scholarly journals Large-scale functional coactivation patterns reflect the structural connectivity of the medial prefrontal cortex

2020 ◽  
Author(s):  
Dale T Tovar ◽  
Robert S Chavez
Keyword(s):  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Large Scale ◽  
Spatial Scales ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Machine Learning Techniques ◽  
Affective Processing ◽  
Affective Neuroscience ◽  
Psychological Phenomena

Abstract The medial prefrontal cortex (MPFC) is among the most consistently implicated brain regions in social and affective neuroscience. Yet, this region is also highly functionally heterogeneous across many domains and has diverse patterns of connectivity. The extent to which the communication of functional networks in this area is facilitated by its underlying structural connectivity fingerprint is critical for understanding how psychological phenomena are represented within this region. In the current study, we combined diffusion magnetic resonance imaging and probabilistic tractography with large-scale meta-analysis to investigate the degree to which the functional co-activation patterns of the MPFC is reflected in its underlying structural connectivity. Using unsupervised machine learning techniques, we compared parcellations between the two modalities and found congruence between parcellations at multiple spatial scales. Additionally, using connectivity and coactivation similarity analyses, we found high correspondence in voxel-to-voxel similarity between each modality across most, but not all, subregions of the MPFC. These results provide evidence that meta-analytic functional coactivation patterns are meaningfully constrained by underlying neuroanatomical connectivity and provide convergent evidence of distinct subregions within the MPFC involved in affective processing and social cognition.

scholarly journals Large-scale functional coactivation patterns reflect the structural connectivity of the medial prefrontal cortex

2020 ◽  
Author(s):  
Dale T. Tovar ◽  
Robert Chavez
Keyword(s):  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Large Scale ◽  
Spatial Scales ◽  
Structural Connectivity ◽  
Machine Learning Techniques ◽  
Affective Processing ◽  
Activation Patterns ◽  
Psychological Phenomena ◽  
Co Activation

The medial prefrontal cortex (MPFC) is among the most consistently implicated brain regions in social and affective neuroscience. Yet, this region is also highly functionally heterogeneous across many domains and has diverse patterns of connectivity. The extent to which the communication of functional networks in this area is facilitated by its underlying structural connectivity fingerprint is critical for understanding how psychological phenomena are represented within this region. In the current study, we combined diffusion magnetic resonance imaging and probabilistic tractography with large-scale meta-analysis to investigate the degree to which the functional co-activation patterns of the MPFC is reflected in its underlying structural connectivity. Using unsupervised machine learning techniques, we compared parcellations between the two modalities and found congruence between parcellations at multiple spatial scales. Additionally, using connectivity and coactivation similarity analyses, we found high correspondence in voxel-to-voxel similarity between each modality across most, but not all, subregions of the MPFC. These results provide evidence that meta-analytic functional co-activation patterns are meaningfully constrained by underlying neuroanatomical connectivity and provide convergent evidence of distinct subregions within the MPFC involved in affective processing and social cognition.

scholarly journals Neural correlates of integrated self and social processing

2020 ◽  
Vol 15 (9) ◽  
pp. 941-949
Author(s):  
Laura Finlayson-Short ◽  
Christopher G Davey ◽  
Ben J Harrison
Keyword(s):  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Processing Condition ◽  
Emotional Valence ◽  
Neural Correlates ◽  
Brain Regions ◽  
Affective Processing ◽  
Social Processing ◽  
Referential Processing ◽  
Core Self

Abstract Self-referential and social processing are often engaged concurrently in naturalistic judgements and elicit activity in overlapping brain regions. We have termed this integrated processing ‘self-other referential processing’ and developed a task to measure its neural correlates. Ninety-eight healthy young people aged 16–25 (M = 21.5 years old, 67% female) completed our novel functional magnetic resonance imaging task. The task had two conditions, an active self-other referential processing condition in which participants rated how much they related to emotional faces and a control condition. Rating relatedness required thinking about oneself (self-referential processing) and drawing a comparison to an imagined other (social processing). Self-other referential processing elicited activity in the default mode network and social cognition system; most notably in the ‘core self’ regions of the medial prefrontal cortex and posterior cingulate cortex. Relatedness and emotional valence directly modulated activity in these core self areas, while emotional valence additionally modulated medial prefrontal cortex activity. This shows the key role of the medial prefrontal cortex in constructing the ‘social-affective self’. This may help to unify disparate models of medial prefrontal cortex function, demonstrating its role in coordinating multiple processes—self-referential, social and affective processing—to allow the self to exist in a complex social world.

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

scholarly journals Novel Inferences in a Multidimensional Social Network Use a Grid-like Code

2020 ◽  
Author(s):  
Seongmin A. Park ◽  
Douglas S. Miller ◽  
Erie D. Boorman
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Entorhinal Cortex ◽  
Brain Regions ◽  
Cognitive Map ◽  
General Mechanism ◽  
Temporoparietal Junction ◽  
Flexible Behavior ◽  
Discrete Problems

ABSTRACTGeneralizing experiences to guide decision making in novel situations is a hallmark of flexible behavior. It has been hypothesized such flexibility depends on a cognitive map of an environment or task, but directly linking the two has proven elusive. Here, we find that discretely sampled abstract relationships between entities in an unseen two-dimensional (2-D) social hierarchy are reconstructed into a unitary 2-D cognitive map in the hippocampus and entorhinal cortex. We further show that humans utilize a grid-like code in several brain regions, including entorhinal cortex and medial prefrontal cortex, for inferred direct trajectories between entities in the reconstructed abstract space during discrete decisions. Moreover, these neural grid-like codes in the entorhinal cortex predict neural decision value computations in the medial prefrontal cortex and temporoparietal junction area during choice. Collectively, these findings show that grid-like codes are used by the human brain to infer novel solutions, even in abstract and discrete problems, and suggest a general mechanism underpinning flexible decision making and generalization.

scholarly journals Constitutive Genetic Deletion of Hcn1 Increases Alcohol Preference during Adolescence

2020 ◽  
Vol 10 (11) ◽  
pp. 763
Author(s):  
Michael C. Salling ◽  
Neil L. Harrison
Keyword(s):  
Prefrontal Cortex ◽  
Alcohol Consumption ◽  
Medial Prefrontal Cortex ◽  
Neuronal Excitability ◽  
Cell Types ◽  
Brain Regions ◽  
Alcohol Preference ◽  
Hcn Channels ◽  
Direct Role ◽  
Genetic Deletion

The hyperpolarization-activated cyclic nucleotide-gated channel (HCN), which underlies the hyperpolarization-activated cation current (Ih), has diverse roles in regulating neuronal excitability across cell types and brain regions. Recently, HCN channels have been implicated in preclinical models of substance abuse including alcohol. In the prefrontal cortex of rodents, HCN expression and Ih magnitude are developmentally regulated during adolescence and may be vulnerable to alcohol’s effects. In mice, binge alcohol consumption during the adolescent period results in a sustained reduction in Ih that coincides with increased alcohol consumption in adulthood, yet the direct role HCN channels have on alcohol consumption are unknown. Here, we show that the genetic deletion of Hcn1 causes an increase in alcohol preference on intermittent 2-bottle choice task in homozygous null (HCN1−/−) male mice compared to wild-type littermates without affecting saccharine or quinine preference. The targeted viral deletion of HCN1 in pyramidal neurons of the medial prefrontal cortex resulted in a gradual loss of Hcn1 expression and a reduction in Ih magnitude during adolescence, however, this did not significantly affect alcohol consumption or preference. We conclude that while HCN1 regulates alcohol preference, the genetic deletion of Hcn1 in the medial prefrontal cortex does not appear to be the locus for this effect.

scholarly journals Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

2020 ◽  
Author(s):  
Kristen R. Maynard ◽  
Leonardo Collado-Torres ◽  
Lukas M. Weber ◽  
Cedric Uytingco ◽  
Brianna K. Barry ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prefrontal Cortex ◽  
Web Application ◽  
Dorsolateral Prefrontal Cortex ◽  
Large Scale ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Sequencing Data ◽  
Dorsolateral Prefrontal ◽  
Transcriptomics Data

AbstractWe used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research (http://research.libd.org/spatialLIBD).

scholarly journals Integrating memories: Congruency and reactivation aid memory integration through reinstatement of prior knowledge

2019 ◽  
Author(s):  
Marlieke T.R. van Kesteren ◽  
Paul Rignanese ◽  
Pierre G. Gianferrara ◽  
Lydia Krabbendam ◽  
Martijn Meeter
Keyword(s):  
Prefrontal Cortex ◽  
Prior Knowledge ◽  
Medial Prefrontal Cortex ◽  
Medial Temporal Lobe ◽  
Knowledge Building ◽  
Activity Patterns ◽  
Brain Regions ◽  
Memory Integration ◽  
The Brain ◽  
Parahippocampal Place Area

AbstractBuilding consistent knowledge schemas that organize information and guide future learning is of great importance in everyday life. Such knowledge building is suggested to occur through reinstatement of prior knowledge during new learning in stimulus-specific brain regions. This process is proposed to yield integration of new with old memories, supported by the medial prefrontal cortex (mPFC) and medial temporal lobe (MTL). Possibly as a consequence, congruency of new information with prior knowledge is known to enhance subsequent memory. Yet, it is unknown how reactivation and congruency interact to optimize memory integration processes that lead to knowledge schemas. To investigate this question, we here used an adapted AB-AC inference paradigm in combination with functional Magnetic Resonance Imaging (fMRI). Participants first studied an AB-association followed by an AC-association, so B (a scene) and C (an object) were indirectly linked through their common association with A (an unknown pseudoword). BC-associations were either congruent or incongruent with prior knowledge (e.g. a bathduck or a hammer in a bathroom), and participants were asked to report subjective reactivation strength for B while learning AC. Behaviorally, both the congruency and reactivation measures enhanced memory integration. In the brain, these behavioral effects related to univariate and multivariate parametric effects of congruency and reactivation on activity patterns in the MTL, mPFC, and Parahippocampal Place Area (PPA). Moreover, mPFC exhibited larger connectivity with the PPA for more congruent associations. These outcomes provide insights into the neural mechanisms underlying memory integration enhancement, which can be important for educational learning.Significance statementHow does our brain build knowledge through integrating information that is learned at different periods in time? This question is important in everyday learning situations such as educational settings. Using an inference paradigm, we here set out to investigate how congruency with, and active reactivation of previously learned information affects memory integration processes in the brain. Both these factors were found to relate to activity in memory-related regions such as the medial prefrontal cortex (mPFC) and the hippocampus. Moreover, activity in the parahippocampal place area (PPA), assumed to reflect reinstatement of the previously learned associate, was found to predict subjective reactivation strength. These results show how we can moderate memory integration processes to enhance subsequent knowledge building.

scholarly journals Regional heterogeneity in gene expression, regulation and coherence in hippocampus and dorsolateral prefrontal cortex across development and in schizophrenia

2018 ◽  
Author(s):  
L Collado-Torres ◽  
EE Burke ◽  
A Peterson ◽  
JH Shin ◽  
RE Straub ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prefrontal Cortex ◽  
Brain Region ◽  
Dorsolateral Prefrontal Cortex ◽  
Large Scale ◽  
Brain Regions ◽  
Second Phase ◽  
Regional Heterogeneity ◽  
Surrogate Variable Analysis ◽  
Dorsolateral Prefrontal

AbstractRecent large-scale genomics efforts have better characterized the molecular correlates of schizophrenia in postmortem human neocortex, but not hippocampus which is a brain region prominently implicated in its pathogenesis. Here in the second phase of the BrainSeq Consortium (Phase II), we have generated RiboZero RNA-seq data for 900 samples across both the dorsolateral prefrontal cortex (DLPFC) and the hippocampus (HIPPO) for 551 individuals (286 affected by schizophrenia disorder: SCZD). We identify substantial regional differences in gene expression, in both pre- and post-natal life, and find widespread differences in how genes are regulated across development. By extending quality surrogate variable analysis (qSVA) to multiple brain regions, we identified 48 and 245 differentially expressed genes (DEG) by SCZD diagnosis (FDR<5%) in HIPPO and DLPFC, respectively, with surprisingly minimal overlap in DEG between the two brain regions. We further identified 205,618 brain region-dependent eQTLs (FDR<1%) and found that 124 GWAS risk loci contain eQTLs in at least one of the regions. We also identify potential molecular correlates of in vivo evidence of altered prefrontal-hippocampal functional coherence in schizophrenia. These results underscore the complexity and regional heterogeneity of the transcriptional correlates of schizophrenia, and suggest future schizophrenia therapeutics may need to target molecular pathologies localized to specific brain regions.

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