scholarly journals Unraveling reproducible dynamic states of individual brain functional parcellation

2020 ◽  
pp. 1-28 ◽  
Author(s):  
Amal Boukhdhir ◽  
Yu Zhang ◽  
Max Mignotte ◽  
Pierre Bellec
Keyword(s):  
Functional Organization ◽  
Time Windows ◽  
Brain Networks ◽  
Anterior Cingulate ◽  
Fmri Data ◽  
Network Analyses ◽  
Stability Maps ◽  
The Rich ◽  
The Brain ◽  
Dynamic States

Data-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging. An important limitation of classical parcellations is that they are static, that is, they neglect dynamic reconfigurations of brain networks. In this paper, we proposed a new method to identify dynamic states of parcellations, which we hypothesized would improve reproducibility over static parcellation approaches. For a series of seed voxels in the brain, we applied a cluster analysis to regroup short (3 min) time windows into “states” with highly similar seed parcels. We splitted individual time series of the Midnight scan club sample into two independent sets of 2.5 hr (test and retest). We found that average within-state parcellations, called stability maps, were highly reproducible (over 0.9 test-retest spatial correlation in many instances) and subject specific (fingerprinting accuracy over 70% on average) between test and retest. Consistent with our hypothesis, seeds in heteromodal cortices (posterior and anterior cingulate) showed a richer repertoire of states than unimodal (visual) cortex. Taken together, our results indicate that static functional parcellations are incorrectly averaging well-defined and distinct dynamic states of brain parcellations. This work calls to revisit previous methods based on static parcellations, which includes the majority of published network analyses of fMRI data. Our method may, thus, impact how researchers model the rich interactions between brain networks in health and disease.

scholarly journals Unraveling reproducible dynamic states of individual brain functional parcellation

2020 ◽  
Author(s):  
Amal Boukhdhir ◽  
Yu Zhang ◽  
Max Mignotte ◽  
Pierre Bellec
Keyword(s):  
Functional Organization ◽  
Time Windows ◽  
Brain Networks ◽  
Anterior Cingulate ◽  
Fmri Data ◽  
Network Analyses ◽  
Stability Maps ◽  
The Rich ◽  
The Brain ◽  
Dynamic States

AbstractData-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging. An important limitation of classical parcellations is that they are static, i.e. they neglect dynamic reconfigurations of brain networks. In this paper, we proposed a new method to identify dynamic states of parcellations, which we hypothesized would improve reproducibility over static parcellation approaches. For a series of seed voxels in the brain, we applied a cluster analysis to regroup short (3 minutes) time windows into “states” with highly similar seed parcels. We splitted individual time series of the Midnight scan club sample into two independent sets of 2.5 hours (test and retest). We found that average within-state parcellations, called stability maps, were highly reproducible (over .9 test-retest spatial correlation in many instances) and subject specific (fingerprinting accuracy over 70% on average) between test and retest. Consistent with our hypothesis, seeds in heteromodal cortices (posterior and anterior cingulate) showed a richer repertoire of states than unimodal (visual) cortex. Taken together, our results indicate that static functional parcellations are incorrectly averaging well-defined and distinct dynamic states of brain parcellations. This work calls to revisit previous methods based on static parcellations, which includes the majority of published network analyses of fMRI data. Our method may, thus, impact how researchers model the rich interactions between brain networks in health and disease.

scholarly journals The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Camille Fauchon ◽  
David Meunier ◽  
Isabelle Faillenot ◽  
Florence B Pomares ◽  
Hélène Bastuji ◽  
...  
Keyword(s):  
Information Integration ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Modular Organization ◽  
Noxious Heat ◽  
Intracranial Eeg ◽  
Brain Connectome ◽  
Posterior Parietal ◽  
The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

scholarly journals Brown-Séquard: On neural networks and brain localization of functions

2014 ◽  
Vol 8 (1) ◽  
pp. 79-82
Author(s):  
Eliasz Engelhardt
Keyword(s):  
Functional Organization ◽  
Body Of Knowledge ◽  
Current State ◽  
Brain Localization ◽  
Physical Functions ◽  
Action At A Distance ◽  
The Rich ◽  
The 19Th Century ◽  
The Brain ◽  
Neuroimaging Techniques

ABSTRACT The notion that the brain (encephalon) is a network of interconnected neurons has a long and memorable history. Cytoarchitectonic and hodological studies coupled with advanced neuroimaging techniques have produced a substantial body of knowledge on structural and functional organization. Acquiring the rich knowledge held today took a long and winding journey. Important advancements were made in the 19th century, with the remarkable Brown-Séquard figuring as one of the protagonists. Regarding the brain, he proposed nine mental and physical functions (organs) related to distributed cell clusters, interconnected according to their roles, the "network of anastomosing cells", dynamically submitted to "dynamogenic and inhibitory activities", and "action at a distance" concepts, the latter also related to his notion of "recovery". It is remarkable that someone was able to propose, ahead of his time, and with the limited technical resources available, such significant concepts that paved the way for the current state of knowledge.

scholarly journals Effector-independent brain network for auditory-motor integration: fMRI evidence from singing and cello playing

2020 ◽  
Author(s):  
Melanie Segado ◽  
Robert J. Zatorre ◽  
Virginia B. Penhune
Keyword(s):  
Motor Control ◽  
Auditory Feedback ◽  
Brain Activity ◽  
Brain Networks ◽  
Anterior Cingulate ◽  
List Type ◽  
Control Mechanisms ◽  
Monitoring And Control ◽  
Pitch Regulation ◽  
The Brain

AbstractMany everyday tasks share high-level sensory goals but differ in the movements used to accomplish them. One example of this is musical pitch regulation, where the same notes can be produced using the vocal system or a musical instrument controlled by the hands. Cello playing has previously been shown to rely on brain structures within the singing network for performance of single notes, except in areas related to primary motor control, suggesting that the brain networks for auditory feedback processing and sensorimotor integration may be shared (Segado et al. 2018). However, research has shown that singers and cellists alike can continue singing/playing in tune even in the absence of auditory feedback (Chen et al. 2013, Kleber et al. 2013), so different paradigms are required to test feedback monitoring and control mechanisms. In singing, auditory pitch feedback perturbation paradigms have been used to show that singers engage a network of brain regions including anterior cingulate cortex (ACC), anterior insula (aINS), and intraparietal sulcus (IPS) when compensating for incorrect pitch feedback, and posterior superior temporal gyrus (pSTG) and supramarginal gyrus (SMG) when ignoring it (Zarate et al. 2005, 2008). To determine whether the brain networks for cello playing and singing directly overlap in these sensory-motor integration areas, in the present study expert cellists were asked to compensate for or ignore introduced pitch perturbations when singing/playing during fMRI scanning. We found that cellists were able to sing/play target tones, and compensate for and ignore introduced feedback perturbations equally well. Brain activity overlapped for singing and playing in IPS and SMG when compensating, and pSTG and dPMC when ignoring; differences between singing/playing across all three conditions were most prominent in M1, centered on the relevant motor effectors (hand, larynx). These findings support the hypothesis that pitch regulation during cello playing relies on structures within the singing network and suggests that differences arise primarily at the level of forward motor control.HighlightsExpert cellists were asked to compensate for or ignore introduced pitch perturbations when singing/playing during fMRI scanning.Cellists were able to sing/play target tones, and compensate for and ignore introduced feedback perturbations equally well.Brain activity overlapped for singing and playing in IPS and SMG when compensating, and pSTG and dPMC when ignoring.Differences between singing/playing across were most prominent in M1, centered around the relevant motor effectors (hand, larynx)Findings support the hypothesis that pitch regulation during cello playing relies on structures within the singing network with differences arising primarily at the level of forward motor control

The Dynamics of Speech Motor Control Revealed with Time-Resolved fMRI

2019 ◽  
Vol 30 (1) ◽  
pp. 241-255 ◽  
Author(s):  
Niels Janssen ◽  
Cristian Camilo Rincón Mendieta
Keyword(s):  
Motor Control ◽  
Speech Production ◽  
Temporal Dynamics ◽  
Motor Behavior ◽  
Inferior Frontal Gyrus ◽  
Brain Networks ◽  
Fmri Data ◽  
Speech Motor Control ◽  
The Brain ◽  
Speech Motor

Abstract Holding a conversation means that speech must be started, maintained, and stopped continuously. The brain networks that underlie these aspects of speech motor control remain poorly understood. Here we collected functional magnetic resonance imaging (fMRI) data while participants produced normal and fast rate speech in response to sequences of visually presented objects. We took a non-conventional approach to fMRI data analysis that allowed us to study speech motor behavior as it unfolded over time. To this end, whole-brain fMRI signals were extracted in stimulus-locked epochs using slice-based fMRI. These data were then subjected to group independent component analysis to discover spatially independent networks that were associated with different temporal activation profiles. The results revealed two basic brain networks with different temporal dynamics: a cortical network that was activated continuously during speech production, and a second cortico-subcortical network that increased in activity during the initiation and suppression of speech production. Additional analyses explored whether key areas involved in motor suppression such as the right inferior frontal gyrus, sub-thalamic nucleus and pre-supplementary motor area provide first-order signals to stop speech. The results reveal for the first time the brain networks associated with the initiation, maintenance, and suppression of speech motor behavior.

scholarly journals Functional parcellation of human brain using localized topo-connectivity mapping

2021 ◽  
Author(s):  
Yu Zhao ◽  
Yurui Gao ◽  
Muwei Li ◽  
Adam W. Anderson ◽  
Zhaohua Ding ◽  
...  
Keyword(s):  
Human Brain ◽  
Functional Organization ◽  
Statistical Tests ◽  
Simulated Data ◽  
Fmri Data ◽  
Anatomical Structures ◽  
Connectivity Mapping ◽  
Subcortical Region ◽  
Human Connectome Project ◽  
The Brain

<p>The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, there has been less progress in the derivation of functional structures from voxel-wise analyses at finer scales. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain using resting-state fMRI data. Here we describe its mathematical background and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data as part of the Human Connectome Project to generate group-average LTM images. Functional structures revealed by this approach agree moderately well with anatomical structures identified by T<sub>1</sub>-weighted images and fractional anisotropy maps derived from diffusion MRI. Moreover, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-informed parcellations are significantly larger than those of random parcellations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.</p>

scholarly journals Functional reorganization of brain networks across the human menstrual cycle

2019 ◽  
Author(s):  
Laura Pritschet ◽  
Tyler Santander ◽  
Caitlin M. Taylor ◽  
Evan Layher ◽  
Shuying Yu ◽  
...  
Keyword(s):  
Menstrual Cycle ◽  
Human Brain ◽  
Estrogen Receptors ◽  
Sex Hormones ◽  
Functional Organization ◽  
Brain Networks ◽  
List Type ◽  
Functional Architecture ◽  
Human Menstrual Cycle ◽  
The Brain

AbstractThe brain is an endocrine organ, sensitive to the rhythmic changes in sex hormone production that occurs in most mammalian species. In rodents and nonhuman primates, estrogen and progesterone’s impact on the brain is evident across a range of spatiotemporal scales. Yet, the influence of sex hormones on the functional architecture of the human brain is largely unknown. In this dense-sampling, deep phenotyping study, we examine the extent to which endogenous fluctuations in sex hormones alter intrinsic brain networks at rest in a woman who underwent brain imaging and venipuncture for 30 consecutive days. Standardized regression analyses illustrate estrogen and progesterone’s widespread associations with functional connectivity. Time-lagged analyses examined the temporal directionality of these relationships and suggest that cortical network dynamics (particularly in the Default Mode and Dorsal Attention Networks, whose hubs are densely populated with estrogen receptors) are preceded—and perhaps driven—by hormonal fluctuations. A similar pattern of associations was observed in a follow-up study one year later. Together, these results reveal the rhythmic nature in which brain networks reorganize across the human menstrual cycle. Neuroimaging studies that densely sample the individual connectome have begun to transform our understanding of the brain’s functional organization. As these results indicate, taking endocrine factors into account is critical for fully understanding the intrinsic dynamics of the human brain.HighlightsIntrinsic fluctuations in sex hormones shape the brain’s functional architecture.Estradiol facilitates tighter coherence within whole-brain functional networks.Progesterone has the opposite, reductive effect.Ovulation (via estradiol) modulates variation in topological network states.Effects are pronounced in network hubs densely populated with estrogen receptors.

scholarly journals How tasks change whole-brain functional organization to reveal brain-phenotype relationships

2019 ◽  
Author(s):  
Abigail S. Greene ◽  
Siyuan Gao ◽  
Stephanie Noble ◽  
Dustin Scheinost ◽  
R. Todd Constable
Keyword(s):  
Predictive Modeling ◽  
Functional Organization ◽  
Prediction Performance ◽  
Fmri Data ◽  
Bold Signal ◽  
Psychophysiological Interaction ◽  
Induced Changes ◽  
Complex Human Traits ◽  
The Brain ◽  
Task Activation

AbstractFunctional connectivity (FC) calculated from task fMRI data better reveals brain-phenotype relationships than rest-based FC, but how tasks have this effect is unknown. In over 700 individuals performing 7 tasks, we use psychophysiological interaction (PPI) and predictive modeling analyses to demonstrate that task-induced changes in FC successfully predict phenotype, and these changes are not simply driven by task activation. Activation, however, is useful for prediction only if the in-scanner task is related to the predicted phenotype. Given this evidence that tasks change patterns of FC independent of activation to amplify brain-phenotype relationships, we develop and apply an inter-subject PPI analysis to further characterize these predictive FC changes. We find that task-induced consistency of FC patterns across individuals is useful for prediction—to a point; these results suggest that tasks improve FC-based prediction performance by de-noising the BOLD signal, revealing meaningful individual differences in brain functional organization. Together, these findings demonstrate that, when it comes to the effects of in-scanner tasks on the brain, focal activation is only the tip of the iceberg, and they offer a framework to best leverage both task activation and FC to reveal the neural bases of complex human traits, symptoms, and behaviors.

scholarly journals Functional parcellation of human brain using localized topo-connectivity mapping

2021 ◽  
Author(s):  
Yu Zhao ◽  
Yurui Gao ◽  
Muwei Li ◽  
Adam W. Anderson ◽  
Zhaohua Ding ◽  
...  
Keyword(s):  
Human Brain ◽  
Functional Organization ◽  
Statistical Tests ◽  
Simulated Data ◽  
Fmri Data ◽  
Anatomical Structures ◽  
Connectivity Mapping ◽  
Subcortical Region ◽  
Human Connectome Project ◽  
The Brain

<p>The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, there has been less progress in the derivation of functional structures from voxel-wise analyses at finer scales. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain using resting-state fMRI data. Here we describe its mathematical background and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data as part of the Human Connectome Project to generate group-average LTM images. Functional structures revealed by this approach agree moderately well with anatomical structures identified by T<sub>1</sub>-weighted images and fractional anisotropy maps derived from diffusion MRI. Moreover, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-informed parcellations are significantly larger than those of random parcellations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.</p>

scholarly journals Disconnectomics of the Rich Club Impacts Motor Recovery After Stroke

Stroke ◽  
2021 ◽  
Vol 52 (6) ◽  
pp. 2115-2124
Author(s):  
Philip Egger ◽  
Giorgia G. Evangelista ◽  
Philipp J. Koch ◽  
Chang-Hyun Park ◽  
Laura Levin-Gleba ◽  
...  
Keyword(s):  
Brain Networks ◽  
Small Lesion ◽  
Lesion Volume ◽  
Good Recovery ◽  
Imaging Data ◽  
Brain Organization ◽  
Rich Club ◽  
Network Analyses ◽  
Poor Recovery ◽  
The Rich

Background and Purpose: Structural brain networks possess a few hubs, which are not only highly connected to the rest of the brain but are also highly connected to each other. These hubs, which form a rich-club, play a central role in global brain organization. To investigate whether the concept of rich-club sheds new light on poststroke recovery, we applied a novel network-theoretical quantification of lesions to patients with stroke and compared the outcomes with what lesion size alone would indicate. Methods: Whole-brain structural networks of 73 patients with ischemic stroke were reconstructed using diffusion-weighted imaging data. Disconnectomes, a new type of network analyses, were constructed using only those fibers that pass through the lesion. Fugl-Meyer upper extremity scores and their changes were used to determine whether the patients show natural recovery or not. Results: Cluster analysis revealed 3 patient clusters: small-lesion-good-recovery, midsized-lesion-poor-recovery (MLPR), and large-lesion-poor-recovery (LLPR). The small-lesion-good-recovery consisted of subjects whose lesions were small, and whose prospects for recovery were relatively good. To explain the nondifference in recovery between the MLPR and LLPR clusters despite the difference (LLPR>MLPR) in lesion volume, we defined the metric to be the sum of the entries in the disconnectome and, more importantly, the to be the sum of all entries in the disconnectome corresponding to edges with at least one node in the rich-club. Unlike lesion volume and corticospinal tract damage (MLPR<LLPR), for , this relationship was reversed (MLPR>LLPR) or showed no difference for . Conclusions: Smaller lesions that focus on the rich-club can be just as devastating as much larger lesions that do not focus on the rich-club, pointing to the role of the rich-club as a backbone for functional communication within brain networks and for recovery from stroke.

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