scholarly journals A question of scale

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Muireann Irish ◽  
Siddharth Ramanan
Keyword(s):  
Spatial Scales ◽  
Brain Regions ◽  
Distance Information ◽  
Fmri Experiment

An fMRI experiment reveals distinct brain regions that respond in a graded manner as humans process distance information across increasing spatial scales.

scholarly journals Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation

2021 ◽  
Vol 17 (12) ◽  
pp. e1009681
Author(s):  
Michiel W. H. Remme ◽  
Urs Bergmann ◽  
Denis Alevi ◽  
Susanne Schreiber ◽  
Henning Sprekeler ◽  
...  
Keyword(s):  
Memory Consolidation ◽  
Computational Models ◽  
Hippocampal Formation ◽  
Spatial Scales ◽  
Single Cells ◽  
Brain Regions ◽  
Quantitative Agreement ◽  
Long Term Memory ◽  
Hebbian Plasticity ◽  
Mathematical Analyses

Systems memory consolidation involves the transfer of memories across brain regions and the transformation of memory content. For example, declarative memories that transiently depend on the hippocampal formation are transformed into long-term memory traces in neocortical networks, and procedural memories are transformed within cortico-striatal networks. These consolidation processes are thought to rely on replay and repetition of recently acquired memories, but the cellular and network mechanisms that mediate the changes of memories are poorly understood. Here, we suggest that systems memory consolidation could arise from Hebbian plasticity in networks with parallel synaptic pathways—two ubiquitous features of neural circuits in the brain. We explore this hypothesis in the context of hippocampus-dependent memories. Using computational models and mathematical analyses, we illustrate how memories are transferred across circuits and discuss why their representations could change. The analyses suggest that Hebbian plasticity mediates consolidation by transferring a linear approximation of a previously acquired memory into a parallel pathway. Our modelling results are further in quantitative agreement with lesion studies in rodents. Moreover, a hierarchical iteration of the mechanism yields power-law forgetting—as observed in psychophysical studies in humans. The predicted circuit mechanism thus bridges spatial scales from single cells to cortical areas and time scales from milliseconds to years.

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 The functional organization of human epileptic hippocampus

2016 ◽  
Vol 115 (6) ◽  
pp. 3140-3145 ◽  
Author(s):  
Petr Klimes ◽  
Juliano J. Duque ◽  
Ben Brinkmann ◽  
Jamie Van Gompel ◽  
Matt Stead ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Local Field ◽  
Medial Temporal Lobe ◽  
Slow Wave Sleep ◽  
Functional Organization ◽  
Spectral Power ◽  
Spatial Scales ◽  
Field Potential ◽  
Brain Regions ◽  
Behavioral State

The function and connectivity of human brain is disrupted in epilepsy. We previously reported that the region of epileptic brain generating focal seizures, i.e., the seizure onset zone (SOZ), is functionally isolated from surrounding brain regions in focal neocortical epilepsy. The modulatory effect of behavioral state on the spatial and spectral scales over which the reduced functional connectivity occurs, however, is unclear. Here we use simultaneous sleep staging from scalp EEG with intracranial EEG recordings from medial temporal lobe to investigate how behavioral state modulates the spatial and spectral scales of local field potential synchrony in focal epileptic hippocampus. The local field spectral power and linear correlation between adjacent electrodes provide measures of neuronal population synchrony at different spatial scales, ∼1 and 10 mm, respectively. Our results show increased connectivity inside the SOZ and low connectivity between electrodes in SOZ and outside the SOZ. During slow-wave sleep, we observed decreased connectivity for ripple and fast ripple frequency bands within the SOZ at the 10 mm spatial scale, while the local synchrony remained high at the 1 mm spatial scale. Further study of these phenomena may prove useful for SOZ localization and help understand seizure generation, and the functional deficits seen in epileptic eloquent cortex.

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 Computational Methods in Social Neuroscience: Recent Advances, New Tools, and Future Directions

2021 ◽  
Author(s):  
Carolyn Parkinson
Keyword(s):  
Computational Methods ◽  
Spatial Scales ◽  
Brain Regions ◽  
Neural Response ◽  
Response Patterns ◽  
Social Environments ◽  
Social Worlds ◽  
Recent Advances ◽  
Social Decisions ◽  
And Behavior

Abstract Recent years have seen a surge of exciting developments in the computational tools available to social neuroscientists. This paper highlights and synthesizes recent advances that have been enabled by the application of such tools, as well as methodological innovations likely to be of interest and utility to social neuroscientists, but that have been concentrated in other sub-fields. Papers in this special issue are emphasized, many of which contain instructive materials (e.g., tutorials, code) for researchers new to the highlighted methods. These include approaches for modeling social decisions, characterizing multivariate neural response patterns at varying spatial scales, using decoded neurofeedback to draw causal links between specific neural response patterns and psychological and behavioral phenomena, examining time-varying patterns of connectivity between brain regions, and characterizing the social networks in which social thought and behavior unfold in everyday life. By combining computational methods for characterizing participants’ rich social environments – at the levels of stimuli, paradigms, and the webs of social relationships that surround people – with those for capturing the psychological processes that undergird social behavior and the wealth of information contained in neuroimaging datasets, social neuroscientists can gain new insights into how people create, understand, and navigate their complex social worlds.

scholarly journals Modules in connectomes of phase-synchronization comprise anatomically contiguous, spatially related regions

2021 ◽  
Author(s):  
N. Williams ◽  
S. H. Wang ◽  
G. Arnulfo ◽  
L. Nobili ◽  
S. Palva ◽  
...  
Keyword(s):  
Phase Synchronization ◽  
Spatial Scales ◽  
Brain Regions ◽  
Detection Methods ◽  
Modular Organization ◽  
Consensus Clustering ◽  
Volume Conduction ◽  
Functional Connections ◽  
Gamma Frequency ◽  
Cortical Regions

Modules in brain connectomes are essential to balancing the functional segregation and integration crucial to brain operation. Connectomes are the set of structural or functional connections between each pair of brain regions. Non-invasive methodologies, Electroencephalography (EEG) and Magnetoencephalography (MEG), have been used to identify modules in connectomes of phase-synchronization, but have been compromised by spurious phase-synchronization due to EEG volume conduction or MEG field spread. In this study, we used invasive, intracerebral recordings with stereo-electroencephalography (SEEG, N = 67), to identify modules in connectomes of phase-synchronization. To do this, we used submillimetre localization of SEEG contacts and closest-white-matter referencing, to generate group-level connectomes of phase-synchronization minimally affected by volume conduction. Then, we employed community detection methods together with a novel consensus clustering approach, to identify modules in connectomes of phase-synchronization. The connectomes of phase-synchronization possessed significant modular organization at multiple spatial scales, from 3-320 Hz. These identified modules were highly similar within neurophysiologically meaningful frequency bands. Modules up to the high-gamma frequency band comprised only anatomically contiguous regions, unlike modules identified with functional Magnetic Resonance Imaging (fMRI). Strikingly, the identified modules comprised cortical regions involved in shared repertoires of cognitive functions including vision, language and attention. These results demonstrate the viability of combining SEEG with advanced methods, to identify modules in connectomes of phase-synchronization. The modules correspond to brain systems with specific functional roles in perceptual, cognitive, and motor processing.

scholarly journals The Menstrual Cycle Modulates Whole-Brain Turbulent Dynamics

2021 ◽  
Vol 15 ◽  
Author(s):  
Eleonora De Filippi ◽  
Carme Uribe ◽  
Daniela S. Avila-Varela ◽  
Noelia Martínez-Molina ◽  
Venera Gashaj ◽  
...  
Keyword(s):  
Information Processing ◽  
Menstrual Cycle ◽  
Spatial Scales ◽  
Hormonal Contraceptive ◽  
Brain Regions ◽  
Whole Brain ◽  
Attention Networks ◽  
Dynamic Framework ◽  
Two Phases ◽  
Contraceptive Study

Brain dynamics have recently been shown to be modulated by rhythmic changes in female sex hormone concentrations across an entire menstrual cycle. However, many questions remain regarding the specific differences in information processing across spacetime between the two main follicular and luteal phases in the menstrual cycle. Using a novel turbulent dynamic framework, we studied whole-brain information processing across spacetime scales (i.e., across long and short distances in the brain) in two open-source, dense-sampled resting-state datasets. A healthy naturally cycling woman in her early twenties was scanned over 30 consecutive days during a naturally occurring menstrual cycle and under a hormonal contraceptive regime. Our results indicated that the luteal phase is characterized by significantly higher information transmission across spatial scales than the follicular phase. Furthermore, we found significant differences in turbulence levels between the two phases in brain regions belonging to the default mode, salience/ventral attention, somatomotor, control, and dorsal attention networks. Finally, we found that changes in estradiol and progesterone concentrations modulate whole-brain turbulent dynamics in long distances. In contrast, we reported no significant differences in information processing measures between the active and placebo phases in the hormonal contraceptive study. Overall, the results demonstrate that the turbulence framework is able to capture differences in whole-brain turbulent dynamics related to ovarian hormones and menstrual cycle stages.

scholarly journals A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics

2021 ◽  
Author(s):  
Jennifer S Goldman ◽  
Lionel Kusch ◽  
Bahar Hazal Yalcinkaya ◽  
Damien Depannemaecker ◽  
Trang-Anh Estelle Nghiem ◽  
...  
Keyword(s):  
Open Access ◽  
Human Brain ◽  
Slow Wave Sleep ◽  
Spatial Scales ◽  
Mean Field ◽  
Brain Regions ◽  
Inhibitory Neurons ◽  
Neural Dynamics ◽  
Healthy Human ◽  
Brain States

Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirical spontaneous and stimulus-evoked dynamics in the space, time, phase, and frequency domains. Remarkably, the model also reproduces brain-wide enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.

scholarly journals Revealing architectural order with quantitative label-free imaging and deep learning

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Syuan-Ming Guo ◽  
Li-Hao Yeh ◽  
Jenny Folkesson ◽  
Ivan E Ivanov ◽  
Anitha P Krishnan ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Brain Regions ◽  
Three Dimensions ◽  
Live Cells ◽  
Label Free ◽  
Computationally Efficient ◽  
Human Brain Tissue ◽  
Tissue Slices ◽  
Efficient Prediction ◽  
Fluorescence Images

We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue.

Abstract 25: Periinfarct Rewiring Supports Recovery After Primary Motor Cortex Stroke

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Mitsouko van Assche ◽  
Elisabeth Dirren ◽  
Alexia Bourgeois ◽  
Andreas Kleinschmidt ◽  
Jonas Richiardi ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Spatial Scales ◽  
Premotor Cortex ◽  
Brain Regions ◽  
Relative Increase ◽  
Motor Recovery ◽  
The Core ◽  
Hand Dexterity ◽  
Motor Network

Background and Purpose: After stroke restricted to the primary motor cortex (M1), it is uncertain whether network reorganization associated with motor recovery involves the periinfarct or more remote brain regions. In humans, the challenge is to recruit patients with similar lesions in size and location. Methods: We studied 16 patients with focal M1 stroke and hand paresis. Motor function and resting-state MRI functional connectivity (FC) were studied at three time points: acute (<10 days), early subacute (3 weeks), and late subacute (3 months). FC correlates of motor recovery were investigated at three spatial scales, i) ipsilesional non-infarcted M1, ii) core motor network (including M1, premotor cortex (PMC), supplementary motor area (SMA), and primary somatosensory cortex), and iii) extended motor network including all regions structurally connected to the upper limb representation of M1. Results: Hand dexterity was impaired only in the acute phase ( P =0.036). At a small spatial scale, improved dexterity was associated with increased FC involving mainly the ipsilesional non-infarcted M1 and contralesional motor regions (cM1: rho=0.732; P =0.004; cPMC: rho=0.837, P <0.001; cSMA: rho=0.736; P =0.004). At a larger scale, motor recovery correlated with the relative increase in total FC strength in the core motor network compared to the extended motor network (rho=0.71; P =0.006). Conclusions: FC changes associated with motor improvement involve the perilesional M1 and do not extend beyond the core motor network. The ipsilesional non-infarcted M1 and core motor regions could hence be primary targets for future restorative therapies.

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