7% and 85.4%, respectively. Combining all three significantly improved prediction validity to 87.5%. Protective and risk circuits related to these DLPFC loci were identified that are known to support “bottom up” drive to use drug and “top down” control over behavior together with social emotional, learning and memory processing.ConclusionThree DLPFC-centric circuits were identified that predict relapse to cocaine use with high accuracy. These functionally distinct DLPFC-based circuits provide insights into the multiple roles played by the DLPFC in cognitive and affective functioning that affects treatment outcome. The identified DLPFC loci may serve as potential neuromodulation targets for addiction treatment and as clinically relevant biomarkers of its efficacy.

scholarly journals Predicting Cortical Signatures of Consciousness using Dynamic Functional Connectivity Graph-Convolutional Neural Networks

2020 ◽  
Author(s):  
A. Grigis ◽  
J. Tasserie ◽  
V. Frouin ◽  
B. Jarraya ◽  
L. Uhrig
Keyword(s):  
Neural Networks ◽  
Functional Connectivity ◽  
Clustering Algorithms ◽  
Brain Regions ◽  
Supervised Machine Learning ◽  
Brain State ◽  
Level Of Consciousness ◽  
Clinical Consequences ◽  
Brain States ◽  
The Brain

AbstractDecoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. Using clustering algorithms, we previously demonstrated that resting-state functional MRI (rsfMRI) data can be split into several clusters also called “brain states” corresponding to “functional configurations” of the brain. Here, we propose to use a supervised machine learning method based on artificial neural networks to predict functional brain states across levels of consciousness from rsfMRI. Because it is key to consider the topology of brain regions used to build the dynamical functional connectivity matrices describing the brain state at a given time, we applied BrainNetCNN, a graph-convolutional neural network (CNN), to predict the brain states in awake and anesthetized non-human primate rsfMRI data. BrainNetCNN achieved a high prediction accuracy that lies in [0.674, 0.765] depending on the experimental settings. We propose to derive the set of connections found to be important for predicting a brain state, reflecting the level of consciousness. The results demonstrate that deep learning methods can be used not only to predict brain states but also to provide additional insight on cortical signatures of consciousness with potential clinical consequences for the monitoring of anesthesia and the diagnosis of disorders of consciousness.

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