The neural basis of attentional alterations in prenatally protein malnourished rats

Abstract Protein malnutrition during gestation alters brain development and produces specific behavioral and cognitive changes that persist into adulthood and increase the risks of neuropsychiatric disorders. Given evidence for the role of the prefrontal cortex in such diseases, it is significant that studies in humans and animal models have shown that prenatal protein malnutrition specifically affects functions associated with prefrontal cortex. However, the neural basis underlying these changes is unclear. In the current study, prenatally malnourished and control rats performed a sustained attention task with an unpredictable distractor, a task that depends on intact prefrontal cortical function. Radiolabeled 2-deoxyglucose was used to measure neural and brain network activity during the task. Results confirmed that adult prenatally malnourished rats were more distractible than controls and exhibited lower functional activity in prefrontal cortices. Thus, prefrontal activity was a predictor of task performance in controls but not prenatally malnourished animals. Instead, prenatally malnourished animals relied on different brain networks involving limbic structures such as the hippocampus. These results provide evidence that protein reduction during brain development has more wide-reaching effects on brain networks than previously appreciated, resulting in the formation of brain networks that may reflect compensatory responses in prenatally malnourished brains.

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Linking flexibility of brain networks to cognitive development in preschool children

10.1101/2020.03.24.005074 ◽

2020 ◽

Author(s):

Lily Chamakura ◽

Syed Naser Daimi ◽

Katsumi Watanabe ◽

Joydeep Bhattacharya ◽

Goutam Saha

Keyword(s):

Preschool Children ◽

Brain Development ◽

Cognitive Ability ◽

Temporal Variability ◽

Short Term Memory ◽

Brain Networks ◽

Brain Network ◽

Memory And Attention ◽

Functional Brain Networks ◽

Human Adults

AbstractRecent studies of functional connectivity networks (FCNs) suggest that the reconfiguration of brain network across time, both at rest and during task, is linked with cognition in human adults. In this study, we tested this prediction, i.e. cognitive ability is associated with a flexible brain network in preschool children of 3-4 years - a critical age, representing a ‘blossoming period’ for brain development. We recorded magnetoen-cephalogram (MEG) data from 88 preschoolers, and assessed their cognitive ability by a battery of cognitive tests. We estimated FCNs obtained from the source reconstructed MEG recordings, and characterized the temporal variability at each node using a novel path-based measure of temporal variability; the latter captures reconfiguration of the node’s interactions to the rest of the network across time. Using connectome predictive modeling, we demonstrated that the temporal variability of fronto-temporal nodes in the dynamic FCN can reliably predict out-of-scanner performance of short-term memory and attention distractability in novel participants. Further, we observed that the network-level temporal variability increased with age, while individual nodes exhibited an inverse relationship between temporal variability and node centrality. These results demonstrate that functional brain networks, and especially their reconfiguration ability, are important to cognition at an early but a critical stage of human brain development.

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Mapping human brain networks with cortico-cortical evoked potentials

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2013.0528 ◽

2014 ◽

Vol 369 (1653) ◽

pp. 20130528 ◽

Cited By ~ 77

Author(s):

Corey J. Keller ◽

Christopher J. Honey ◽

Pierre Mégevand ◽

Laszlo Entz ◽

Istvan Ulbert ◽

...

Keyword(s):

Cerebral Cortex ◽

Cognitive Processing ◽

Cognitive Abilities ◽

Large Scale ◽

Effective Connectivity ◽

Brain Networks ◽

Brain Network ◽

Fundamental Aspect ◽

Neural Basis ◽

Network Function

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex.

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Brain Network Dysfunction in Bipolar Disorder

CNS Spectrums ◽

10.1017/s1092852900020800 ◽

2006 ◽

Vol 11 (4) ◽

pp. 312-320 ◽

Cited By ~ 70

Author(s):

Caleb M. Adler ◽

Melissa P. DelBello ◽

Stephen M. Strakowski

Keyword(s):

Bipolar Disorder ◽

Brain Networks ◽

Brain Network ◽

Brain Lesions ◽

Morbidity And Mortality ◽

Emergent Properties ◽

Psychiatric Condition ◽

Neural Basis ◽

Recent Advances ◽

Neuroimaging Techniques

ABSTRACTBipolar disorder is a common psychiatric condition with significant associated morbidity and mortality. Despite its significance, the neurophysiology and neuropathology of this illness is incompletely understood. Recent advances in neuroimaging techniques have helped to begin clarifying these areas. Specifically, bipolar disorder appears to arise from abnormalities within discrete brain networks (eg, the anterior limbic network). The expression of the symptoms of bipolar disorder does not appear to result from single, localized brain lesions, but rather are emergent properties of dysfunction of these brain networks. As neuroimaging techniques continue to improve, the underlying neural basis of bipolar disorder will be clarified.

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Investigating the interaction between white matter and brain state on tDCS-induced changes in brain network activity

10.1101/2020.10.09.332742 ◽

2020 ◽

Author(s):

Danielle L. Kurtin ◽

Ines R. Violante ◽

Karl Zimmerman ◽

Robert Leech ◽

Adam Hampshire ◽

...

Keyword(s):

White Matter ◽

Brain Activity ◽

Inferior Frontal Gyrus ◽

Brain Networks ◽

Brain Network ◽

Network Activity ◽

Brain State ◽

State Dependent ◽

Cathodal Tdcs ◽

The Right

AbstractBackgroundTranscranial direct current stimulation (tDCS) is a form of noninvasive brain stimulation whose potential as a cognitive therapy is hindered by our limited understanding of how participant and experimental factors influence its effects. Using functional MRI to study brain networks, we have previously shown in healthy controls that the physiological effects of tDCS are strongly influenced by brain state. We have additionally shown, in both healthy and traumatic brain injury (TBI) populations, that the behavioral effects of tDCS are positively correlated with white matter (WM) structure.ObjectivesIn this study we investigate how these two factors, WM structure and brain state, interact to shape the effect of tDCS on brain network activity.MethodsWe applied anodal, cathodal and sham tDCS to the right inferior frontal gyrus (rIFG) of healthy (n=22) and TBI participants (n=34). We used the Choice Reaction Task (CRT) performance to manipulate brain state during tDCS. We acquired simultaneous fMRI to assess activity of cognitive brain networks and used Fractional Anisotropy (FA) as a measure of WM structure.ResultsWe find that the effects of tDCS on brain network activity in TBI participants are highly dependent on brain state, replicating findings from our previous healthy control study in a separate, patient cohort. We then show that WM structure further modulates the brain-state dependent effects of tDCS on brain network activity. These effects are not unidirectional – in the absence of task with anodal and cathodal tDCS, FA is positively correlated with brain activity in several regions of the default mode network. Conversely, with cathodal tDCS during CRT performance, FA is negatively correlated with brain activity in a salience network region.ConclusionsOur results show that experimental and participant factors interact to have unexpected effects on brain network activity, and that these effects are not fully predictable by studying the factors in isolation.

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Sex Differences in Functional Brain Networks for Language

Cerebral Cortex ◽

10.1093/cercor/bhz184 ◽

2019 ◽

Vol 30 (3) ◽

pp. 1528-1537 ◽

Cited By ~ 3

Author(s):

Min Xu ◽

Xiuling Liang ◽

Jian Ou ◽

Hong Li ◽

Yue-jia Luo ◽

...

Keyword(s):

Sex Differences ◽

Brain Connectivity ◽

Brain Networks ◽

Brain Network ◽

Great Promise ◽

Neural Basis ◽

Men And Women ◽

Functional Brain ◽

Brain Functions ◽

The Brain

Abstract Men and women process language differently, but how the brain functions to support this difference is poorly understood. A few studies reported sex influences on brain activation for language, whereas others failed to detect the difference at the functional level. Recent advances of brain network analysis have shown great promise in picking up brain connectivity differences between sexes, leading us to hypothesize that the functional connections among distinct brain regions for language may differ in males and females. To test this hypothesis, we scanned 58 participants’ brain activities (28 males and 30 females) in a semantic decision task using functional magnetic resonance imaging. We found marked sex differences in dynamic interactions among language regions, as well as in functional segregation and integration of brain networks during language processing. The brain network differences were further supported by a machine learning analysis that accurately discriminated males from females using the multivariate patterns of functional connectivity. The sex-specific functional brain connectivity may constitute an essential neural basis for the long-held notion that men and women process language in different ways. Our finding also provides important implications for sex differences in the prevalence of language disorders, such as dyslexia and stuttering.

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Irritability Is Associated With Decreased Cortical Surface Area and Anxiety With Decreased Gyrification During Brain Development

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.744419 ◽

2021 ◽

Vol 12 ◽

Author(s):

Camille Piguet ◽

Angeline Mihailov ◽

Antoine Grigis ◽

Charles Laidi ◽

Edouard Duchesnay ◽

...

Keyword(s):

Prefrontal Cortex ◽

Anxiety Disorders ◽

Brain Development ◽

Surface Area ◽

Structural Changes ◽

Brain Network ◽

Anterior Cingulate ◽

Cortical Surface ◽

Mood And Anxiety Disorders ◽

Gyrification Index

Background: Brain development is of utmost importance for the emergence of psychiatric disorders, as the most severe of them arise before 25 years old. However, little is known regarding how early transdiagnostic symptoms, in a dimensional framework, are associated with cortical development. Anxiety and irritability are central vulnerability traits for subsequent mood and anxiety disorders. In this study, we investigate how these dimensions are related to structural changes in the brain to understand how they may increase the transition risk to full-blown disorders.Methods: We used the opportunity of an open access developmental cohort, the Healthy Brain Network, to investigate associations between cortical surface markers and irritability and anxiety scores as measured by parents and self-reports.Results: We found that in 658 young people (with a mean age of 11.6) the parental report of irritability is associated with decreased surface area in the bilateral rostral prefrontal cortex and the precuneus. Furthermore, parental reports of anxiety were associated with decreased local gyrification index in the anterior cingulate cortex and dorsomedial prefrontal cortex.Conclusions: These results are consistent with current models of emotion regulation network maturation, showing decreased surface area or gyrification index in regions associated with impaired affective control in mood and anxiety disorders. Our results highlight how dimensional traits may increase vulnerability for these disorders.

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Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

10.1101/773895 ◽

2019 ◽

Author(s):

João F. Guassi Moreira ◽

Katie A. McLaughlin ◽

Jennifer A. Silvers

Keyword(s):

Emotion Regulation ◽

Skill Acquisition ◽

Network Architecture ◽

Evolutionary Biology ◽

Brain Activity ◽

Brain Networks ◽

Adult Life ◽

Brain Network ◽

Network Activity ◽

Whole Brain

AbstractThe ability to regulate emotions is key to goal attainment and wellbeing. Although much has been discovered about how the human brain develops to support the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework in conjunction with machine learning to: (i) parse the neural underpinnings of emotion regulation skill acquisition while accounting for age, and (ii) build a working taxonomy of brain network activity supporting emotion regulation in a sample of youth (N = 70, 34 female). We were able to predict emotion regulation ability, but not age, using network activity metrics from whole-brain networks during an emotion regulation task. Further, by leveraging analytic techniques traditionally used in evolutionary biology (e.g., cophenetic correlations), we were able to demonstrate that brain networks evince reliable taxonomic organization to meet emotion regulation demands in youth. This work shows that meaningful information about emotion regulation development is encoded in whole-brain network activity, suggesting that brain activity during emotion regulation encodes unique information about regulatory skill acquisition in youth but not domain-general maturation.Significance StatementThe acquisition of emotion regulation is critical for healthy functioning in later adult life. To date, little is known about how brain networks support the developmental acquisition of emotion regulation skills. This is noteworthy because brain networks have been increasingly shown to provide highly useful information about neural activity. Here we show that brain activity during an emotion regulation task encodes information about regulatory abilities over and above age. These results suggest emotion regulation skills are dependent on neural specialization of domain-specific systems, whereas age is encoded via domain-general systems.

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Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

10.1101/509802 ◽

2019 ◽

Author(s):

Yongjie Zhu ◽

Chi Zhang ◽

Petri Toiviainen ◽

Minna Huotilainen ◽

Klaus Mathiak ◽

...

Keyword(s):

Large Scale ◽

Brain Activity ◽

Brain Networks ◽

Brain Network ◽

Network Activity ◽

Sensor Data ◽

Frequency Dependent ◽

Feature Processing ◽

Ongoing Eeg ◽

Musical Feature

AbstractRecently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method that combined music information retrieval with spatial Independent Components Analysis (ICA) to probe the interplay between the spatial profiles and the spectral patterns. We projected the sensor data into cortical space using a minimum-norm estimate and applied the Short Time Fourier Transform (STFT) to obtain frequency information. Then, spatial ICA was made to extract spatial-spectral-temporal information of brain activity in source space and five long-term musical features were computationally extracted from the naturalistic stimuli. The spatial profiles of the components whose temporal courses were significantly correlated with musical feature time series were clustered to identify reproducible brain networks across the participants. Using the proposed approach, we found brain networks of musical feature processing are frequency-dependent and three plausible frequency-dependent networks were identified; the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.

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