Aberrant Temporal-Spatial Complexity of Intrinsic Fluctuations in Major Depression

Abstract Accumulating evidence suggested that the brain is highly dynamic, thus investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis. We found reduced number of CAPs, as well as transitions between different CAPs of the DMN and CCN, in patients with MDD. These results suggested reduced variability and flexibility of these two brain networks in the patients. By contrast, we also found increased number of CAPs of the SN in the patients, indicating enhanced variability of the SN in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC and insula. More importantly, we showed that our findings were robust and reproducible with another independent data set. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.

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Neuronal cascades shape whole-brain functional dynamics at rest

10.1101/2020.12.25.424385 ◽

2020 ◽

Author(s):

Giovanni Rabuffo ◽

Jan Fousek ◽

Christophe Bernard ◽

Viktor Jirsa

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Blood Oxygen Level Dependent ◽

Brain Network ◽

Neural Activation ◽

Data Set ◽

Whole Brain ◽

Dynamic Component ◽

Leading Role ◽

Co Activation

AbstractAt rest, mammalian brains display a rich complex spatiotemporal behavior, which is reminiscent of healthy brain function and has provided nuanced understandings of several major neurological conditions. Despite the increasingly detailed phenomenological documentation of the brain’s resting state, its principle underlying causes remain unknown. To establish causality, we link structurally defined features of a brain network model to neural activation patterns and their variability. For the mouse, we use a detailed connectome-based model and simulate the resting state dynamics for neural sources and whole brain imaging signals (Blood-Oxygen-Level-Dependent (BOLD), Electroencephalography (EEG)). Under conditions of near-criticality, characteristic neuronal cascades form spontaneously and propagate through the network. The largest neuronal cascades produce short-lived but robust co-fluctuations at pairs of regions across the brain. During these co-activation episodes, long-lasting functional networks emerge giving rise to epochs of stable resting state networks correlated in time. Sets of neural cascades are typical for a resting state network, but different across. We experimentally confirm the existence and stability of functional connectivity epochs comprising BOLD co-activation bursts in mice (N=19). We further demonstrate the leading role of the neuronal cascades in a simultaneous EEG/fMRI data set in humans (N=15), explaining a large part of the variability of functional connectivity dynamics. We conclude that short-lived neuronal cascades are a major robust dynamic component contributing to the organization of the slowly evolving spontaneous fluctuations in brain dynamics at rest.

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Influence of Primary Auditory Cortex in the Characterization of Autism Spectrum in Young Adults using Brain Connectivity Parameters and Deep Belief Networks: An fMRI Study

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666191111142039 ◽

2020 ◽

Vol 16 (9) ◽

pp. 1059-1073

Author(s):

Vidhusha Srinivasan ◽

N. Udayakumar ◽

Kavitha Anandan

Keyword(s):

Functional Connectivity ◽

Auditory Cortex ◽

Language Processing ◽

Brain Connectivity ◽

Primary Auditory Cortex ◽

Autism Spectrum ◽

Belief Networks ◽

Deep Belief Networks ◽

High Functioning ◽

Low Functioning

Background: The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research. Objective: This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivity parameters and distinguishes the classes using deep belief networks. Methods: The task-based functional Magnetic Resonance Images (fMRI) of both high and low functioning autistic groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involved in a defined language processing task. The language processing regions of the brain, along with Default Mode Network (DMN) have been considered for the analysis. The functional connectivity maps have been plotted through graph theory procedures. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the individual groups. These parameters have been fed to Deep Belief Networks (DBN) to classify the subjects under consideration as either LFA or HFA. Results: Results showed increased functional connectivity in high functioning subjects. It was found that the additional interaction of the Primary Auditory Cortex lying in the temporal lobe, with other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for the low functioning group. Conclusion: Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been recognized. Therefore, this work that suggests the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating different groups in autism spectrum.

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Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

Neural Plasticity ◽

10.1155/2018/9354207 ◽

2018 ◽

Vol 2018 ◽

pp. 1-20 ◽

Cited By ~ 2

Author(s):

Alkinoos Athanasiou ◽

Nikos Terzopoulos ◽

Niki Pandria ◽

Ioannis Xygonakis ◽

Nicolas Foroglou ◽

...

Keyword(s):

Spinal Cord ◽

Spinal Cord Injury ◽

Functional Connectivity ◽

Motor Imagery ◽

Healthy Subjects ◽

Brain Connectivity ◽

Descending Pathways ◽

Brain Organization ◽

Sensorimotor Network ◽

Cord Injury

Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.

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Changes in functional connectivity associated with facial expression processing over the working adult lifespan

10.31234/osf.io/yrkbw ◽

2021 ◽

Author(s):

Thomas Murray ◽

Justin O'Brien ◽

Veena Kumari

Keyword(s):

Functional Connectivity ◽

Facial Expressions ◽

Brain Connectivity ◽

Negative Emotions ◽

Brain Regions ◽

Emotional Expressions ◽

Expression Recognition ◽

Frontal Pole ◽

Adult Lifespan ◽

Age Related

The recognition of negative emotions from facial expressions is shown to decline across the adult lifespan, with some evidence that this decline begins around middle age. While some studies have suggested ageing may be associated with changes in neural response to emotional expressions, it is not known whether ageing is associated with changes in the network connectivity associated with processing emotional expressions. In this study, we examined the effect of participant age on whole-brain connectivity to various brain regions that have been associated with connectivity during emotion processing: the left and right amygdalae, medial prefrontal cortex (mPFC), and right posterior superior temporal sulcus (rpSTS). The study involved healthy participants aged 20-65 who viewed facial expressions displaying anger, fear, happiness, and neutral expressions during functional magnetic resonance imaging (fMRI). We found effects of age on connectivity between the left amygdala and voxels in the occipital pole and cerebellum, between the right amygdala and voxels in the frontal pole, and between the rpSTS and voxels in the orbitofrontal cortex, but no effect of age on connectivity with the mPFC. Furthermore, ageing was more greatly associated with a decline in connectivity to the left amygdala and rpSTS for negative expressions in comparison to happy and neutral expressions, consistent with the literature suggesting a specific age-related decline in the recognition of negative emotions. These results add to the literature surrounding ageing and expression recognition by suggesting that changes in underlying functional connectivity might contribute to changes in recognition of negative facial expressions across the adult lifespan.

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Physiological and head motion signatures in static and time-varying functional connectivity and their subject discriminability

10.1101/2020.02.04.934554 ◽

2020 ◽

Cited By ~ 3

Author(s):

Alba Xifra-Porxas ◽

Michalis Kassinopoulos ◽

Georgios D. Mitsis

Keyword(s):

Functional Connectivity ◽

Brain Connectivity ◽

Brain Activity ◽

Large Data ◽

Head Motion ◽

Time Varying ◽

Resting State Networks ◽

Human Connectome Project ◽

Noninvasive Biomarker ◽

Recurrent Patterns

AbstractHuman brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

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Lynn_etal_MathSkills_CPM_Preprint_V1_05July2021

10.31234/osf.io/xp79b ◽

2021 ◽

Author(s):

Andrew Lynn ◽

Eric D. Wilkey ◽

Gavin Price

Keyword(s):

Functional Connectivity ◽

Predictive Modeling ◽

Brain Connectivity ◽

Network Connectivity ◽

Brain Regions ◽

Math Skills ◽

Whole Brain ◽

Building Models ◽

State Dependent ◽

Brain Behavior

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

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An Exploratory Pilot Study of Brain Activation and Functional Connectivity Induced by the “Goldberg” Variations 276 years after their Commission

Medical Problems of Performing Artists ◽

10.21091/mppa.2019.4030 ◽

2019 ◽

Vol 34 (4) ◽

pp. 191-197

Author(s):

Christof Karmonik ◽

Makiko Hirata ◽

Saba Elias ◽

J Todd Frazier

Keyword(s):

Pilot Study ◽

Functional Connectivity ◽

Brain Connectivity ◽

Brain Activation ◽

Johann Sebastian Bach ◽

Technical Development ◽

The Subject ◽

Goldberg Variations ◽

The Brain

Around 1741, composer Johann Sebastian Bach published a long and complicated keyboard piece, calling it Aria with diverse variations for a harpsichord with two manuals. It was the capstone of a publication project called German Clavier-Übung (Keyboard Practice) where Bach wanted to show what was possible at the keyboard in terms of technical development, virtuosic finesse and compositional sophistication. The music is meticulously patterned, beginning with a highly ornamented Aria, the bass line of which fuels the 30 variations that follow. The piece is clearly divided into two parts with the second half beginning with an overture with a fanfare opening, in variation 16. The piece ends as it begins, with the return of the Aria. Here, we present an investigation into activation and connectivity in the brain of a pianist, who listened to her own recording of the “Goldberg” variation while undergoing a fMRI examination. Similarity of brain connectivity is quantified and compared with the subjective scores provided by the subject.

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Resting-state brain network topological properties and the correlation with neuropsychological assessment in adolescent narcolepsy

SLEEP ◽

10.1093/sleep/zsaa018 ◽

2020 ◽

Vol 43 (8) ◽

Author(s):

Xiao Fulong ◽

Spruyt Karen ◽

Lu Chao ◽

Zhao Dianjiang ◽

Zhang Jun ◽

...

Keyword(s):

Functional Connectivity ◽

Neuropsychological Assessment ◽

Resting State ◽

Brain Connectivity ◽

Small World ◽

Theoretical Method ◽

Brain Network ◽

Topological Properties ◽

Depressive Feelings ◽

Network Properties

Abstract Study Objectives To evaluate functional connectivity and topological properties of brain networks, and to investigate the association between brain topological properties and neuropsychiatric behaviors in adolescent narcolepsy. Methods Resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessment were applied in 26 adolescent narcolepsy patients and 30 healthy controls. fMRI data were analyzed in three ways: group independent component analysis and a graph theoretical method were applied to evaluate topological properties within the whole brain. Lastly, network-based statistics was utilized for group comparisons in region-to-region connectivity. The relationship between topological properties and neuropsychiatric behaviors was analyzed with correlation analyses. Results In addition to sleepiness, depressive symptoms and impulsivity were detected in adolescent narcolepsy. In adolescent narcolepsy, functional connectivity was decreased between regions of the limbic system and the default mode network (DMN), and increased in the visual network. Adolescent narcolepsy patients exhibited disrupted small-world network properties. Regional alterations in the caudate nucleus (CAU) and posterior cingulate gyrus were associated with subjective sleepiness and regional alterations in the CAU and inferior occipital gyrus were associated with impulsiveness. Remodeling within the salience network and the DMN was associated with sleepiness, depressive feelings, and impulsive behaviors in narcolepsy. Conclusions Alterations in brain connectivity and regional topological properties in narcoleptic adolescents were associated with their sleepiness, depressive feelings, and impulsive behaviors.

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Individual Variation in Functional Brain Network Topography is Linked to Schizophrenia Symptomatology

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa088 ◽

2020 ◽

Author(s):

Uzma Nawaz ◽

Ivy Lee ◽

Adam Beermann ◽

Shaun Eack ◽

Matcheri Keshavan ◽

...

Keyword(s):

Individual Differences ◽

Functional Connectivity ◽

Negative Symptom ◽

Individual Variation ◽

Symptom Severity ◽

Spatial Organization ◽

Brain Connectivity ◽

Brain Network ◽

Brain Organization ◽

Functional Brain Network

Abstract Resting-state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia-negative symptom severity and network connectivity are actually due to individual differences in network spatial organization. Forty-four participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole-brain functional connectivity correlates with negative symptom severity at the individual voxel level. Brain connectivity to a region of the right dorsolateral prefrontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of 2 networks: the default mode network (DMN) and the task-positive network (TPN). Both networks demonstrate strong (r = ~0.49) and significant (P < .001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN. Previously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (eg, transcranial magnetic stimulation [TMS]).

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Brain development in attention deficit hyperactivity disorder: A neuroimaging perspective review

European Psychiatry ◽

10.1016/j.eurpsy.2016.01.1277 ◽

2016 ◽

Vol 33 (S1) ◽

pp. S357-S357 ◽

Cited By ~ 1

Author(s):

V. Pereira ◽

P. de Castro-Manglano ◽

C. Soutullo Esperon

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Functional Connectivity ◽

Brain Development ◽

Attention Deficit ◽

Default Mode Network ◽

Brain Connectivity ◽

Child And Adolescent Psychiatry ◽

Global Perspective ◽

Default Mode ◽

Hyperactivity Disorder

IntroductionAttention deficit hyperactivity disorder (ADHD) is a challenge in child and adolescent psychiatry. In the recent decades many studies with longitudinal designs have used neuroimaging with ADHD patients, suggesting its neurodevelopmental origin.ObjectivesStudy the findings of neuroimaging (MRI, fMRI, DTI, PET) techniques on ADHD patients from a longitudinal point of view, looking also for the potential influence of treatments and other predictors (i.e. genetics).AimsTo provide a global perspective of all the recent findings on ADHD patients with the neuroimaging technics, focusing on longitudinal measurements of the changes in brain development.MethodsWe conducted a review of the literature in the databases Pubmed and ScienceDirect (terms ADHD, neuroimaging, MRI, fMRI, DTI, PET, functional connectivity, metilphenidate and cortical thickness). We focused on studies using neuroimaging techniques with ADHD patients, looking at their populations, methodologies and results.ResultsThe studies found abnormalities in the structure of grey matter, activity and brain connectivity in many neural networks, with particular involvement of the fronto-parietal and Default Mode Network. There is also convergent evidence for white matter pathology and disrupted anatomical connectivity in ADHD. In addition, dysfunctional connectivity during rest and during cognitive tasks has been demonstrated.ConclusionsThis evidence describe ADHD as a brain development disorder, with delays and disruptions in the global development of the central nervous system that compromises grey and white matters, most evident in the prefrontal cortex, parietal and posterior cingulate cortices, as well as basal ganglia, damaging activity and structural and functional connectivity of various brain networks, especially the fronto-striato-parietal and default mode network.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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