Neuroscience › Cognitive Neuroscience

Functional Brain Connectivity Studies

Works Count: 84917

Wikipedia

Description

This cluster of papers focuses on the analysis of brain functional connectivity networks using techniques such as resting-state fMRI, default mode network activity, cortical parcellation, and graph theoretical analysis. It explores the organization, development, and dysfunction of brain networks in various neurological disorders.

Keywords

Functional Connectivity; Resting-State fMRI; Default Mode Network; Brain Network Organization; Connectome; Neuroimaging Data Analysis; Cortical Parcellation; Graph Theory; Brain Network Development; Neurological Disorders

Statistical Parametric Mapping: The Analysis of Functional Brain Images

2007-01-01

W.D. Penny , Karl Friston , John Ashburner +2 more

Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging

2007-08-20

Michael Fox , Marcus E. Raichle

Methods to detect, characterize, and remove motion artifact in resting state fMRI

2013-08-29

Jonathan D. Power , Anish Mitra , Timothy O. Laumann +3 more

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<i>Conn</i>: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks

2012-05-29

Susan Whitfield‐Gabrieli , Alfonso Nieto-Castañón

Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of … Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.

Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

2011-10-17

Jonathan D. Power , Kelly A. Barnes , Abraham Z. Snyder +2 more

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Complex brain networks: graph theoretical analysis of structural and functional systems

2009-02-04

Edward T. Bullmore , Olaf Sporns

Consistent resting-state networks across healthy subjects

2006-09-01

Jessica S. Damoiseaux , Serge A.R.B. Rombouts , Frederik Barkhof +4 more

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest … Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.

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BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics

2013-07-04

Mingrui Xia , Jinhui Wang , Yong He

The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and … The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).

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Large-scale brain networks and psychopathology: a unifying triple network model

2011-09-11

Vinod Menon

Functional connectivity in the motor cortex of resting human brain using echo‐planar mri

1995-10-01

Bharat B. Biswal , F. Zerrin Yetkin , Victor M. Haughton +1 more

Abstract An MRI time course of 512 echo‐planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic … Abstract An MRI time course of 512 echo‐planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (<0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation ( P < 10 −3 ) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.

A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data

2005-04-22

Simon B. Eickhoff , Klaas Ε. Stephan , Hartmut Mohlberg +4 more

Brainstorm: A User-Friendly Application for MEG/EEG Analysis

2011-01-01

François Tadel , Sylvain Baillet , John C. Mosher +2 more

Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical … Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).

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The WU-Minn Human Connectome Project: An overview

2013-05-16

David C. Van Essen , Stephen M. Smith , Deanna M. Barch +3 more

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Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference

2008-04-12

Stephen M. Smith , Thomas E. Nichols

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Searching for a baseline: Functional imaging and the resting human brain

2001-10-01

Debra A. Gusnard , Marcus E. Raichle

DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI

2010-01-01

Chao‐Gan Yan

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. … Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.

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The organization of the human cerebellum estimated by intrinsic functional connectivity

2011-07-28

B. T. Thomas Yeo , Fenna M. Krienen , Jorge Sepulcre +7 more

The striatum is connected to the cerebral cortex through multiple anatomical loops that process sensory, limbic, and heteromodal information. Tract-tracing studies in the monkey reveal that these corticostriatal connections form … The striatum is connected to the cerebral cortex through multiple anatomical loops that process sensory, limbic, and heteromodal information. Tract-tracing studies in the monkey reveal that these corticostriatal connections form stereotyped patterns in the striatum. Here the organization of the striatum was explored in the human with resting-state functional connectivity MRI (fcMRI). Data from 1,000 subjects were registered with nonlinear deformation of the striatum in combination with surface-based alignment of the cerebral cortex. fcMRI maps derived from seed regions placed in the foot and tongue representations of the motor cortex yielded the expected inverted somatotopy in the putamen. fcMRI maps derived from the supplementary motor area were located medially to the primary motor representation, also consistent with anatomical studies. The topography of the complete striatum was estimated and replicated by assigning each voxel in the striatum to its most strongly correlated cortical network in two independent groups of 500 subjects. The results revealed at least five cortical zones in the striatum linked to sensorimotor, premotor, limbic, and two association networks with a topography globally consistent with monkey anatomical studies. The majority of the human striatum was coupled to cortical association networks. Examining these association networks further revealed details that fractionated the five major networks. The resulting estimates of striatal organization provide a reference for exploring how the striatum contributes to processing motor, limbic, and heteromodal information through multiple large-scale corticostriatal circuits.

What we can do and what we cannot do with fMRI

2008-06-01

Nikos K. Logothetis

Correspondence of the brain's functional architecture during activation and rest

2009-07-22

Stephen M. Smith , Peter T. Fox , Karla L. Miller +7 more

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions … Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is "at rest." In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically "active" even when at "rest."

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The Reorienting System of the Human Brain: From Environment to Theory of Mind

2008-05-01

Maurizio Corbetta , Gaurav H. Patel , Gordon L. Shulman

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Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain

1993-04-01

Matti Hämäläinen , Riitta Hari , Risto J. Ilmoniemi +2 more

Magnetoencephalography (MEG) is a noninvasive technique for investigating neuronal activity in the living human brain. The time resolution of the method is better than 1 ms and the spatial discrimination … Magnetoencephalography (MEG) is a noninvasive technique for investigating neuronal activity in the living human brain. The time resolution of the method is better than 1 ms and the spatial discrimination is, under favorable circumstances, 2-3 mm for sources in the cerebral cortex. In MEG studies, the weak 10 fT-1 pT magnetic fields produced by electric currents flowing in neurons are measured with multichannel SQUID (superconducting quantum interference device) gradiometers. The sites in the cerebral cortex that are activated by a stimulus can be found from the detected magnetic-field distribution, provided that appropriate assumptions about the source render the solution of the inverse problem unique. Many interesting properties of the working human brain can be studied, including spontaneous activity and signal processing following external stimuli. For clinical purposes, determination of the locations of epileptic foci is of interest. The authors begin with a general introduction and a short discussion of the neural basis of MEG. The mathematical theory of the method is then explained in detail, followed by a thorough description of MEG instrumentation, data analysis, and practical construction of multi-SQUID devices. Finally, several MEG experiments performed in the authors' laboratory are described, covering studies of evoked responses and of spontaneous activity in both healthy and diseased brains. Many MEG studies by other groups are discussed briefly as well.

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Preface: Cerebral Cortex Has Come of Age

1991-01-01

Patricia S. Goldman‐Rakic , Pasko Rakić

In recent years, many new cortical areas have been identified in the macaque monkey. The number of iden tified connections hetween areas has increased even more dramatically. We report here … In recent years, many new cortical areas have been identified in the macaque monkey. The number of iden tified connections hetween areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31% of the possible number of pathways it each area were connected with all others. The actual degree of connectivity is likely to he closer to 40%. The great majority of pathways involve reciprocal connections be tween areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory/motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40%. Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somato sensory/motor areas that is more comprehensive thao those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas V1 and V2 and, at a high level, are related to the distinction between processing centers in the temporal and parietal lobes. However, there are some pathways and relationships (about 10% of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainties in the reported assignment.

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Functional Network Organization of the Human Brain

2011-11-01

Jonathan D. Power , Alexander L. Cohen , Scott M. Nelson +7 more

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Parallel Organization of Functionally Segregated Circuits Linking Basal Ganglia and Cortex

1986-03-01

Garrett E. Alexander , Mahlon R. DeLong , P L Strick

Information about the basal ganglia has accumulated at a prodigious pace over the past decade, necessitating major revisions in our concepts of the structural and functional organization of these nuclei. … Information about the basal ganglia has accumulated at a prodigious pace over the past decade, necessitating major revisions in our concepts of the structural and functional organization of these nuclei. From earlier data it had appeared that the basal ganglia served primarily to integrate diverse inputs from the entire cerebral cortex and to funnel these influences, via the ventrolateral thalamus, to the motor cortex (Allen & Tsukahara 1974, Evarts & Thach 1969, Kemp & Powell 1971). In particular, the basal

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

2006-03-14

Rahul S. Desikan , Florent Ségonne , Bruce Fischl +7 more

What is cognitive reserve? Theory and research application of the reserve concept

2002-03-01

Yaakov Stern

The idea of reserve against brain damage stems from the repeated observation that there does not appear to be a direct relationship between the degree of brain pathology or brain … The idea of reserve against brain damage stems from the repeated observation that there does not appear to be a direct relationship between the degree of brain pathology or brain damage and the clinical manifestation of that damage. This paper attempts to develop a coherent theoretical account of reserve. One convenient subdivision of reserve models revolves around whether they envision reserve as a passive process, such as in brain reserve or threshold, or see the brain as actively attempting to cope with or compensate for pathology, as in cognitive reserve. Cognitive reserve may be based on more efficient utilization of brain networks or of enhanced ability to recruit alternate brain networks as needed. A distinction is suggested between reserve, the ability to optimize or maximize normal performance, and compensation, an attempt to maximize performance in the face of brain damage by using brain structures or networks not engaged when the brain is not damaged. Epidemiologic and imaging data that help to develop and support the concept of reserve are presented.

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Measuring the thickness of the human cerebral cortex from magnetic resonance images

2000-09-12

Bruce Fischl , Anders M. Dale

Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for … Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test–retest studies, as well as by comparison of cross-subject regional thickness measures with published values.

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An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets

2003-06-09

Joseph A. Maldjian , Paul J. Laurienti , Robert Kraft +1 more

The precuneus: a review of its functional anatomy and behavioural correlates

2006-01-06

Andrea E. Cavanna , Michael Trimble

Functional neuroimaging studies have started unravelling unexpected functional attributes for the posteromedial portion of the parietal lobe, the precuneus. This cortical area has traditionally received little attention, mainly because of … Functional neuroimaging studies have started unravelling unexpected functional attributes for the posteromedial portion of the parietal lobe, the precuneus. This cortical area has traditionally received little attention, mainly because of its hidden location and the virtual absence of focal lesion studies. However, recent functional imaging findings in healthy subjects suggest a central role for the precuneus in a wide spectrum of highly integrated tasks, including visuo-spatial imagery, episodic memory retrieval and self-processing operations, namely first-person perspective taking and an experience of agency. Furthermore, precuneus and surrounding posteromedial areas are amongst the brain structures displaying the highest resting metabolic rates (hot spots) and are characterized by transient decreases in the tonic activity during engagement in non-self-referential goal-directed actions (default mode of brain function). Therefore, it has recently been proposed that precuneus is involved in the interwoven network of the neural correlates of self-consciousness, engaged in self-related mental representations during rest. This hypothesis is consistent with the selective hypometabolism in the posteromedial cortex reported in a wide range of altered conscious states, such as sleep, drug-induced anaesthesia and vegetative states. This review summarizes the current knowledge about the macroscopic and microscopic anatomy of precuneus, together with its wide-spread connectivity with both cortical and subcortical structures, as shown by connectional and neurophysiological findings in non-human primates, and links these notions with the multifaceted spectrum of its behavioural correlates. By means of a critical analysis of precuneus activation patterns in response to different mental tasks, this paper provides a useful conceptual framework for matching the functional imaging findings with the specific role(s) played by this structure in the higher-order cognitive functions in which it has been implicated. Specifically, activation patterns appear to converge with anatomical and connectivity data in providing preliminary evidence for a functional subdivision within the precuneus into an anterior region, involved in self-centred mental imagery strategies, and a posterior region, subserving successful episodic memory retrieval.

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Statistical parametric maps in functional imaging: A general linear approach

1994-01-01

Karl Friston , Andrew P. Holmes , Keith J. Worsley +3 more

Abstract Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps … Abstract Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. [1991]: J Cereb Blood Flow Metab 11:690–699; Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900–918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accomodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data. The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis. © 1995 Wiley‐Liss, Inc.

Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control

2007-02-28

William W. Seeley , Vinod Menon , Alan F. Schatzberg +5 more

Variations in neural circuitry, inherited or acquired, may underlie important individual differences in thought, feeling, and action patterns. Here, we used task-free connectivity analyses to isolate and characterize two distinct … Variations in neural circuitry, inherited or acquired, may underlie important individual differences in thought, feeling, and action patterns. Here, we used task-free connectivity analyses to isolate and characterize two distinct networks typically coactivated during functional MRI tasks. We identified a “salience network,” anchored by dorsal anterior cingulate (dACC) and orbital frontoinsular cortices with robust connectivity to subcortical and limbic structures, and an “executive-control network” that links dorsolateral frontal and parietal neocortices. These intrinsic connectivity networks showed dissociable correlations with functions measured outside the scanner. Prescan anxiety ratings correlated with intrinsic functional connectivity of the dACC node of the salience network, but with no region in the executive-control network, whereas executive task performance correlated with lateral parietal nodes of the executive-control network, but with no region in the salience network. Our findings suggest that task-free analysis of intrinsic connectivity networks may help elucidate the neural architectures that support fundamental aspects of human behavior.

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Dynamic causal modelling

2003-07-16

Karl Friston , L. Harrison , W.D. Penny

A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains

2001-07-01

Catriona D. Good , Ingrid S. Johnsrude , John Ashburner +3 more

Dynamic mapping of human cortical development during childhood through early adulthood

2004-05-17

Nitin Gogtay , Jay N. Giedd , Leslie Lusk +7 more

We report the dynamic anatomical sequence of human cortical gray matter development between the age of 4–21 years using quantitative four-dimensional maps and time-lapse sequences. Thirteen healthy children for whom … We report the dynamic anatomical sequence of human cortical gray matter development between the age of 4–21 years using quantitative four-dimensional maps and time-lapse sequences. Thirteen healthy children for whom anatomic brain MRI scans were obtained every 2 years, for 8–10 years, were studied. By using models of the cortical surface and sulcal landmarks and a statistical model for gray matter density, human cortical development could be visualized across the age range in a spatiotemporally detailed time-lapse sequence. The resulting time-lapse “movies” reveal that ( i ) higher-order association cortices mature only after lower-order somatosensory and visual cortices, the functions of which they integrate, are developed, and ( ii ) phylogenetically older brain areas mature earlier than newer ones. Direct comparison with normal cortical development may help understanding of some neurodevelopmental disorders such as childhood-onset schizophrenia or autism.

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Large-scale automated synthesis of human functional neuroimaging data

2011-06-26

Tal Yarkoni , Russell A. Poldrack , Thomas E. Nichols +2 more

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A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

2007-05-04

Yashar Behzadi , Khaled Restom , Joy Liau +1 more

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Functional connectivity in the resting brain: A network analysis of the default mode hypothesis

2002-12-27

Michael D. Greicius , Ben Krasnow , Allan L. Reiss +1 more

Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive … Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve.

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Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates

2016-06-28

Anders Eklund , Thomas E. Nichols , Hans Knutsson

The most widely used task fMRI analyses use parametric methods that depend on a variety of assumptions. While individual aspects of these fMRI models have been evaluated, they have not … The most widely used task fMRI analyses use parametric methods that depend on a variety of assumptions. While individual aspects of these fMRI models have been evaluated, they have not been evaluated in a comprehensive manner with empirical data. In this work, a total of 2 million random task fMRI group analyses have been performed using resting state fMRI data, to compute empirical familywise error rates for the software packages SPM, FSL and AFNI, as well as a standard non-parametric permutation method. While there is some variation, for a nominal familywise error rate of 5% the parametric statistical methods are shown to be conservative for voxel-wise inference and invalid for cluster-wise inference; in particular, cluster size inference with a cluster defining threshold of p = 0.01 generates familywise error rates up to 60%. We conduct a number of follow up analyses and investigations that suggest the cause of the invalid cluster inferences is spatial auto correlation functions that do not follow the assumed Gaussian shape. By comparison, the non-parametric permutation test, which is based on a small number of assumptions, is found to produce valid results for voxel as well as cluster wise inference. Using real task data, we compare the results between one parametric method and the permutation test, and find stark differences in the conclusions drawn between the two using cluster inference. These findings speak to the need of validating the statistical methods being used in the neuroimaging field.

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<i>The Brain's Default Network</i>

2008-03-01

Randy L. Buckner , Jessica R. Andrews‐Hanna , Daniel L. Schacter

Thirty years of brain imaging research has converged to define the brain's default network—a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we … Thirty years of brain imaging research has converged to define the brain's default network—a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment. Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system. Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others. Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems. The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation. The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self‐relevant mental simulations. These two subsystems converge on important nodes of integration including the posterior cingulate cortex. The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world. We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer's disease.

Investigations into resting-state connectivity using independent component analysis

2005-05-29

Christian F. Beckmann , Marilena DeLuca , Joseph T. Devlin +1 more

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) … Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory–motor cortex.

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Nonparametric permutation tests for functional neuroimaging: A primer with examples

2001-10-15

Thomas E. Nichols , Andrew P. Holmes

Abstract Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. … Abstract Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([ 1996 ]: J Cereb Blood Flow Metab 16:7–22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel‐by‐voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi‐subject PET/SPECT or f MRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and f MRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices. Hum. Brain Mapping 15:1–25, 2001. © 2001 Wiley‐Liss, Inc.

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Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI

2004-03-15

Michael D. Greicius , Gaurav Srivastava , Allan L. Reiss +1 more

Recent functional imaging studies have revealed coactivation in a distributed network of cortical regions that characterizes the resting state, or default mode, of the human brain. Among the brain regions … Recent functional imaging studies have revealed coactivation in a distributed network of cortical regions that characterizes the resting state, or default mode, of the human brain. Among the brain regions implicated in this network, several, including the posterior cingulate cortex and inferior parietal lobes, have also shown decreased metabolism early in the course of Alzheimer's disease (AD). We reasoned that default-mode network activity might therefore be abnormal in AD. To test this hypothesis, we used independent component analysis to isolate the network in a group of 13 subjects with mild AD and in a group of 13 age-matched elderly controls as they performed a simple sensory-motor processing task. Three important findings are reported. Prominent coactivation of the hippocampus, detected in all groups, suggests that the default-mode network is closely involved with episodic memory processing. The AD group showed decreased resting-state activity in the posterior cingulate and hippocampus, suggesting that disrupted connectivity between these two regions accounts for the posterior cingulate hypometabolism commonly detected in positron emission tomography studies of early AD. Finally, a goodness-of-fit analysis applied at the individual subject level suggests that activity in the default-mode network may ultimately prove a sensitive and specific biomarker for incipient AD.

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The Brain's Default Mode Network

2015-05-04

Marcus E. Raichle

The brain's default mode network consists of discrete, bilateral and symmetrical cortical areas, in the medial and lateral parietal, medial prefrontal, and medial and lateral temporal cortices of the human, … The brain's default mode network consists of discrete, bilateral and symmetrical cortical areas, in the medial and lateral parietal, medial prefrontal, and medial and lateral temporal cortices of the human, nonhuman primate, cat, and rodent brains. Its discovery was an unexpected consequence of brain-imaging studies first performed with positron emission tomography in which various novel, attention-demanding, and non-self-referential tasks were compared with quiet repose either with eyes closed or with simple visual fixation. The default mode network consistently decreases its activity when compared with activity during these relaxed nontask states. The discovery of the default mode network reignited a longstanding interest in the significance of the brain's ongoing or intrinsic activity. Presently, studies of the brain's intrinsic activity, popularly referred to as resting-state studies, have come to play a major role in studies of the human brain in health and disease. The brain's default mode network plays a central role in this work.

Cortical Surface-Based Analysis

1999-02-01

Anders M. Dale , Bruce Fischl , Martin I. Sereno

Functional Neuroanatomy of Emotion: A Meta-Analysis of Emotion Activation Studies in PET and fMRI

2002-06-01

K. Luan Phan , Tor D. Wager , Stephan F. Taylor +1 more

Mapping the Structural Core of Human Cerebral Cortex

2008-06-24

Patric Hagmann , Leila Cammoun , Xavier Gigandet +4 more

Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these … Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.

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Complex network measures of brain connectivity: Uses and interpretations

2009-10-10

Mikail Rubinov , Olaf Sporns

A multi-modal parcellation of human cerebral cortex

2016-07-19

Matthew F. Glasser , Timothy S. Coalson , Emma C. Robinson +7 more

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FreeSurfer

2012-01-10

Bruce Fischl

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The organization of the human cerebral cortex estimated by intrinsic functional connectivity

2011-06-09

B.T. Thomas Yeo , Fenna M. Krienen , Jorge Sepulcre +7 more

Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, … Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.

Brain age estimation: premise, promise, and problems.

2025-08-01

Jarrad Perron , Ji Hyun Ko

Simulating Scalp EEG from Ultrahigh-Density ECoG Data Illustrates Cortex to Scalp Projection Patterns

2025-06-25

Seyed Yahya Shirazi , Julie Onton , Scott Makeig | bioRxiv (Cold Spring Harbor Laboratory)

Ultrahigh-density electrocorticography (μECoG) provides unprecedented spatial resolution for recording cortical electrical activity. This study uses simulated scalp projections from an μECoG recording to challenge the assumption that channel-level electroencephalography (EEG) … Ultrahigh-density electrocorticography (μECoG) provides unprecedented spatial resolution for recording cortical electrical activity. This study uses simulated scalp projections from an μECoG recording to challenge the assumption that channel-level electroencephalography (EEG) reflects only local field potentials near the recording electrode. Using a 1024-electrode μECoG array placed on the primary motor cortex during finger movements, we applied Adaptive Mixture Independent Component Analysis (AMICA) to decompose activity into maximally independent grid activity components and projected these to 207 simulated EEG scalp electrode channels using a high-definition MR image-based electrical forward-problem head model. Our findings demonstrate how cortical surface-recorded potentials propagate to scalp electrodes both far from and near to the generating location. This work has significant implications for interpreting both EEG and ECoG data in clinical and research applications. Clinical Relevance - This study provides insights for interpreting scalp EEG data, demonstrating that scalp channel activity represents a complex mixture of distributed cortical source activities rather than primarily activity generated nearest to the scalp electrodes. These findings may hopefully spur improvement in EEG-based diagnostics for neurological disorders.

Grip Strength as a Marker of Resting-State Network Integrity and Well-Being in Early Psychosis

2025-06-25

Heather Burrell Ward , Adam Beermann , Karlos Manzanarez Felix +7 more | American Journal of Psychiatry

Psychomotor function is a critical marker of risk and outcome of psychosis. Grip strength is one aspect of psychomotor function that is known to be linked to structural neural integrity … Psychomotor function is a critical marker of risk and outcome of psychosis. Grip strength is one aspect of psychomotor function that is known to be linked to structural neural integrity and well-being. This study sought to determine whether grip strength is a marker of alterations in resting-state connectivity and well-being in psychotic disorders in order to further clarify the mechanisms by which psychosis phenomenology is related to psychomotor processes. The authors analyzed resting-state functional MRI and grip strength in 89 individuals with early psychosis and 51 control subjects without psychiatric disorders from the Human Connectome Project for Early Psychosis. Participants ranged in age from 16 to 35 years. Using multivariate pattern analysis of whole-connectome data, the authors identified brain correlates of grip strength and then replicated this analysis using the NIH Toolbox well-being measures and the Global Assessment of Functioning Scale (GAF). The psychosis group exhibited reduced grip strength, well-being, and GAF scores compared to the control group. Grip strength was linked to resting-state connectivity in the sensorimotor cortex, anterior cingulate cortex, and cerebellum. Connectivity correlated with the default mode network (DMN) (rsensorimotor=0.22, rcingulate=0.30, rcerebellum=0.24). When the analysis was repeated for GAF and well-being, overlapping regions in the sensorimotor cortex and cerebellum were connected to the DMN and related to GAF (rsensorimotor=0.17, rcerebellum=0.28) and well-being (rsensorimotor=0.16, rcerebellum=0.16). Relationships were driven by the psychosis group for cerebellum and cingulate nodes. Data-driven, connectome-wide analysis identified shared brain correlates of grip strength, overall function, and well-being in a sample of young adults with psychosis and healthy control subjects. This suggests that grip strength may be a marker of DMN connectivity, which may in turn be an important marker of overall health, even in young adult populations.

Voxel- and surface-based morphometry in the cortical thickness and cortical and subcortical gray matter volume in patients with mild-to-moderate Alzheimer’s disease

2025-06-25

Kaidi Li , Dong-Ge Xie , Zhengyong Zhang +2 more | Frontiers in Aging Neuroscience

Aim This study aimed to investigate alterations in whole-brain cortical thickness (CT) and cortical and subcortical gray matter volume (GMV) in patients with Alzheimer’s disease (AD) compared with healthy controls … Aim This study aimed to investigate alterations in whole-brain cortical thickness (CT) and cortical and subcortical gray matter volume (GMV) in patients with Alzheimer’s disease (AD) compared with healthy controls (HC) using voxel-based morphometry (VBM) and surface-based morphometry (SBM). Furthermore, we sought to develop a combined predictive model based on these neuroimaging markers and assess their potential clinical utility for the early detection and diagnosis of AD. Methods A total of 42 patients diagnosed with mild-to-moderate AD and 49 demographically matched HC were recruited for this study. VBM and SBM analyses were performed on three-dimensional T1-weighted magnetization-prepared rapid gradient echo (3D T1-MPRAGE) imaging sequences to identify brain regions that exhibited statistically significant differences between the AD and HC groups. Brain regions showing significant group differences were selected as the regions of interest (ROIs). Pearson’s correlation analysis was used to assess the relationship between extracted neuroimaging metrics (CT, cortical GMV, and subcortical GMV) and cognitive performance. Predictive models were constructed using CT (from SBM), cortical GMV, and subcortical GMV (from VBM) metrics derived from ROIs, both individually and in combination. Model performance in discriminating between patients with AD and HCs was evaluated using a receiver operating characteristic (ROC) curve analysis. Results Compared to HCs, patients with AD exhibited significant CT reductions primarily in the transverse temporal gyrus, superior temporal gyrus, supramarginal gyrus, insula, temporal pole, entorhinal cortex, and fusiform gyrus. Significant GMV reductions in patients with AD were observed predominantly in the hippocampus, parahippocampal gyrus, posterior temporal lobe, inferior temporal gyrus, middle temporal gyrus, limbic lobe structures, fusiform gyrus, amygdala, and thalamus, as detected by VBM analysis. Extracted CT, cortical GMV, and subcortical GMV measurements from the ROIs demonstrated significant positive correlations with both MMSE and MoCA scores. Conclusion In patients with AD, VBM and SBM showed overlapping cortical GMV and CT reductions. Volume/thickness reduction was correlated with lower MMSE/MoCA scores, confirming functional relevance. ROC analysis revealed that combining CT and GMV improved cognitive impairment prediction compared to single measures. This integrated approach may enhance clinical diagnosis and early risk identification of AD.

Modeling heterogeneity in cognitive trajectories in the Framingham Heart Study

2025-06-25

Yuan Fang , Jiachen Chen , Jesse Mez +6 more | Frontiers in Aging Neuroscience

Introduction The prevalence of cognitive impairment in the population is growing; however, there is substantial heterogeneity in the rate of decline across different cognitive domains. Harmonized factor scores measuring memory, … Introduction The prevalence of cognitive impairment in the population is growing; however, there is substantial heterogeneity in the rate of decline across different cognitive domains. Harmonized factor scores measuring memory, executive function, and language domains have been created in the Framingham Heart Study (FHS). Methods This work identified FHS participants with two or more repeated factor scores after age 60 and fitted latent class mixed models (LCMM) to cluster cognitive trajectories within each domain. Non-linear shapes of trajectories were modeled piecewise linearly, followed by stepwise selections to select cluster-specific change points. Results We identified different latent classes of participants with early cognitive decline, compared to late decliners, for each domain. Ten-fold cross-validation yielded stable subgroupings. Our findings show latent-class-related differential patterns in cognitive aging in the FHS. We also investigated the association between identified latent classes with existing protein biomarkers of cognitive aging in a subsample of the study and found elevated levels of CD40L and CD14 were associated with a higher risk of early decline in memory and executive function domain, respectively. Discussion In summary, our study advances the understanding of cognitive decline heterogeneity among FHS participants and sets the stage for further investigations into early intervention strategies and personalized approaches to mitigate cognitive aging risks.

Polygenic Risk, Psychopathology, and Personalized Functional Brain Network Topography in Adolescence

2025-06-25

Kevin Sun , J. Eric Schmitt , Tyler M. Moore +7 more | JAMA Psychiatry

Importance Functional brain networks are associated with both behavior and genetic factors. To uncover biological mechanisms of psychopathology, it is critical to define how the spatial organization of these networks … Importance Functional brain networks are associated with both behavior and genetic factors. To uncover biological mechanisms of psychopathology, it is critical to define how the spatial organization of these networks relates to genetic risk during development. Objective To determine the associations among transdiagnostic polygenic risk scores (PRSs), personalized functional brain networks (PFNs), and overall psychopathology (p-factor) during early adolescence. Design, Setting, and Participants The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing longitudinal cohort study of 21 collection sites across the US. This cross-sectional analysis includes ABCD baseline data collected between September 2016 and October 2018. The ABCD Study is a multisite community-based study. The sample is largely recruited through school systems. ABCD exclusion criteria included severe sensory, intellectual, medical, or neurological issues that interfere with protocol and scanner contraindications. Split-half subsets were used for cross-validation, matched on age, ethnicity, family structure, handedness, parental education, site, sex, and anesthesia exposure. Data were analyzed from January 2023 to July 2024. Exposures Polygenic risk scores of transdiagnostic genetic factors F1 (PRS-F1) and F2 (PRS-F2) derived from adults in Psychiatric Genomic Consortium and UK Biobanks datasets. PRS-F1 indexes liability for common psychiatric symptoms and disorders related to mood disturbance; PRS-F2 indexes liability for rarer forms of mental illness characterized by mania and psychosis. Main Outcomes and Measures P-factor derived from bifactor models of youth- and parent-reported mental health assessments and person-specific functional brain network topography derived from functional magnetic resonance imaging scans. Results Total participants included 11 873 children aged 9 to 10 years; 5678 (47.8%) were female, and the mean (SD) age was 9.92 (0.62) years. PFN topography was found to be heritable (imaging subsample, n = 7459; 57.1% of vertices: mean h 2 , 0.35; false discovery rate–corrected P &amp;lt; .05). PRS-F1 was associated with p-factor (European ancestry subsample, n = 5815; r , 0.12; 95% CI, 0.09-0.15; P &amp;lt; .001). Interindividual differences in functional network topography were associated with p-factor (imaging subsample, n = 7459; mean r , 0.12), PRS-F1 (imaging and European ancestry subsample, n = 3982; mean r , 0.05), and PRS-F2 (n = 3982; mean r , 0.08). Cortical maps of p-factor and PRS-F1 regression coefficients were correlated ( r , 0.70; P = .003, permutation test, N = 1000). Conclusions and Relevance Polygenic risk for transdiagnostic adulthood psychopathology was associated with both p-factor and heritable PFN topography during early adolescence in this study. These results may advance our understanding of the developmental drivers of psychopathology.

Beyond Pairwise Interactions: Charting Higher-Order Models of Brain Function

2025-06-25

Andrea Santoro , Matteo Neri , Simone Poetto +4 more | bioRxiv (Cold Spring Harbor Laboratory)

Traditional models of brain connectivity have primarily focused on pairwise interactions, overlooking the rich dynamics that emerge from simultaneous interactions among multiple brain regions. Although a plethora of higher-order interaction … Traditional models of brain connectivity have primarily focused on pairwise interactions, overlooking the rich dynamics that emerge from simultaneous interactions among multiple brain regions. Although a plethora of higher-order interaction (HOI) metrics have been proposed, a systematic evaluation of their comparative properties and utility is missing. Here, we present the first large-scale analysis of information-theoretic and topological HOI metrics, applied to both resting-state and task fMRI data from 100 unrelated subjects of the Human Connectome Project. We identify a clear taxonomy of HOI metrics - redundant, synergistic, and topological-, with the latter acting as bridges along the redundancy-synergy continuum. Despite methodological differences, all HOI metrics align with the brain's overarching unimodal-to-transmodal functional hierarchy. However, certain metrics show specific associations with the neurotransmitter receptor architecture. HOI metrics outperform traditional pairwise models in brain fingerprinting and perform comparably in task decoding, underscoring their value for characterizing individual functional profiles. Finally, multivariate analysis reveals that - among all HOI metrics - topological descriptors are key to linking brain function with behavioral variability, positioning them as valuable tools for linking neural architecture and cognitive function. Overall, our findings establish HOIs as a powerful framework for capturing the brain's multidimensional dynamics, providing a conceptual map to guide their application across cognitive and clinical neuroscience.

MEGaNorm: Normative Modeling of MEG Brain Oscillations Across the Human Lifespan

2025-06-25

Mohammad Zamanzadeh , Ymke Verduyn , Augustijn A. A. de Boer +7 more | bioRxiv (Cold Spring Harbor Laboratory)

Normative modeling provides a principled framework for quantifying individual deviations from typical brain development and is increasingly used to study heterogeneity in neuropsychiatric conditions. While widely applied to structural phenotypes, … Normative modeling provides a principled framework for quantifying individual deviations from typical brain development and is increasingly used to study heterogeneity in neuropsychiatric conditions. While widely applied to structural phenotypes, functional normative models remain underdeveloped. Here, we introduce MEGaNorm, the first normative modeling framework for charting lifespan trajectories of resting-state magnetoencephalography (MEG) brain oscillations. Using a large, multi-site dataset comprising 1,846 individuals aged 6-88 and spanning three MEG systems, we model relative oscillatory power in canonical frequency bands using hierarchical Bayesian regression, accounting for age, sex, and site effects. To support interpretation at multiple scales, we introduce Neuro-Oscillo Charts, visual tools that summarize normative trajectories at the population level and quantify individual-level deviations, enabling personalized assessment of functional brain dynamics. Applying this framework to a Parkinson's disease cohort (n = 160), we show that normative deviation scores reveal disease-related abnormalities and uncover a continuum of patients in theta-beta deviation space. This work provides the first lifespan-encompassing normative reference for MEG oscillations, enabling population-level characterization and individualized benchmarking. All models and tools are openly available and designed for federated, continual adaptation as new data become available, offering a scalable resource for precision neuropsychiatry.

BrainCHEF: Cross-Level Hypergraph Enhanced Fusion model for brain networks

2025-06-24

Zhiteng Zhu , Jiannuo Li , Lan Yao | Computers in Biology and Medicine

Region-specific mean field models enhance simulations of local and global brain dynamics

2025-06-24

Roberta Maria Lorenzi , Fulvia Palesi , Claudia Casellato +2 more | npj Systems Biology and Applications

The default mode subnetworks’ involvement in diverse cognitive transitions suggests a role in external update of internal models

2025-06-24

Ashley X Zhou , John Duncan , Daniel J. Mitchell | Current Opinion in Behavioral Sciences

Gray matter volume heterogeneity by stage, site of origin and pathophysiology in schizophrenia

2025-06-23

Yuchao Jiang , Lena Palaniyappan , Xiao Chang +7 more | Nature Mental Health

BrainNet-GAN: Generative Adversarial Graph Convolutional Network for Functional Brain Network Synthesis from Routine Clinical Brain Structural T1-Weighted Sequence

2025-06-23

Nan Hao , Zhiwei Song , Qiang Zheng | Brain Topography

Brainpack: a suite of advanced statistical techniques for multi-subject and multi-group fMRI data analysis

2025-06-23

Arunava Samaddar , Brooke S. Jackson , Nicole A. Lazar +2 more | Journal of the Korean Statistical Society

Transdiagnostic neuromodulation of impulsivity: current status and future trajectories

2025-06-23

Gangliang Zhong , Tianzhen Chen , Na Zhong +7 more | Translational Psychiatry

Impulsivity, increasingly perceived as a transdiagnostic characteristic, significantly influences diverse psychiatric conditions and emanates beyond the boundaries defined by traditional classification systems. Transdiagnostic research has shed light on the complex … Impulsivity, increasingly perceived as a transdiagnostic characteristic, significantly influences diverse psychiatric conditions and emanates beyond the boundaries defined by traditional classification systems. Transdiagnostic research has shed light on the complex clinical manifestations of impulsivity, and the underpinning neural circuitry. The pressing challenge now is to translate this enhanced understanding into precise and potent interventions tailored to these different aspects of impulsivity. Recent advancements in neuromodulation, specifically targeting brain circuits, have provided encouraging evidence for improvements in clinical symptoms, and neural circuitry across various psychiatric conditions, signposting a transformative phase in crafting interventions that tackle impulsivity from a transdiagnostic perspective. However, the field continues to ascertain a universally embraced framework that effectively amalgamates these discoveries into a unified clinical methodology. The Research Domain Criteria (RDoC) delivers a neuroscientifically informed framework that aims to reconcile the neurobiological underpinnings with clinical symptoms, thereby facilitating targeted neuromodulation strategies. In this context, we propose a pioneering RDoC-compliant framework that strategically targets the neural circuits implicated in clinical impulsivity symptoms, applicable across diagnostic categories. Furthermore, we introduce a set of meticulously selected tools for each stage within this framework, thus reinforcing its applicability and aiding future investigative pursuits in this area.

Machine learning for classifying affective valence from fMRI: a systematic review and meta-analysis

2025-06-23

Charith Chitraranjan , R B Dayananda , Dakshitha Suriyaaratchie +2 more | Discover Artificial Intelligence

Can short-term memory capacity change resting brain behavior? Findings from neuropsychological assessment and dynamic causal modeling

2025-06-23

Ahmad Nazlim Yusoff , Elza Azri Othman , Mazlyfarina Mohamad +3 more | Applied Neuropsychology Adult

This study aimed to investigate different short-term memory capacities (STMC) on resting brain of healthy individuals particularly the neuropsychology and connectivity behaviors. The outcomes may serve as a baseline for … This study aimed to investigate different short-term memory capacities (STMC) on resting brain of healthy individuals particularly the neuropsychology and connectivity behaviors. The outcomes may serve as a baseline for clinical diagnosis of memory decline due to aging and mental disorders. It was hypothesized that resting brain of low and typical STMC individuals behaves differently. Thirty-nine healthy young male adults were recruited from local universities. They were categorized as typical or low STMC based on their scores in the Malay Version of the Auditory-Verbal Learning Test (MVAVLT). A resting-state functional magnetic resonance imaging (rs-fMRI) was conducted and data were analyzed using statistical parametric mapping (SPM) and dynamic causal modeling (DCM). Nine neuropsychological assessments were significantly higher (p < 0.05) in typical STMC participants compared with low STMC participants. Four activation clusters survived the contrast "Low > Typical" uncorrected at set and cluster levels threshold (pFWE < 0.05). A causal model containing these clusters as nodes found that there is no preference on negative or positive connectivity among typical and low STMC groups. Nevertheless, implementing a reduced connection scheme revealed more significant connections for the low STMC group. To conclude, the low STMC participants scored lower in all neuropsychological assessments, but a higher activation profile with more areas being connected effectively as compared with the typical STMC group. The results suggest a higher resting neural activity and communication among certain brain areas in low STMC individuals that the brain could have executed as a compensation strategy.

Genetic contributions to brain criticality and its relationship with human cognitive functions

2025-06-23

Yumeng Xin , Yue Cui , Shan Yu +1 more | Proceedings of the National Academy of Sciences

Recently, extensive evidence has demonstrated that the brain operates close to a critical state, characterized by dynamic patterns known as neuronal avalanches. The critical state, reflecting the delicate balance between … Recently, extensive evidence has demonstrated that the brain operates close to a critical state, characterized by dynamic patterns known as neuronal avalanches. The critical state, reflecting the delicate balance between neural excitation and inhibition, offers numerous advantages in information processing. However, the role of genetics in shaping brain criticality is not fully understood. Whether there is any shared genetic factor influencing the critical state and cognitive functions remains elusive. Here, we aimed to address these questions by examining the heritability of brain criticality and its relation to cognitive function by analyzing resting-state functional magnetic resonance imaging (rs-fMRI) in 250 monozygotic twins, 142 dizygotic twins, and 437 Not-twin subjects. We found that genetic factors substantially influenced brain criticality across various scales, encompassing brain regions, functional networks, and the whole brain. These genetic influences exhibited heterogeneity, with the criticality of the primary sensory cortex being more strongly influenced by genetic factors compared to that of the association cortex. Furthermore, we combined rs-fMRI data with transcriptional microarray data from the Allen Brain Atlas: Human Brain (ABHB) dataset and found that the organization of regional critical dynamics was highly explained by a specific gene expression profile. Finally, our results showed that the critical state was correlated with total cognition and had a genetic link with it. These findings provide empirical evidence that brain criticality is a biological phenotype and suggest a shared genetic foundation underlying brain criticality and cognitive functions. Our results pave the way toward revealing specific biological mechanisms contributing to critical dynamics and their associations with brain function and dysfunction.

Influence of recent cannabis use on altered spectral entropy modulation and connectivity strength in patients with psychosis

2025-06-23

Vicente Molina , Álvaro Díez , Inés Fernández‐Linsenbarth +6 more | European Archives of Psychiatry and Clinical Neuroscience

Cannabis use is highly prevalent in individuals with psychosis, raising concerns about its influence on brain function. Electroencephalography (EEG) studies have identified alterations in brain activity in psychosis, including changes … Cannabis use is highly prevalent in individuals with psychosis, raising concerns about its influence on brain function. Electroencephalography (EEG) studies have identified alterations in brain activity in psychosis, including changes in spectral entropy (SE) modulation and connectivity strength (CS). However, the degree to which cannabis use contributes to these alterations remains unclear. This study investigated the effects of recent cannabis use on specific EEG measures previously found to be altered in psychosis: (i) SE modulation, (ii) pre-stimulus theta and broadband CS, and (iii) baseline CS in the gamma band. We focused specifically on the immediate effects of recent cannabis use, without considering factors like tetrahydrocannabinol content, frequency of use, or age of onset. We included 93 patients with psychosis (32 recent cannabis users, 61 non-users) and 86 age- and sex-matched healthy controls (HC; all non-users). Recent cannabis use was defined as any consumption within the past week, assessed through a clinical interview and confirmed by urinalysis. Patients had diagnosis of schizophrenia or bipolar disorder. EEG data were recorded during a P300 task, and SE modulation and baseline CS were calculated. Both patient groups (cannabis users and non-users) exhibited significantly impaired SE modulation and elevated gamma and broadband CS, compared to HC. Crucially, no significant differences were found between the two patient groups in any of the EEG measures. Recent cannabis use does not appear to be the primary driver of the observed electrophysiological alterations in psychosis. Impaired SE modulation and increased CS are likely core features of psychosis itself, independent of recent cannabis exposure. This suggests that these EEG abnormalities may represent underlying vulnerability markers for psychosis. However, further research is needed to explore the potential long-term and early-onset effects of cannabis use on brain function in individuals with psychosis.

Enhancement of the left frontoparietal network through real‐time <scp>functional magnetic resonance imaging</scp> functional connectivity–informed neurofeedback and its impact on working memory in schizophrenia: A pilot study

2025-06-22

Yuko Kobayashi , Tomohisa Asai , Yujiro Yoshihara +7 more | Psychiatry and Clinical Neurosciences

Aim Cognitive impairment in schizophrenia shows limited improvement with pharmacotherapy, indicating a need for effective treatment. The frontoparietal network supports working memory, and a biomarker has successfully predicted performance in … Aim Cognitive impairment in schizophrenia shows limited improvement with pharmacotherapy, indicating a need for effective treatment. The frontoparietal network supports working memory, and a biomarker has successfully predicted performance in patients, with the left frontoparietal network contributing the most to working memory. We hypothesized that enhancing functional connectivity in this network through real‐time neurofeedback (NF) will improve working memory in patients with schizophrenia. Methods We conducted a two‐arm, nonrandomized pilot study in patients with schizophrenia, with a NF group ( N = 11) and a control N‐back training group ( N = 11). The NF training lasted 5 days (one session per day). The first session included baseline measurements, while the next four sessions involved training. The participants completed cognitive and clinical assessments and resting‐state scans preintervention and postintervention. Our primary neural outcome was increased functional connectivity during NF, and the behavioral outcome was improvement in working memory, as indicated by scores on the digit‐span backward task and working memory capability measured by the N‐back task. Results The NF group showed increased functional connectivity within the left frontoparietal network during the final session. A significant correlation existed between functional connectivity and the improvement in the mean N‐back level, indicating that enhancing this network can boost working memory. A group‐by‐time interaction effect improved postintervention task score on the digit‐span backward task in the NF group. In addition, post‐NF scans indicated an enhanced resting‐state functional connectivity within the left frontoparietal network. Conclusion These results highlight the potential of functional connectivity–informed NF as a novel therapeutic approach for improving working memory in schizophrenia. Clinical Trial Registration Japan Registry of Clinical Trials (UMIN000024831, jRCTs052180168, jRCTs032190244).

International Collaborations at the Intersection of Brain Sciences and Artificial Intelligence

2025-06-21

John Darrell Van Horn , Emiliano Ricciardi | Neuroinformatics

topoEEG: An Python-framework for analyzing EEG data in neurodegeneratives disease through Topological Deep Learning

2025-06-21

Miriam Esteve , Alejandro Martinez-Gracia , J. J. Rodríguez-Sala +1 more | SoftwareX

Partial Spectroscopy of the Human Brain Based On the Joint Analysis of MEG and MRI Data. Application to the CamCAN Data Array

2025-06-21

С.Д. Рыкунов , Andrii Boyko | Mathematical Biology and Bioinformatics

The new technology was developed to calculate spectral characteristics of various compartments of the human brain. This technology combines two types of spatial data: 1) MEG-based functional tomogram presenting spatial … The new technology was developed to calculate spectral characteristics of various compartments of the human brain. This technology combines two types of spatial data: 1) MEG-based functional tomogram presenting spatial distribution of the electric sources and 2) anatomical structure of the brain estimated by the magnetic resonance imaging. Multichannel magnetoencephalograms were used to calculate the functional tomogram by the precise frequency-pattern analysis, decomposing brain activity into a set of elementary oscillations. In the functional tomogram, unique spatial location corresponds to each spectral component, generated by the equivalent current dipole. Based on this fact, we calculated partial spectra – sets of frequencies, generated by various anatomical regions of the brain. Individual spatial structure of the brain compartments was determined by the annotated segmentation of magnetic resonance tomogram for each subject under study. The partial spectrum of the brain compartment was composed of the set of frequencies, localized into this compartment. Here we calculated partial spectra of 15 compartments of the brain for 600 subjects, using MEG and MRI datasets obtained from the open archive CamCAN. Average spectra were calculated and the distribution of the spectral power between brain compartments was estimated.

Some reflections on OHBM 2024

2025-06-20

Sidhant Chopra | Aperture Neuro

In this commentary, I offer a personal account of major themes and trends of the 2024 meeting of the Organization for Human Brain Mapping (OHBM) in Seoul, Korea. I focus … In this commentary, I offer a personal account of major themes and trends of the 2024 meeting of the Organization for Human Brain Mapping (OHBM) in Seoul, Korea. I focus on the proliferation of approaches that combine human magnetic resonance imaging (MRI) with computational models and data acquired in other species using diverse imaging techniques. I propose that this integrative approach will help us overcome the measurement limitations of human MRI and open new scientific opportunities for the future.

Functions of the posterior cingulate cortex and default network

2025-06-20

Brett L. Foster , Seth R. Koslov | Current Opinion in Behavioral Sciences

Norepinephrine system dysconnectivity in major depressive disorder: the effect of short term SSRI treatment

2025-06-20

Shuting Chen , Jinqiang Zhang , Guowei Wu +7 more | European Neuropsychopharmacology

Aberrant Cortical-Subcortical-Cerebellar Connectivity in Resting-State fMRI as an Imaging Marker of Schizophrenia and Psychosis: A Systematic Review of Data-Driven Whole-Brain Functional Connectivity Analyses

2025-06-20

Kyle M. Jensen , Tricia Z. King , Pablo Andrés-Camazón +2 more | medRxiv (Cold Spring Harbor Laboratory)

Schizophrenia is extremely heterogenous, and the underlying brain mechanisms are not fully understood. Many attempts have been made to substantiate and delineate the relationship between schizophrenia and the brain through … Schizophrenia is extremely heterogenous, and the underlying brain mechanisms are not fully understood. Many attempts have been made to substantiate and delineate the relationship between schizophrenia and the brain through unbiased exploratory investigations of resting-state functional magnetic resonance imaging (rs-fMRI). The results of numerous data-driven rs-fMRI studies have converged in support of the disconnection hypothesis framework, reporting aberrant connectivity in cortical-subcortical-cerebellar circuitry. However, this model is vague and underspecified, encompassing a vast array of findings across studies. It is necessary to further refine this model to identify consistent patterns and establish stable imaging markers of schizophrenia and psychosis. The organizational structure of the NeuroMark atlas is especially well-equipped for describing functional units derived through independent component analysis (ICA) and uniting findings across studies utilizing data-driven whole-brain functional connectivity (FC) to characterize schizophrenia and psychosis. Towards this goal, a systematic literature review was conducted on primary empirical articles published in English in peer-reviewed journals between January 2019 - February 2025 which utilized cortical-subcortical-cerebellar terminology to describe schizophrenia-control comparisons of whole-brain FC in human rs-fMRI. The electronic databases utilized included Google scholar, PubMed, and APA PsycInfo, and search terms included ("schizophrenia" OR "psychosis") AND "resting-state fMRI" AND ("cortical-subcortical-cerebellar" OR "cerebello-thalamo-cortical"). 10 studies were identified and NeuroMark nomenclature was utilized to describe findings within a common reference space. The most consistent patterns included cerebellar-thalamic hypoconnectivity, cerebellar-cortical (sensorimotor & insular-temporal) hyperconnectivity, subcortical (basal ganglia & thalamic) - cortical (sensorimotor, temporoparietal, insular-temporal, occipitotemporal, & occipital) hyperconnectivity, and cortical-cortical (insular-temporal & occipitotemporal) hypoconnectivity. Patterns implicating prefrontal cortex are largely inconsistent across studies and may not be effective targets for establishing stable imaging markers based on static FC in rs-fMRI. Instead, adapting new analytical strategies, or focusing on nodes in the cerebellum, thalamus, and primary motor and sensory cortex may prove to be a more effective approach.

Bridging the epistemological divide in neuroscience to improve ontological clarity

2025-06-20

Giulia Baracchini , Eli J. Müller , James M. Shine | Aperture Neuro

Neuroscience as a field is relatively fragmented. This perspective highlights the epistemological divide that arises from the wide variety of different experimental approaches that we use, which in turn lead … Neuroscience as a field is relatively fragmented. This perspective highlights the epistemological divide that arises from the wide variety of different experimental approaches that we use, which in turn lead to ontological clashes in our understanding of brain function. We argue that overcoming these conceptual barriers requires fostering collaboration without sacrificing domain expertise. While interdisciplinary training and international cooperation offer promising avenues, practical challenges persist, such as the time investment required for dual specializations and the risk of diluted expertise. We propose leveraging shared data repositories and computational modelling frameworks to benchmark methodologies, facilitating a more coherent integration of findings across subfields. Additionally, we advocate for the creation of a structured “map” of neuroscience, charting relationships between domains to enhance conceptual clarity. By embracing these strategies, we can move toward a more unified, mature neuroscience capable of addressing fundamental questions about the brain with greater precision and coherence.

Human brain mapping at the Singularity

2025-06-20

Ariel Rokem | Aperture Neuro

Data-driven research has made significant headway within and for human brain mapping. Is this because we have reached some kind of mythical Singularity? Or is it the consequence of a … Data-driven research has made significant headway within and for human brain mapping. Is this because we have reached some kind of mythical Singularity? Or is it the consequence of a variety of socio-technical factors? Using David Donoho’s “Data Science at the Singularity” (2024) as a framework, I analyze the progress that we have seen in advancing frictionless reproducibility through open data, code re-execution and the adoption of the common task framework. I critique some of these ideas and point to issues with their implementation in human brain mapping. Finally, I point out some ideas and hopes for the future of data-driven research in neuroimaging.

The road to mapping neural circuits with fMRI: Challenges and advances

2025-06-20

Seong‐Gi Kim | Aperture Neuro

BOLD fMRI has significantly advanced our understanding of human brain function. To further progress in brain science, it is essential to interpret the causal relationships underlying fMRI data, moving beyond … BOLD fMRI has significantly advanced our understanding of human brain function. To further progress in brain science, it is essential to interpret the causal relationships underlying fMRI data, moving beyond simple correlations of brain activity. In this article, current challenges and recent advances in circuit analysis using animal fMRI are presented, along with discussion of their potential implications for human fMRI research.

Data-guided neuroimaging and visualization: From functional decomposition to dynamic fusion

2025-06-20

Vince D. Calhoun | Aperture Neuro

This invited contribution to the State of the Brain series reflects on several emerging and foundational themes that are shaping the future of brain mapping. As the field continues its … This invited contribution to the State of the Brain series reflects on several emerging and foundational themes that are shaping the future of brain mapping. As the field continues its rapid evolution–fueled by methodological innovation and increasingly powerful models–this paper argues for a principled approach grounded in “data fidelity,” resisting premature dimensionality reduction in favor of preserving rich, high-dimensional representations. I propose a structured framework for functional decomposition, classifying methods across three key dimensions: source (anatomical, functional, multimodal), mode (categorical, dimensional), and fit (predefined, data-driven, hybrid). Special emphasis is placed on hybrid approaches, such as the NeuroMark pipeline, which integrate spatial priors with data-driven refinement to boost sensitivity to individual differences while maintaining cross-subject generalizability. Beyond decomposition, we introduce the concept of expressive visualization–a paradigm that aims to surface meaningful patterns embedded in complex, dynamic NeuroAI models. I also discuss the role of leveraging higher order statistics, advances in modeling time-varying connectivity, and the promise of symmetric, dynamic multimodal fusion techniques. Collectively, these developments point to a future where neuroimaging analysis is not only more methodologically rigorous, but also more interpretable, scalable, and clinically impactful. In sum, I advocate for a continued focus on approaches allowing for high-dimensional yet informative spatiotemporal summaries of the data as we chart the next phase of discovery in brain science.