scholarly journals Primate homologs of mouse cortico-striatal circuits

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Joshua Henk Balsters ◽  
Valerio Zerbi ◽  
Jerome Sallet ◽  
Nicole Wenderoth ◽  
Rogier B Mars
Keyword(s):  
Prefrontal Cortex ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Fingerprint Matching ◽  
Motor Circuits ◽  
Connectivity Patterns ◽  
Species Comparisons ◽  
Similarities And Differences ◽  
Anterior Prefrontal Cortex

With the increasing necessity of animal models in biomedical research, there is a vital need to harmonise findings across species by establishing similarities and differences in rodent and primate neuroanatomy. Using connectivity fingerprint matching, we compared cortico-striatal circuits across humans, non-human primates, and mice using resting-state fMRI data in all species. Our results suggest that the connectivity patterns for the nucleus accumbens and cortico-striatal motor circuits (posterior/lateral putamen) were conserved across species, making them reliable targets for cross-species comparisons. However, a large number of human and macaque striatal voxels were not matched to any mouse cortico-striatal circuit (mouse->human: 85% unassigned; mouse->macaque 69% unassigned; macaque->human; 31% unassigned). These unassigned voxels were localised to the caudate nucleus and anterior putamen, overlapping with executive function and social/language regions of the striatum and connected to prefrontal-projecting cerebellar lobules and anterior prefrontal cortex, forming circuits that seem to be unique for non-human primates and humans.

scholarly journals Primate homologs of mouse cortico-striatal circuits

2019 ◽  
Author(s):  
Joshua H. Balsters ◽  
Valerio Zerbi ◽  
Jérôme Sallet ◽  
Nicole Wenderoth ◽  
Rogier B. Mars
Keyword(s):  
Prefrontal Cortex ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Fingerprint Matching ◽  
Motor Circuits ◽  
Connectivity Patterns ◽  
Species Comparisons ◽  
Similarities And Differences ◽  
Anterior Prefrontal Cortex

AbstractWith the increasing necessity of animal models in biomedical research, there is a vital need to harmonise findings across species by establishing similarities and differences in rodent and primate neuroanatomy. Using a connectivity fingerprint matching approach, we compared cortico-striatal circuits across humans, non-human primates, and mice using resting state fMRI data in all species. Our results suggest that the connectivity patterns for both the nucleus accumbens and cortico-striatal motor circuits (posterior/lateral putamen) were conserved across species, making them reliable targets for cross-species comparisons. However, a large number of human and macaque striatal voxels were not matched to any mouse cortico-striatal circuit (mouse->human: 85% unassigned; mouse->macaque 69% unassigned; macaque->human; 31% unassigned). These unassigned voxels were largely localised to the caudate nucleus and anterior putamen, overlapping with executive function and social/language regions of the striatum, and connected to prefrontal-projecting cerebellar lobules and anterior prefrontal cortex, forming circuits that seem to be unique for non-human primates and humans. Our results demonstrate the potential of connectivity fingerprint matching to bridge the gap between rodent and primate neuroanatomy.

Impaired attentional and socio-affective networks in subjects with antisocial behaviors: a meta-analysis of resting-state functional connectivity studies

2021 ◽  
pp. 1-11
Author(s):  
Jules Roger Dugré ◽  
Stéphane Potvin
Keyword(s):  
Prefrontal Cortex ◽  
Resting State ◽  
Meta Analysis ◽  
Cingulate Cortex ◽  
Resting State Fmri ◽  
Posterior Cingulate Cortex ◽  
Antisocial Behaviors ◽  
Antisocial Personality ◽  
Resting State Functional Connectivity ◽  
Connectivity Patterns

Abstract In the past decade, there has been a growing interest in examining resting-state functional connectivity deficits in subjects with conduct and antisocial personality disorder. Through meta-analyses and literature reviews, extensive work has been done to characterize their abnormalities in brain activation during a wide range of functional magnetic resonance imaging (fMRI) tasks. However, there is currently no meta-analytical evidence regarding neural connectivity patterns during resting-state fMRI. Therefore, we conducted a coordinate-based meta-analysis of resting-state fMRI studies on individuals exhibiting antisocial behaviors. Of the retrieved studies, 18 used a seed-based connectivity approach (513 cases v. 488 controls), 20 employed a non-seed-based approach (453 cases v. 460 controls) and 20 included a correlational analysis between the severity of antisocial behaviors and connectivity patterns (3462 subjects). Meta-analysis on seed-based studies revealed significant connectivity deficits in the amygdala, middle cingulate cortex, ventral posterior cingulate cortex-precuneus, ventromedial and dorsomedial prefrontal cortex, premotor cortex, and superior parietal lobule. Additionally, non-seed-based meta-analysis showed increased connectivity in the ventral posterior cingulate cortex and decreased connectivity in the parietal operculum, calcarine cortex, and cuneus. Finally, we found meta-analytical evidence for negative relationship between the severity of antisocial behaviors and connectivity with the ventromedial prefrontal cortex. Functional characterization and meta-analytical connectivity modeling indicated that these findings overlapped with socio-affective and attentional processes. This further underscores the importance of these functions in the pathophysiology of conduct and antisocial personality disorders.

Aging effect on head motion: A Machine Learning study on resting state fMRI data

2021 ◽  
Vol 352 ◽  
pp. 109084
Author(s):  
Valeria Saccà ◽  
Alessia Sarica ◽  
Andrea Quattrone ◽  
Federico Rocca ◽  
Aldo Quattrone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Aging Effect ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Head Motion ◽  
Learning Study

Altered Resting-State Frontoparietal Control Network in Children with Attention-Deficit/Hyperactivity Disorder

2015 ◽  
Vol 21 (4) ◽  
pp. 271-284 ◽  
Author(s):  
Hsiang-Yuan Lin ◽  
Wen-Yih Isaac Tseng ◽  
Meng-Chuan Lai ◽  
Kayako Matsuo ◽  
Susan Shur-Fen Gau
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Resting State ◽  
Control Network ◽  
Children With Adhd ◽  
Hyperactivity Disorder ◽  
Anterior Prefrontal Cortex ◽  
The Right ◽  
Frontoparietal Control Network

AbstractThe frontoparietal control network, anatomically and functionally interposed between the dorsal attention network and default mode network, underpins executive control functions. Individuals with attention-deficit/hyperactivity disorder (ADHD) commonly exhibit deficits in executive functions, which are mainly mediated by the frontoparietal control network. Involvement of the frontoparietal control network based on the anterior prefrontal cortex in neurobiological mechanisms of ADHD has yet to be tested. We used resting-state functional MRI and seed-based correlation analyses to investigate functional connectivity of the frontoparietal control network in a sample of 25 children with ADHD (7–14 years; mean 9.94±1.77 years; 20 males), and 25 age-, sex-, and performance IQ-matched typically developing (TD) children. All participants had limited in-scanner head motion. Spearman’s rank correlations were used to test the associations between altered patterns of functional connectivity with clinical symptoms and executive functions, measured by the Conners’ Continuous Performance Test and Spatial Span in the Cambridge Neuropsychological Test Automated Battery. Compared with TD children, children with ADHD demonstrated weaker connectivity between the right anterior prefrontal cortex (PFC) and the right ventrolateral PFC, and between the left anterior PFC and the right inferior parietal lobule. Furthermore, this aberrant connectivity of the frontoparietal control network in ADHD was associated with symptoms of impulsivity and opposition-defiance, as well as impaired response inhibition and attentional control. The findings support potential integration of the disconnection model and the executive dysfunction model for ADHD. Atypical frontoparietal control network may play a pivotal role in the pathophysiology of ADHD. (JINS, 2015, 21, 271–284)

scholarly journals Identifying patients with PTSD utilizing resting-state fMRI data and neural network approach

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mirza Naveed Shahzad ◽  
Haider Ali ◽  
Tanzila Saba ◽  
Amjad Rehman ◽  
Hoshang Kolivand ◽  
...  
Keyword(s):  
Neural Network ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data

Data in Brief ◽  
2020 ◽  
Vol 29 ◽  
pp. 105213 ◽  
Author(s):  
Pradyumna Lanka ◽  
D. Rangaprakash ◽  
Sai Sheshan Roy Gotoor ◽  
Michael N. Dretsch ◽  
Jeffrey S. Katz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Resting State Fmri ◽  
Clinical Diagnostics ◽  
Fmri Data ◽  
Matlab Toolbox

scholarly journals Neurophysiology (fMRI)

2015 ◽  
Vol 42 (S1) ◽  
pp. S38-S38
Author(s):  
ST Lang ◽  
B Goodyear ◽  
J Kelly ◽  
P Federico
Keyword(s):  
Cognitive Functioning ◽  
Default Mode Network ◽  
Resting State ◽  
Attentional Control ◽  
Brain Tumours ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Resting State Networks ◽  
Default Mode ◽  
Eloquent Cortex

Background: Resting state functional MRI (rs-fMRI) provides many advantages to task-based fMRI in neurosurgical populations, foremost of which is the lack of the need to perform a task. Many networks can be identified by rs-fMRI in a single period of scanning. Despite the advantages, there is a paucity of literature on rs-fMRI in neurosurgical populations. Methods: Eight patients with tumours near areas traditionally considered as eloquent cortex participated in a five minute rs-fMRI scan. Resting-state fMRI data underwent Independent Component Analysis (ICA) using the Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) toolbox in FSL. Resting state networks (RSNs) were identified on a visual basis. Results: Several RSNs, including language (N=7), sensorimotor (N=7), visual (N=7), default mode network (N=8) and frontoparietal attentional control (n=7) networks were readily identifiable using ICA of rs-fMRI data. Conclusion: These pilot data suggest that ICA applied to rs-fMRI data can be used to identify motor and language networks in patients with brain tumours. We have also shown that RSNs associated with cognitive functioning, including the default mode network and the frontoparietal attentional control network can be identified in individual subjects with brain tumours. While preliminary, this suggests that rs-fMRI may be used pre-operatively to localize areas of cortex important for higher order cognitive functioning.

A Deterministic and Symbolic Regression Hybrid Applied to Resting-State fMRI Data

2014 ◽  
pp. 155-173 ◽  
Author(s):  
Ilknur Icke ◽  
Nicholas A. Allgaier ◽  
Christopher M. Danforth ◽  
Robert A. Whelan ◽  
Hugh P. Garavan ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Symbolic Regression ◽  
Fmri Data
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