scholarly journals Functional MRI of large scale activity in behaving mice

2020 ◽  
Author(s):  
Madalena S. Fonseca ◽  
Mattia G. Bergomi ◽  
Zachary F. Mainen ◽  
Noam Shemesh
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Spatial Scales ◽  
Distributed Processing ◽  
Brain Regions ◽  
Visually Guided ◽  
Dynamic Interactions ◽  
Complex Behaviour ◽  
Wide Range ◽  
Circuit Techniques

ABSTRACTBehaviour involves complex dynamic interactions across many brain regions. Detecting whole-brain activity in mice performing sophisticated behavioural tasks could facilitate insights into distributed processing underlying behaviour, guide local targeting, and help bridge the disparate spatial scales between rodent and human studies. Here, we present a comprehensive approach for recording brain-wide activity with functional magnetic resonance imaging (fMRI) compatible with a wide range of behavioural paradigms and neuroscience questions. We introduce hardware and procedural advances to allow multi-sensory, multi-action behavioural paradigms in the scanner. We identify signal artifacts arising from task-related body movements and propose novel strategies to suppress them. We validate and explore our approach in a 4-odour classical conditioning and a visually-guided operant task, illustrating how it can be used to extract information insofar intangible to rodent behaviour studies. Our work paves the way for future studies combining fMRI and local circuit techniques during complex behaviour to tackle multi-scale behavioural neuroscience questions.

scholarly journals Persistent magnetic vortex flow at a supergranular vertex

2018 ◽  
Vol 610 ◽  
pp. A84 ◽  
Author(s):  
Iker S. Requerey ◽  
Basilio Ruiz Cobo ◽  
Milan Gošić ◽  
Luis R. Bellot Rubio
Keyword(s):  
Magnetic Fields ◽  
Large Scale ◽  
Vortex Flow ◽  
Spatial Scales ◽  
Magnetic Vortex ◽  
Magnetic Feature ◽  
Magnetic Element ◽  
Flow Maps ◽  
Wide Range ◽  
Intensity Images

Context. Photospheric vortex flows are thought to play a key role in the evolution of magnetic fields. Recent studies show that these swirling motions are ubiquitous in the solar surface convection and occur in a wide range of temporal and spatial scales. Their interplay with magnetic fields is poorly characterized, however. Aims. We study the relation between a persistent photospheric vortex flow and the evolution of a network magnetic element at a supergranular vertex. Methods. We used long-duration sequences of continuum intensity images acquired with Hinode and the local correlation-tracking method to derive the horizontal photospheric flows. Supergranular cells are detected as large-scale divergence structures in the flow maps. At their vertices, and cospatial with network magnetic elements, the velocity flows converge on a central point. Results. One of these converging flows is observed as a vortex during the whole 24 h time series. It consists of three consecutive vortices that appear nearly at the same location. At their core, a network magnetic element is also detected. Its evolution is strongly correlated to that of the vortices. The magnetic feature is concentrated and evacuated when it is caught by the vortices and is weakened and fragmented after the whirls disappear. Conclusions. This evolutionary behavior supports the picture presented previously, where a small flux tube becomes stable when it is surrounded by a vortex flow.

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.

Neurocognitive Aging and Functional Connectivity Using Functional Magnetic Resonance Imaging

2018 ◽  
Author(s):  
Hana Burianová
Keyword(s):  
Functional Connectivity ◽  
Cognitive Processes ◽  
Large Scale ◽  
Healthy Aging ◽  
Brain Activity ◽  
Brain Regions ◽  
Effective Strategies ◽  
Cognitive Improvement ◽  
Age Related ◽  
Neurocognitive Aging

Determining the mechanisms that underlie neurocognitive aging, such as compensation or dedifferentiation, and facilitating the development of effective strategies for cognitive improvement is essential due to the steadily rising aging population. One approach to study the characteristics of healthy aging comprises the assessment of functional connectivity, delineating markers of age-related neurocognitive plasticity. Functional connectivity paradigms characterize complex one-to-many (or many-to-many) structure–function relations, as higher-level cognitive processes are mediated by the interaction among a number of functionally related neural areas rather than localized to discrete brain regions. Task-related or resting-state interregional correlations of brain activity have been used as reliable indices of functional connectivity, delineating age-related alterations in a number of large-scale brain networks, which subserve attention, working memory, episodic retrieval, and task-switching. Together with behavioral and regional activation studies, connectivity studies and modeling approaches have contributed to our understanding of the mechanisms of age-related reorganization of distributed functional networks; specifically, reduced neural specificity (dedifferentiation) and associated impairment in inhibitory control and compensatory neural recruitment.

scholarly journals Organization of Propagated Intrinsic Brain Activity in Individual Humans

2019 ◽  
Vol 30 (3) ◽  
pp. 1716-1734 ◽  
Author(s):  
Ryan V Raut ◽  
Anish Mitra ◽  
Scott Marek ◽  
Mario Ortega ◽  
Abraham Z Snyder ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Cortical Excitability ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Organization ◽  
Individual Level ◽  
Slow Activity ◽  
Lag Structure

Abstract Spontaneous infra-slow (<0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks—altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

NEW CHALLENGES FACING INTEGRATIVE BIOLOGICAL SCIENCE IN THE POST-GENOMIC ERA

2006 ◽  
Vol 14 (02) ◽  
pp. 275-293 ◽  
Author(s):  
CHRISTOPHER S. OEHMEN ◽  
TJERK P. STRAATSMA ◽  
GORDON A. ANDERSON ◽  
GALYA ORR ◽  
BOBBIE-JO M. WEBB-ROBERTSON ◽  
...  
Keyword(s):  
Paradigm Shift ◽  
Large Scale ◽  
Experimental Testing ◽  
Spatial Scales ◽  
Geographical Area ◽  
Biological Data ◽  
Biological Research ◽  
Complex Data ◽  
Discovery Research ◽  
Wide Range

The future of biology will be increasingly driven by the fundamental paradigm shift from hypothesis-driven research to data-driven discovery research employing the growing volume of biological data coupled to experimental testing of new discoveries. But hardware and software limitations in the current workflow infrastructure make it impossible or intractible to use real data from disparate sources for large-scale biological research. We identify key technological developments needed to enable this paradigm shift involving (1) the ability to store and manage extremely large datasets which are dispersed over a wide geographical area, (2) development of novel analysis and visualization tools which are capable of operating on enormous data resources without overwhelming researchers with unusable information, and (3) formalisms for integrating mathematical models of biosystems from the molecular level to the organism population level. This will require the development of algorithms and tools which efficiently utilize high-performance compute power and large storage infrastructures. The end result will be the ability of a researcher to integrate complex data from many different sources with simulations to analyze a given system at a wide range of temporal and spatial scales in a single conceptual model.

scholarly journals Cloud Inhomogeneity from MODIS

2005 ◽  
Vol 18 (23) ◽  
pp. 5110-5124 ◽  
Author(s):  
Lazaros Oreopoulos ◽  
Robert F. Cahalan
Keyword(s):  
Optical Thickness ◽  
Large Scale ◽  
Spatial Scales ◽  
Temporal Variations ◽  
Global Scale ◽  
Time Of Day ◽  
Wide Range ◽  
Level 3 ◽  
Inhomogeneity Parameter ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Two full months (July 2003 and January 2004) of Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Level-3 data from the Terra and Aqua satellites are analyzed in order to characterize the horizontal variability of vertically integrated cloud optical thickness (“cloud inhomogeneity”) at global scales. The monthly climatology of cloud inhomogeneity is expressed in terms of standard parameters, initially calculated for each day of the month at spatial scales of 1° × 1° and subsequently averaged at monthly, zonal, and global scales. Geographical, diurnal, and seasonal changes of inhomogeneity parameters are examined separately for liquid and ice phases and separately over land and ocean. It is found that cloud inhomogeneity is overall weaker in summer than in winter. For liquid clouds, it is also consistently weaker for local morning than local afternoon and over land than ocean. Cloud inhomogeneity is comparable for liquid and ice clouds on a global scale, but with stronger spatial and temporal variations for the ice phase, and exhibits an average tendency to be weaker for near-overcast or overcast grid points of both phases. Depending on cloud phase, hemisphere, surface type, season, and time of day, hemispheric means of the inhomogeneity parameter ν (roughly the square of the ratio of optical thickness mean to standard deviation) have a wide range of ∼1.7 to 4, while for the inhomogeneity parameter χ (the ratio of the logarithmic to linear mean) the range is from ∼0.65 to 0.8. The results demonstrate that the MODIS Level-3 dataset is suitable for studying various aspects of cloud inhomogeneity and may prove invaluable for validating future cloud schemes in large-scale models capable of predicting subgrid variability.

scholarly journals MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dongsheng Xiao ◽  
Brandon J. Forys ◽  
Matthieu P. Vanni ◽  
Timothy H. Murphy
Keyword(s):  
Machine Learning ◽  
Quantitative Analysis ◽  
Brain Activity ◽  
Spatial Scales ◽  
Brain Regions ◽  
Brain Atlas ◽  
Convolutional Network ◽  
Segmentation Approach ◽  
Automated Machine Learning ◽  
User Friendly

AbstractUnderstanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we developed an automated machine learning-based registration and segmentation approach for quantitative analysis of mouse mesoscale cortical images. A deep learning model identifies nine cortical landmarks using only a single raw fluorescent image. Another fully convolutional network was adapted to delimit brain boundaries. This anatomical alignment approach was extended by adding three functional alignment approaches that use sensory maps or spatial-temporal activity motifs. We present this methodology as MesoNet, a robust and user-friendly analysis pipeline using pre-trained models to segment brain regions as defined in the Allen Mouse Brain Atlas. This Python-based toolbox can also be combined with existing methods to facilitate high-throughput data analysis.

scholarly journals Activity flow underlying abnormalities in brain activations and cognition in schizophrenia

2021 ◽  
Vol 7 (29) ◽  
pp. eabf2513
Author(s):  
Luke J. Hearne ◽  
Ravi D. Mill ◽  
Brian P. Keane ◽  
Grega Repovš ◽  
Alan Anticevic ◽  
...  
Keyword(s):  
Cognitive Dysfunction ◽  
Large Scale ◽  
Brain Activity ◽  
Memory Task ◽  
Clinical Intervention ◽  
Brain Regions ◽  
Imaging Data ◽  
Flow Mapping ◽  
Flow Models ◽  
Evoked Activity

Cognitive dysfunction is a core feature of many brain disorders, including schizophrenia (SZ), and has been linked to aberrant brain activations. However, it is unclear how these activation abnormalities emerge. We propose that aberrant flow of brain activity across functional connectivity (FC) pathways leads to altered activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping, an approach that models the movement of task-related activity between brain regions as a function of FC. Using functional magnetic resonance imaging data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to cognitive dysfunction in SZ.

scholarly journals Long-range phase synchronization of high-frequency oscillations in human cortex

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
G. Arnulfo ◽  
S. H. Wang ◽  
V. Myrov ◽  
B. Toselli ◽  
J. Hirvonen ◽  
...  
Keyword(s):  
High Frequency ◽  
Large Scale ◽  
Phase Synchronization ◽  
Brain Activity ◽  
Brain Regions ◽  
Spike Timing ◽  
Human Cortex ◽  
Neuronal Communication ◽  
High Frequency Oscillations ◽  
Connectivity Patterns

Abstract Inter-areal synchronization of neuronal oscillations at frequencies below ~100 Hz is a pervasive feature of neuronal activity and is thought to regulate communication in neuronal circuits. In contrast, faster activities and oscillations have been considered to be largely local-circuit-level phenomena without large-scale synchronization between brain regions. We show, using human intracerebral recordings, that 100–400 Hz high-frequency oscillations (HFOs) may be synchronized between widely distributed brain regions. HFO synchronization expresses individual frequency peaks and exhibits reliable connectivity patterns that show stable community structuring. HFO synchronization is also characterized by a laminar profile opposite to that of lower frequencies. Importantly, HFO synchronization is both transiently enhanced and suppressed in separate frequency bands during a response-inhibition task. These findings show that HFO synchronization constitutes a functionally significant form of neuronal spike-timing relationships in brain activity and thus a mesoscopic indication of neuronal communication per se.

scholarly journals Mapping Pavlovian Conditioning Effects on the Brain: Blocking, Contiguity, and Excitatory Effects

2001 ◽  
Vol 86 (2) ◽  
pp. 809-823 ◽  
Author(s):  
Dirk Jones ◽  
F. Gonzalez-Lima
Keyword(s):  
Pavlovian Conditioning ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Blocking Effect ◽  
Anterior Cingulate ◽  
Caudate Putamen ◽  
Fdg Uptake ◽  
The Brain ◽  
Previous Learning

Pavlovian conditioning effects on the brain were investigated by mapping rat brain activity with fluorodeoxyglucose (FDG) autoradiography. The goal was to map the effects of the same tone after blocking or eliciting a conditioned emotional response (CER). In the tone-blocked group, previous learning about a light blocked a CER to the tone. In the tone-excitor group, the same pairings of tone with shock US resulted in a CER to the tone in the absence of previous learning about the light. A third group showed no CER after pseudorandom presentations of these stimuli. Brain systems involved in the various associative effects of Pavlovian conditioning were identified, and their functional significance was interpreted in light of previous FDG studies. Three conditioning effects were mapped: 1) blocking effects: FDG uptake was lower in medial prefrontal cortex and higher in spinal trigeminal and cuneate nuclei in the tone-blocked group relative to the tone-excitor group. 2) Contiguity effects: relative to pseudorandom controls, similar FDG uptake increases in the tone-blocked and -excitor groups were found in auditory regions (inferior colliculus and cortex), hippocampus (CA1), cerebellum, caudate putamen, and solitary nucleus. Contiguity effects may be due to tone-shock pairings common to the tone-blocked and -excitor groups rather than their different CER. And 3) excitatory effects: FDG uptake increases limited to the tone-excitor group occurred in a circuit linked to the CER, including insular and anterior cingulate cortex, vertical diagonal band nucleus, anterior hypothalamus, and caudoventral caudate putamen. This study provided the first large-scale map of brain regions underlying the Kamin blocking effect on conditioning. In particular, the results suggest that suppression of prefrontal activity and activation of unconditioned stimulus pathways are important neural substrates of the Kamin blocking effect.

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