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 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 Decoding task-specific cognitive states with slow, directed functional networks in the human brain

2019 ◽  
Author(s):  
Devarajan Sridharan ◽  
Shagun Ajmera ◽  
Hritik Jain ◽  
Mali Sundaresan
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Human Brain ◽  
Brain Networks ◽  
Fmri Data ◽  
Invasive Technique ◽  
Functional Interactions ◽  
Cognitive States ◽  
Human Connectome Project ◽  
The Brain

AbstractFlexible functional interactions among brain regions mediate critical cognitive functions. Such interactions can be measured from functional magnetic resonance imaging (fMRI) data with either instantaneous (zero-lag) or lag-based (time-lagged) functional connectivity; only the latter approach permits inferring directed functional interactions. Yet, the fMRI hemodynamic response is slow, and sampled at a timescale (seconds) several orders of magnitude slower than the underlying neural dynamics (milliseconds). It is, therefore, widely held that lag-based fMRI functional connectivity, measured with approaches like as Granger-Geweke causality (GC), provides spurious and unreliable estimates of underlying neural interactions. Experimental verification of this claim has proven challenging because neural ground truth connectivity is often unavailable concurrently with fMRI recordings. We address this challenge by combining machine learning with GC functional connectivity estimation. We estimated instantaneous and lag-based GC functional connectivity networks using fMRI data from 1000 participants, drawn from the Human Connectome Project database. A linear classifier, trained on either instantaneous or lag-based GC, reliably discriminated among seven different task and resting brain states, with over 80% cross-validation accuracy. With network simulations, we demonstrate that instantaneous and lag-based GC exploited interactions at fast and slow timescales, respectively, to achieve robust classification. With human fMRI data, instantaneous and lag-based GC identified distinct, cognitive core networks. Finally, variations in GC connectivity explained inter-individual variations in a variety of cognitive scores. Our findings show that instantaneous and lag-based methods reveal complementary aspects of functional connectivity in the brain, and suggest that slow, directed functional interactions, estimated with fMRI, provide robust markers of behaviorally relevant cognitive states.Author SummaryFunctional MRI (fMRI) is a leading, non-invasive technique for mapping networks in the human brain. Yet, fMRI signals are noisy and sluggish, and fMRI scans are acquired at a timescale of seconds, considerably slower than the timescale of neural spiking (milliseconds). Can fMRI, then, be used to infer dynamic processes in the brain such as the direction of information flow among brain networks? We sought to answer this question by applying machine learning to fMRI scans acquired from 1000 participants in the Human Connectome Project (HCP) database. We show that directed brain networks, estimated with a technique known as Granger-Geweke Causality (GC), accurately predicts individual subjects’ task-specific cognitive states inside the scanner, and also explains variations in a variety of behavioral scores across individuals. We propose that directed functional connectivity, as estimated with fMRI-GC, is relevant for understanding human cognitive function.

scholarly journals Predictability changes what we remember in familiar temporal contexts

2018 ◽  
Author(s):  
Hyojeong Kim ◽  
Margaret L. Schlichting ◽  
Alison R. Preston ◽  
Jarrod A. Lewis-Peacock
Keyword(s):  
Human Brain ◽  
Pattern Classification ◽  
Prediction Error ◽  
Fmri Data ◽  
The Past ◽  
The Future ◽  
Abstract Level ◽  
Episodic Memories ◽  
The Brain

AbstractThe human brain constantly anticipates the future based on memories of the past. Encountering a familiar situation reactivates memory of previous encounters which can trigger a prediction of what comes next to facilitate responsiveness. However, a prediction error can lead to pruning of the offending memory, a process that weakens its representation in the brain and leads to forgetting. Our goal in this study was to evaluate whether memories are spared from pruning in situations that allow for more abstract yet reliable predictions. We hypothesized that when the category, but not the identity, of a new stimulus can be anticipated, this will reduce pruning of existing memories and also reduce encoding of the specifics of new memories. Participants viewed a sequence of objects, some of which reappeared multiple times (“cues”), followed always by novel items. Half of the cues were followed by new items from different (unpredictable) categories, while others were followed by new items from a single (predictable) category. Pattern classification of fMRI data was used to identify category-specific predictions after each cue. Pruning was observed only in unpredictable contexts, while encoding of new items suffered more in predictable contexts. These findings demonstrate that how episodic memories are updated is influenced by the reliability of abstract-level predictions in familiar contexts.

scholarly journals Predictability Changes What We Remember in Familiar Temporal Contexts

2020 ◽  
Vol 32 (1) ◽  
pp. 124-140 ◽  
Author(s):  
Hyojeong Kim ◽  
Margaret L. Schlichting ◽  
Alison R. Preston ◽  
Jarrod A. Lewis-Peacock
Keyword(s):  
Human Brain ◽  
Prediction Error ◽  
Fmri Data ◽  
Prediction Errors ◽  
Associative Memories ◽  
The Past ◽  
Item Level ◽  
Abstract Level ◽  
The Brain

The human brain constantly anticipates the future based on memories of the past. Encountering a familiar situation reactivates memory of previous encounters, which can trigger a prediction of what comes next to facilitate responsiveness. However, a prediction error can lead to pruning of the offending memory, a process that weakens its representation in the brain and leads to forgetting. Our goal in this study was to evaluate whether memories are spared from such pruning in situations that allow for accurate predictions at the categorical level, despite prediction errors at the item level. Participants viewed a sequence of objects, some of which reappeared multiple times (“cues”), followed always by novel items. Half of the cues were followed by new items from different (unpredictable) categories, while others were followed by new items from a single (predictable) category. Pattern classification of fMRI data was used to identify category-specific predictions after each cue. Pruning was observed only in unpredictable contexts, while encoding of new items was less robust in predictable contexts. These findings demonstrate that how associative memories are updated is influenced by the reliability of abstract-level predictions in familiar contexts.

scholarly journals Design of Deep Learning Model for Task-Evoked fMRI Data Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaojie Huang ◽  
Jun Xiao ◽  
Chao Wu
Keyword(s):  
Recurrent Network ◽  
Fmri Data ◽  
Network Module ◽  
Temporal Features ◽  
Human Connectome Project ◽  
Brain States ◽  
Sequential Information ◽  
The Moment ◽  
Deep Learning Model ◽  
The Brain

Machine learning methods have been successfully applied to neuroimaging signals, one of which is to decode specific task states from functional magnetic resonance imaging (fMRI) data. In this paper, we propose a model that simultaneously utilizes characteristics of both spatial and temporal sequential information of fMRI data with deep neural networks to classify the fMRI task states. We designed a convolution network module and a recurrent network module to extract the spatial and temporal features of fMRI data, respectively. In particular, we also add the attention mechanism to the recurrent network module, which more effectively highlights the brain activation state at the moment of reaction. We evaluated the model using task-evoked fMRI data from the Human Connectome Project (HCP) dataset, the classification accuracy got 94.31%, and the experimental results have shown that the model can effectively distinguish the brain states under different task stimuli.

scholarly journals A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome

2020 ◽  
Author(s):  
Kwangsun Yoo ◽  
Monica D. Rosenberg ◽  
Young Hye Kwon ◽  
Dustin Scheinost ◽  
Robert T Constable ◽  
...  
Keyword(s):  
Functional Organization ◽  
Transformation Model ◽  
Cognitive State ◽  
Imaging Data ◽  
State Transformation ◽  
Cognitive States ◽  
Brain Connectome ◽  
Human Connectome Project ◽  
Task Goals ◽  
The Brain

The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model changes in the brain's functional organization. Here, we present a novel connectome-to-connectome (C2C) state transformation framework that enables us to model the brain's functional reorganization in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-specific connectomes from their task-free connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions. Finally, the C2C model reveals how the connectome reorganizes between cognitive states. Previous studies have reported that task-induced modulation of the brain connectome is domain-specific as well as domain-general, but did not specify how brain systems reconfigure to specific cognitive states. Our observations support the existence of reliable state-specific systems in the brain and indicate that we can quantitatively describe patterns of brain reorganization, common across individuals, in a computational model.

scholarly journals Robust Brain State Decoding using Bidirectional Long Short Term Memory Networks in functional MRI

2021 ◽  
Author(s):  
Anant Mittal ◽  
Priya Aggarwal ◽  
Luiz Pessoa ◽  
Anubha Gupta
Keyword(s):  
Short Term Memory ◽  
Fmri Data ◽  
Brain State ◽  
Short Term ◽  
Term Memory ◽  
Feature Representations ◽  
Human Connectome Project ◽  
Brain States ◽  
Long Short Term Memory ◽  
The Brain

Decoding brain states of the underlying cognitive processes via learning discriminative feature representations has recently gained a lot of interest in brain imaging studies. Particularly, there has been an impetus to encode the dynamics of brain functioning by analyzing temporal information avail- able in the fMRI data. Long short term memory (LSTM), a class of machine learning model possessing a "memory" component, is increasingly being observed to perform well in various applications with dynamic temporal behavior, including brain state decoding. Because of the dynamics and inherent latency in fMRI BOLD responses, future temporal context is crucial. However, it is neither encoded nor captured by the conventional LSTM model. This paper performs robust brain state decoding via information encapsulation from both the past and future instances of fMRI data via bidirectional LSTM. This allows for explicitly modeling the dynamics of BOLD response without any delay adjustment. The two hidden activations of forward and reverse directions in bi-LSTM are collated to build the "memory" of the model and are used to robustly predict the brain states at every time instance. Working memory data from the Human Connectome Project (HCP) is utilized for validation and was observed to perform 18 percent better than it's unidirectional counterpart in terms of accuracy in predicting the brain states.

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 Brain/MINDS Beyond Human Brain MRI Project: A Protocol for Multi-Site Harmonization across Brain Disorders Throughout the Lifespan

2020 ◽  
Author(s):  
Shinsuke Koike ◽  
Saori C Tanaka ◽  
Tomohisa Okada ◽  
Toshihiko Aso ◽  
Michiko Asano ◽  
...  
Keyword(s):  
Human Brain ◽  
Neurological Disorders ◽  
Mixed Model ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Clinical Information ◽  
Imaging Biomarkers ◽  
Brain Disorders ◽  
Human Connectome Project ◽  
The Brain

AbstractPsychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (FY2018 ∼ FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. The Harmonization protocol (HARP) was established for five high-quality 3T scanners to obtain multimodal brain images including T1 and T2-weighted, resting state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, providing high reproducibility and sensitivity to subject-specific connectome. A total of 75 TS, as well as patients with various psychiatric and neurological disorders, are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a rigorous, prospective harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.

scholarly journals An integrative understanding of comparative cognition: lessons from human brain evolution

2020 ◽  
Vol 60 (4) ◽  
pp. 991-1006 ◽  
Author(s):  
Yuxiang Liu ◽  
Genevieve Konopka
Keyword(s):  
Human Brain ◽  
Dna Sequences ◽  
Animal Cognition ◽  
Comparative Cognition ◽  
Brain Evolution ◽  
Model Organisms ◽  
Anatomical Structures ◽  
Human Brain Evolution ◽  
Functional Understanding ◽  
The Brain

Abstract A comprehensive understanding of animal cognition requires the integration of studies on behavior, electrophysiology, neuroanatomy, development, and genomics. Although studies of comparative cognition are receiving increasing attention from organismal biologists, most current studies focus on the comparison of behaviors and anatomical structures to understand their adaptative values. However, to understand the most potentially complex cognitive program of the human brain a greater synthesis of a multitude of disciplines is needed. In this review, we start with extensive neuroanatomic comparisons between humans and other primates. One likely specialization of the human brain is the expansion of neocortex, especially in regions for high-order cognition (e.g., prefrontal cortex). We then discuss how such an expansion can be linked to heterochrony of the brain developmental program, resulting in a greater number of neurons and enhanced computational capacity. Furthermore, alteration of gene expression in the human brain has been associated with positive selection in DNA sequences of gene regulatory regions. These results not only imply that genes associated with brain development are a major factor in the evolution of cognition, but also that high-quality whole-genome sequencing and gene manipulation techniques are needed for an integrative and functional understanding of comparative cognition in non-model organisms.

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