scholarly journals Human Primary Olfactory Amygdala Subregions Form Distinct Functional Networks, Suggesting Distinct Olfactory Functions

2021 ◽  
Vol 15 ◽  
Author(s):  
Torben Noto ◽  
Guangyu Zhou ◽  
Qiaohan Yang ◽  
Gregory Lane ◽  
Christina Zelano

Three subregions of the amygdala receive monosynaptic projections from the olfactory bulb, making them part of the primary olfactory cortex. These primary olfactory areas are located at the anterior-medial aspect of the amygdala and include the medial amygdala (MeA), cortical amygdala (CoA), and the periamygdaloid complex (PAC). The vast majority of research on the amygdala has focused on the larger basolateral and basomedial subregions, which are known to be involved in implicit learning, threat responses, and emotion. Fewer studies have focused on the MeA, CoA, and PAC, with most conducted in rodents. Therefore, our understanding of the functions of these amygdala subregions is limited, particularly in humans. Here, we first conducted a review of existing literature on the MeA, CoA, and PAC. We then used resting-state fMRI and unbiased k-means clustering techniques to show that the anatomical boundaries of human MeA, CoA, and PAC accurately parcellate based on their whole-brain resting connectivity patterns alone, suggesting that their functional networks are distinct, relative both to each other and to the amygdala subregions that do not receive input from the olfactory bulb. Finally, considering that distinct functional networks are suggestive of distinct functions, we examined the whole-brain resting network of each subregion and speculated on potential roles that each region may play in olfactory processing. Based on these analyses, we speculate that the MeA could potentially be involved in the generation of rapid motor responses to olfactory stimuli (including fight/flight), particularly in approach/avoid contexts. The CoA could potentially be involved in olfactory-related reward processing, including learning and memory of approach/avoid responses. The PAC could potentially be involved in the multisensory integration of olfactory information with other sensory systems. These speculations can be used to form the basis of future studies aimed at clarifying the olfactory functions of these under-studied primary olfactory areas.

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82715 ◽  
Author(s):  
Guihua Jiang ◽  
Xue Wen ◽  
Yingwei Qiu ◽  
Ruibin Zhang ◽  
Junjing Wang ◽  
...  

2021 ◽  
Author(s):  
Marzena Stefaniuk ◽  
Monika Pawłowska ◽  
Klaudia Nowicka ◽  
Marcin Barański ◽  
Zbigniew Zielinski ◽  
...  

AbstractMany fundamental questions on addiction development are still unanswered. These questions are frequently difficult to address by examining a single brain structure, but can best be addressed at the systems level. Neurons create functional networks that change over time, since brain regions may work together differently in different contexts. We offer a framework for describing the nature behind alcohol binge drinking and the transition to addiction. The present study investigated whole-brain c-Fos expression following reexposure to alcohol in a model of binge-like drinking in mice in IntelliCage. We developed a dedicated image computational workflow to identify c-Fos-positive cells in three-dimensional images obtained after optical tissue clearing and whole-brain imaging in the light-sheet microscope. We analyzed functional networks and brain modularity following reexposure to alcohol. c-Fos levels in brains from animals that were reexposed to alcohol were clearly different from binge drinking animals. Structures involved in reward processing, decision making and characteristic for addictive behaviors stood out particularly. In alcohol reexposed animals differently active structures either gained or lost correlation when compared to the control group.


Author(s):  
Zhen-Zhen Ma ◽  
Jia-Jia Wu ◽  
Xu-Yun Hua ◽  
Mou-Xiong Zheng ◽  
Xiang-Xin Xing ◽  
...  

NeuroImage ◽  
2021 ◽  
Vol 231 ◽  
pp. 117844
Author(s):  
Behzad Iravani ◽  
Artin Arshamian ◽  
Peter Fransson ◽  
Neda Kaboodvand

NeuroImage ◽  
2018 ◽  
Vol 174 ◽  
pp. 599-604 ◽  
Author(s):  
M. Pannunzi ◽  
R. Hindriks ◽  
R.G. Bettinardi ◽  
E. Wenger ◽  
N. Lisofsky ◽  
...  

2018 ◽  
Vol 14 (1) ◽  
pp. 100-109 ◽  
Author(s):  
Jinliang Zhang ◽  
Gaoyan Zhang ◽  
Xianglin Li ◽  
Peiyuan Wang ◽  
Bin Wang ◽  
...  

Neuron ◽  
2018 ◽  
Vol 100 (3) ◽  
pp. 728-738.e7 ◽  
Author(s):  
Jeffrey B. Wang ◽  
Muna Aryal ◽  
Qian Zhong ◽  
Daivik B. Vyas ◽  
Raag D. Airan

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.


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