scholarly journals In silico exploration of mouse brain dynamics by focal stimulation reflects the organization of functional networks and sensory processing

2020 ◽  
Vol 4 (3) ◽  
pp. 807-851
Author(s):  
Andreas Spiegler ◽  
Javad Karimi Abadchi ◽  
Majid Mohajerani ◽  
Viktor K. Jirsa
Keyword(s):  
Information Processing ◽  
Sensory Processing ◽  
Resting State ◽  
Functional Organization ◽  
Brain Dynamics ◽  
Functional Networks ◽  
Brain Responses ◽  
Sensory Cortices ◽  
Stimulation Paradigm

Resting-state functional networks such as the default mode network (DMN) dominate spontaneous brain dynamics. To date, the mechanisms linking brain structure and brain dynamics and functions in cognition, perception, and action remain unknown, mainly due to the uncontrolled and erratic nature of the resting state. Here we used a stimulation paradigm to probe the brain’s resting behavior, providing insights on state-space stability and multiplicity of network trajectories after stimulation. We performed explorations on a mouse model to map spatiotemporal brain dynamics as a function of the stimulation site. We demonstrated the emergence of known functional networks in brain responses. Several responses heavily relied on the DMN and were suggestive of the DMN playing a mechanistic role between functional networks. We probed the simulated brain responses to the stimulation of regions along the information processing chains of sensory systems from periphery up to primary sensory cortices. Moreover, we compared simulated dynamics against in vivo brain responses to optogenetic stimulation. Our results underwrite the importance of anatomical connectivity in the functional organization of brain networks and demonstrate how functionally differentiated information processing chains arise from the same system.

scholarly journals Brainwide functional networks associated with anatomically- and functionally-defined hippocampal subfields using ultrahigh-resolution fMRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wei-Tang Chang ◽  
Stephanie K. Langella ◽  
Yichuan Tang ◽  
Sahar Ahmad ◽  
Han Zhang ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
A Priori ◽  
Longitudinal Axis ◽  
Functional Networks ◽  
Resting State Functional Connectivity ◽  
Underlying Assumption ◽  
Isotropic Resolution ◽  
Hippocampal Subfields ◽  
Fine Grained

AbstractThe hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneous between subfields. However, several prior studies have observed similar resting-state functional connectivity (FC) profiles between aHPSFs. Alternatively, data-driven approaches investigate hippocampal functional organization without a priori assumptions. However, insufficient spatial resolution may result in a number of caveats concerning the reliability of the results. Hence, we developed a functional Magnetic Resonance Imaging (fMRI) sequence on a 7 T MR scanner achieving 0.94 mm isotropic resolution with a TR of 2 s and brain-wide coverage to (1) investigate the functional organization within hippocampus at rest, and (2) compare the brain-wide FC associated with fine-grained aHPSFs and functionally-defined hippocampal subfields (fHPSFs). This study showed that fHPSFs were arranged along the longitudinal axis that were not comparable to the lamellar structures of aHPSFs. For brain-wide FC, the fHPSFs rather than aHPSFs revealed that a number of fHPSFs connected specifically with some of the functional networks. Different functional networks also showed preferential connections with different portions of hippocampal subfields.

scholarly journals Inhibitory connections in the honeybee antennal lobe are spatially patchy

2013 ◽  
Vol 109 (2) ◽  
pp. 332-343 ◽  
Author(s):  
Cyrille C. Girardin ◽  
Sabine Kreissl ◽  
C. Giovanni Galizia
Keyword(s):  
Sensory Processing ◽  
Antennal Lobe ◽  
Functional Organization ◽  
Classical Model ◽  
Activity Patterns ◽  
Spatial Logic ◽  
Brain Center ◽  
In Vivo Calcium Imaging ◽  
Inhibitory Interactions

The olfactory system is a classical model for studying sensory processing. The first olfactory brain center [the olfactory bulb of vertebrates and the antennal lobe (AL) of insects] contains spherical neuropiles called glomeruli. Each glomerulus receives the information from one olfactory receptor type. Interglomerular computation is accomplished by lateral connectivity via interneurons. However, the spatial and functional organization of these lateral connections is not completely understood. Here we studied the spatial logic in the AL of the honeybee. We combined topical application of neurotransmitters, olfactory stimulations, and in vivo calcium imaging to visualize the arrangement of lateral connections. Suppression of activity in a single glomerulus with γ-aminobutyric acid (GABA) while presenting an odor reveals the existence of inhibitory interactions. Stimulating a glomerulus with acetylcholine (ACh) activates inhibitory interglomerular connections that can reduce odor-evoked responses. We show that this lateral network is patchy, in that individual glomeruli inhibit other glomeruli with graded strength, but in a spatially discontinuous manner. These results suggest that processing of olfactory information requires combinatorial activity patterns with complex topologies across the AL.

scholarly journals Consistency in macroscopic human brain responses to noisy time-varying visual inputs

2019 ◽  
Author(s):  
Keiichi Kitajo ◽  
Takumi Sase ◽  
Yoko Mizuno ◽  
Hiromichi Suetani
Keyword(s):  
Information Processing ◽  
Human Brain ◽  
Brain Dynamics ◽  
Time Varying ◽  
Ongoing Activity ◽  
Visual Inputs ◽  
Brain Responses ◽  
Individual Variations ◽  
Open Question ◽  
The Brain

AbstractIt is an open question as to whether macroscopic human brain responses to repeatedly presented external inputs show consistent patterns across trials. We here provide experimental evidence that human brain responses to noisy time-varying visual inputs, as measured by scalp electroencephalography (EEG), show a signature of consistency. The results indicate that the EEG-recorded responses are robust against fluctuating ongoing activity, and that they respond to visual stimuli in a repeatable manner. This consistency presumably mediates robust information processing in the brain. Moreover, the EEG response waveforms were discriminable between individuals, and were invariant over a number of days within individuals. We reveal that time-varying noisy visual inputs can harness macroscopic brain dynamics and can manifest hidden individual variations.

scholarly journals Functional segregation of the human basal forebrain using resting state neuroimaging

2017 ◽  
Author(s):  
Ross D. Markello ◽  
R. Nathan Spreng ◽  
Wen-Ming Luh ◽  
Adam K. Anderson ◽  
Eve De Rosa
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Basal Forebrain ◽  
Nucleus Basalis ◽  
Functional Networks ◽  
List Type ◽  
Functional Connections ◽  
Diagonal Band ◽  
Hippocampal Complex

AbstractThe basal forebrain (BF) is poised to play an important neuromodulatory role in brain re-gions important to cognition due to its broad projections and complex neurochemistry. While significant in vivo work has been done to elaborate BF function in nonhuman rodents and primates, comparatively limited work has examined the in vivo function of the human BF. In the current study we used multi-echo resting state functional magnetic resonance imaging (rs-fMRI) from 100 young adults (18-34 years) to assess the potential segregation of human BF nuclei as well as their associated projections. Bottom-up clustering of voxel-wise functional connectivity maps yielded adjacent functional clusters within the BF that closely aligned with the distinct, hypothesized nuclei important to cognition: the nucleus basalis of Meynert (NBM) and the me-dial septum/diagonal band of Broca (MS/DB). Examining their separate functional connections, the NBM and MS/DB revealed distinct projection patterns, suggesting a conservation of nuclei-specific functional connectivity with homologous regions known to be anatomically innervated by the BF. Specifically, the NBM demonstrated coupling with a widespread cortical network as well as the amygdala, whereas the MS/DB revealed coupling with a more circumscribed net-work, including the orbitofrontal cortex and hippocampal complex. Collectively, these in vivo rs-fMRI data demonstrate that the human BF nuclei support functional networks distinct as-pects of resting-state functional networks, suggesting the human BF may be a neuromodulatory hub important for orchestrating network dynamics.HighlightsThe basal forebrain NBM and the MS/DB support two distinct functional networksFunctional networks closely overlap with known anatomical basal forebrainBasal forebrain networks are distinct from known resting-state functional networks

scholarly journals Sensory Processing and Categorization in Cortical and Deep Neural Networks

2019 ◽  
Author(s):  
Dimitris A. Pinotsis ◽  
Markus Siegel ◽  
Earl K. Miller
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Sensory Processing ◽  
Deep Neural Networks ◽  
Behavioral Model ◽  
Brain Dynamics ◽  
Brain Areas ◽  
Motion Direction ◽  
Neural Representations ◽  
Brain Responses

AbstractMany recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pilot support systems, medical diagnosis algorithms etc.), achieving human-level performance in decision making tasks is still a challenge. At the same time, these tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these decision-making tasks and modeling them using deep neural networks could improve AI performance. Here we modelled some of the complex neural interactions during a sensorimotor decision making task. We investigated how brain dynamics flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We used two different approaches for understanding neural representations. We compared brain responses to 1) the geometry of a sensory or category domain (domain selectivity) and 2) predictions from deep neural networks (computation selectivity). Both approaches gave us similar results. This confirmed the validity of our analyses. Using the first approach, we found that neural representations changed depending on context. We then trained deep recurrent neural networks to perform the same tasks as the animals. Using the second approach, we found that computations in different brain areas also changed flexibly depending on context. Color computations appeared to rely more on sensory processing, while motion computations more on abstract categories. Overall, our results shed light to the biological basis of categorization and differences in selectivity and computations in different brain areas. They also suggest a way for studying sensory and categorical representations in the brain: compare brain responses to both a behavioral model and a deep neural network and test if they give similar results.

scholarly journals Individual variability in functional connectivity architecture of the mouse brain

2020 ◽  
Author(s):  
Eyal Bergmann ◽  
Xenia Gofman ◽  
Alexandra Kavushansky ◽  
Itamar Kahn
Keyword(s):  
Functional Connectivity ◽  
Personalized Medicine ◽  
Individual Variation ◽  
Resting State ◽  
Functional Organization ◽  
Individual Variability ◽  
Functional Networks ◽  
Behavioral Variability ◽  
Mouse Cortex ◽  
Mechanistic Investigations

AbstractThe functional organization of brain networks can be estimated using fMRI by examining the coherence of spontaneous fluctuations in the fMRI signal, a method known as resting-state functional connectivity MRI. Previous studies in humans reported that such functional networks are dominated by stable group and individual factors, demonstrating that fMRI is suited to measuring subject-specific characteristics, and suggesting the utility of such precision fMRI approach in personalized medicine. However, mechanistic investigations to the sources of individual variability in health and disease are limited in humans and thus require animal models. Here, we used repeated-measurement resting-state fMRI in awake mice to quantify the contribution of individual variation to the functional architecture of the mouse cortex. Comparing the organization of functional networks across the group, we found dominant common organizational principles. The data also revealed stable individual features, which create a unique fingerprint that allow identification of individual mice from the group. Examining the distribution of individual variation across the mouse cortex, we found it is homogeneously distributed in both sensory and association networks. Finally, connectome-based predictive modeling of motor behavior in the rotarod task revealed that individual variation in functional connectivity explained behavioral variability. Collectively, these results show that mouse functional networks are characterized by individual variations suggesting that individual variation characterizes the mammalian cortex in general, and not only the primate cortex. These findings lay the foundation for future mechanistic investigations of individual brain organization and pre-clinical studies of brain disorders in the context of personalized medicine.

scholarly journals Consistent local dynamics in the brain across sessions are revealed by whole brain modeling of resting state activity

2017 ◽  
Author(s):  
Patricio Donnelly Kehoe ◽  
Victor M. Saenger ◽  
Nina Lisofsky ◽  
Simone Kühn ◽  
Morten L. Kringelbach ◽  
...  
Keyword(s):  
Resting State ◽  
Transition Point ◽  
Functional Organization ◽  
Group Analysis ◽  
Brain Dynamics ◽  
Single Subject ◽  
High Symmetry ◽  
Whole Brain ◽  
Local Dynamics ◽  
Local Brain

AbstractResting state fMRI has been the primary tool for studying the functional organization of the human brain. However, even at so-called “rest”, ongoing brain activity and its underlying physiological organization is highly dynamic and yet most of the information generated so far comes from group analysis. Here we developed an imaging-based technique capable of portraying information of local dynamics at a single-subject level reliably by using a whole-brain model that estimates a local bifurcation parameter, which reflects if a brain region presents stable, asynchronous or transitory oscillations. Using 50 longitudinal resting state sessions of one single subject and single resting state sessions from a group of 50 participants we demonstrated that individual global and local brain dynamics can be estimated consistently with respect to a reference group using only a scanning time of 15 to 20 minutes. We also showed that brain hubs are closer to a transition point between synchronous and asynchronous oscillatory dynamics and that dynamics in frontal areas have larger variations compared to other regions. Finally, we analyzed the variability and error of these dynamics and found high symmetry between hemispheres, which interestingly was reduced by adding more sessions. The framework presented here can be used to study functional brain dynamics on an individual level, opening new avenues for possible clinical applications.Bullet pointsLocal brain dynamics are consistent across scans.Four scans of five minutes each are enough to get highly reliable and consistent results.Hub areas are in a transition point between a synchronous and asynchronous regime.Variability and error of local dynamics presented high symmetry between hemispheres.

scholarly journals Time scale separation of information processing between sensory and associative regions

2020 ◽  
Author(s):  
P Sorrentino ◽  
G Rabuffo ◽  
R Rucco ◽  
F Baselice ◽  
E Troisi Lopez ◽  
...  
Keyword(s):  
Information Processing ◽  
Resting State ◽  
Large Scale ◽  
Functional Organization ◽  
Brain Regions ◽  
High Temporal Resolution ◽  
Top Down ◽  
Bottom Up ◽  
Speed Of Information Processing ◽  
The Brain

AbstractStimulus perception is assumed to involve the (fast) detection of sensory inputs and their (slower) integration. The capacity of the brain to quickly adapt, at all times, to unexpected stimuli suggests that the interplay between the slow and fast processes happens at short timescales. We hypothesised that, even during resting-state, the flow of information across the brain regions should evolve quickly, but not homogeneously in time. Here we used high temporal-resolution Magnetoencephalography (MEG) signals to estimate the persistence of the information in functional links across the brain. We show that short- and long-lasting retention of the information, entailing different speeds in the update rate, naturally split the brain into two anatomically distinct subnetworks. The “fast updating network” (FUN) is localized in the regions that typically belong to the dorsal and ventral streams during perceptive tasks, while the “slow updating network” (SUN) hinges classically associative areas. Finally, we show that only a subset of the brain regions, which we name the multi-storage core (MSC), belongs to both subnetworks. The MSC is hypothesized to play a role in the communication between the (otherwise) segregated subnetworks.Significance statementThe human brain constantly scans the environment in search of relevant incoming stimuli, and appropriately reconfigures its large-scale activation according to environmental requests. The functional organization substanding these bottom-up and top-down processes, however, is not understood. Studying the speed of information processing between brain regions during resting state, we show the existence of two spatially segregated subnetworks processing information at fast- and slow-rates. Notably, these networks involve the regions that typically belong to the perception stream and the associative regions, respectively. Therefore, we provide evidence that, regardless of the presence of a stimulus, the bottom-up and top-down perceptive pathways are inherent to the resting state dynamics.

scholarly journals Brain-wide functional networks associated with anatomically- and functionally-defined hippocampal subfields using ultrahigh-resolution fMRI

2020 ◽  
Author(s):  
Wei-Tang Chang ◽  
Stephanie K. Langella ◽  
Yichuan Tang ◽  
Han Zhang ◽  
Pew-Thian Yap ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Longitudinal Axis ◽  
Functional Networks ◽  
Resting State Functional Connectivity ◽  
Ultrahigh Resolution ◽  
Underlying Assumption ◽  
Isotropic Resolution ◽  
Hippocampal Subfields ◽  
Lamellar Structures

ABSTRACTThe hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Many studies have shown that aHPSFs, and their respective functional networks, are differentially vulnerable to a variety of disorders. Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneous between subfields. However, several prior studies that have utilized aHPSFs and assessed brain-wide cortical connectivity have observed similar resting-state functional connectivity profiles between aHPSFs. Alternatively, data-driven approaches offer a means to investigate hippocampal functional organization without a priori assumptions. However, insufficient spatial resolution may lead to partial volume effects at the boundaries of hippocampal subfields, resulting in a number of caveats concerning the reliability of the results. Hence, we developed a functional Magnetic Resonance Imaging (fMRI) sequence on a 7T MR scanner achieving 0.94 mm isotropic resolution with a TR of 2s and brain-wide coverage to 1) investigate whether hippocampal functional segmentation with ultrahigh-resolution data demonstrate similar anatomical, lamellar structures in the hippocampus, and 2) define and compare the brain-wide FC associated with fine-grained aHPSFs and functionally-defined hippocampal subfields (fHPSFs). Using a spatially restricted hippocampal Independent Component Analysis (ICA) approach, this study showed that fHPSFs were arranged along the longitudinal axis of the hippocampus that were not comparable to the lamellar structures of aHPSFs. Contrary to the anatomically defined hippocampal subfields which are bilaterally symmetrical, 13 out of 20 fHPSFs were unilateral. For brain-wide FC, the fHPSFs rather than aHPSFs revealed that a number of fHPSFs connected specifically with some of the functional networks. The visual and sensorimotor networks preferentially connected with different portions of CA1, CA3 and CA4/DG. The DMN was also found to connect more extensively with posterior subfields rather than anterior subfields. Finally, the frontoparietal network (FPN) was anticorrelated with the head portion of CA1. The investigation of functional networks associated with the fHPSFs may enhance the sensitivity of biomarkers for a range of neurological disorders, as network-based approaches take into account disease-related alterations in brain-wide interconnections rather than measuring the regional changes of hippocampus.

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