scholarly journals Maternal Protection in Childhood is Associated with Amygdala Reactivity and Structural Connectivity in Adulthood

2019 ◽  
Author(s):  
Madeline J. Farber ◽  
M. Justin Kim ◽  
Annchen R. Knodt ◽  
Ahmad R. Hariri
Keyword(s):  
Functional Connectivity ◽  
Facial Expressions ◽  
Structural Integrity ◽  
Paternal Care ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Controlling Parenting ◽  
Interpersonal Threat ◽  
Maternal Protection ◽  
Amygdala Reactivity

ABSTRACTRecently, we reported that variability in early-life caregiving experiences maps onto individual differences in threat-related brain function. Specifically, we found that greater familial affective responsiveness is associated with increased amygdala reactivity to interpersonal threat, particularly in adolescents having experienced relatively low recent stress. Here, we conceptually replicate and extend on our previous work to provide further evidence that subtle variability in specific features of early caregiving shapes structural and functional connectivity between the amygdala and medial prefrontal cortex (mPFC) in a cohort of 312 young adult volunteers. Multiple regression analyses revealed that participants who reported higher maternal but not paternal protection exhibited increased amygdala reactivity to explicit signals of interpersonal threat (i.e., angry facial expressions) but not implicit signals of broad environmental threat (i.e., fearful facial expressions). While amygdala functional connectivity with regulatory regions of the mPFC was not significantly associated with maternal protection, participants who reported higher maternal protection exhibited relatively decreased structural integrity of the uncinate fasciculus (UF), a white matter tract connecting these same brain regions. The observed associations were independent of the potential confounding influences of participant sex, socioeconomic status, and self-reported childhood trauma. There were no significant associations between structural or functional brain measures and either maternal or paternal care ratings. These findings suggest that an over controlling parenting style in mothers during childhood is associated with functional and structural alterations of brain regions involved in generating and regulating responses to threat in young adulthood.

scholarly journals Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

2021 ◽  
Author(s):  
David Pascucci ◽  
Maria Rubega ◽  
Joan Rue-Queralt ◽  
Sebastien Tourbier ◽  
Patric Hagmann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Meta Analysis ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Noise Perturbation ◽  
Eeg Data ◽  
Functional Brain Networks ◽  
Anatomical Priors ◽  
And Diffusion

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited, especially for electrophysiological data. In the present work, we propose a new linear adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. Our results show that SC priors increase the resilience of FC estimates to noise perturbation while promoting sparser networks under biologically plausible constraints. The proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new method for multimodal imaging and dynamic FC analysis.

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

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.

scholarly journals Unique mapping of structural and functional connectivity on cognition

2018 ◽  
Author(s):  
J. Zimmermann ◽  
J.G. Griffiths ◽  
A.R. McIntosh
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Unique Mapping ◽  
Human Connectome Project ◽  
Behavioural Patterns ◽  
Wide Range ◽  
Psychological Tasks ◽  
New Evidence

AbstractThe unique mapping of structural and functional brain connectivity (SC, FC) on cognition is currently not well understood. It is not clear whether cognition is mapped via a global connectome pattern or instead is underpinned by several sets of distributed connectivity patterns. Moreover, we also do not know whether the pattern of SC and of FC that underlie cognition are overlapping or distinct. Here, we study the relationship between SC and FC and an array of psychological tasks in 609 subjects from the Human Connectome Project (HCP). We identified several sets of connections that each uniquely map onto different aspects of cognitive function. We found a small number of distributed SC and a larger set of cortico-cortical and cortico-subcortical FC that express this association. Importantly, SC and FC each show unique and distinct patterns of variance across subjects and differential relationships to cognition. The results suggest that a complete understanding of connectome underpinnings of cognition calls for a combination of the two modalities.Significance StatementStructural connectivity (SC), the physical white-matter inter-regional pathways in the brain, and functional connectivity (FC), the temporal co-activations between activity of brain regions, have each been studied extensively. Little is known, however, about the distribution of variance in connections as they relate to cognition. Here, in a large sample of subjects (N = 609), we showed that two sets of brain-behavioural patterns capture the correlations between SC, and FC with a wide range of cognitive tasks, respectively. These brain-behavioural patterns reveal distinct sets of connections within the SC and the FC network and provide new evidence that SC and FC each provide unique information for cognition.

scholarly journals Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics

2021 ◽  
Vol 118 (46) ◽  
pp. e2109380118
Author(s):  
Maria Pope ◽  
Makoto Fukushima ◽  
Richard F. Betzel ◽  
Olaf Sporns
Keyword(s):  
Functional Connectivity ◽  
Functional Neuroimaging ◽  
Empirical Studies ◽  
Structural Connectivity ◽  
Spatial Arrangement ◽  
High Amplitude ◽  
Brain Regions ◽  
Brain Dynamics ◽  
Modular Organization ◽  
Fine Scale

The topology of structural brain networks shapes brain dynamics, including the correlation structure of brain activity (functional connectivity) as estimated from functional neuroimaging data. Empirical studies have shown that functional connectivity fluctuates over time, exhibiting patterns that vary in the spatial arrangement of correlations among segregated functional systems. Recently, an exact decomposition of functional connectivity into frame-wise contributions has revealed fine-scale dynamics that are punctuated by brief and intermittent episodes (events) of high-amplitude cofluctuations involving large sets of brain regions. Their origin is currently unclear. Here, we demonstrate that similar episodes readily appear in silico using computational simulations of whole-brain dynamics. As in empirical data, simulated events contribute disproportionately to long-time functional connectivity, involve recurrence of patterned cofluctuations, and can be clustered into distinct families. Importantly, comparison of event-related patterns of cofluctuations to underlying patterns of structural connectivity reveals that modular organization present in the coupling matrix shapes patterns of event-related cofluctuations. Our work suggests that brief, intermittent events in functional dynamics are partly shaped by modular organization of structural connectivity.

scholarly journals Using structural connectivity to augment community structure in EEG functional connectivity

2020 ◽  
Vol 4 (3) ◽  
pp. 761-787 ◽  
Author(s):  
Katharina Glomb ◽  
Emeline Mullier ◽  
Margherita Carboni ◽  
Maria Rubega ◽  
Giannarita Iannotti ◽  
...  
Keyword(s):  
Community Structure ◽  
White Matter ◽  
Functional Connectivity ◽  
Large Scale ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Source Analysis ◽  
Volume Conduction ◽  
Functional Relationships ◽  
The Impact

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

scholarly journals Repetitive transcranial magnetic stimulation restores altered functional connectivity of central poststroke pain model monkeys

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoshinori Kadono ◽  
Keigo Koguchi ◽  
Ken-ichi Okada ◽  
Koichi Hosomi ◽  
Motoki Hiraishi ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Functional Connectivity ◽  
Motor Cortex ◽  
Magnetic Stimulation ◽  
Primary Motor Cortex ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Functional Changes ◽  
Therapeutic Mechanism

AbstractCentral poststroke pain (CPSP) develops after a stroke around the somatosensory pathway. CPSP is hypothesized to be caused by maladaptive reorganization between various brain regions. The treatment for CPSP has not been established; however, repetitive transcranial magnetic stimulation (rTMS) to the primary motor cortex has a clinical effect. To verify the functional reorganization hypothesis for CPSP development and rTMS therapeutic mechanism, we longitudinally pursued the structural and functional changes of the brain by using two male CPSP model monkeys (Macaca fuscata) developed by unilateral hemorrhage in the ventral posterolateral nucleus of the thalamus. Application of rTMS to the ipsilesional primary motor cortex relieved the induced pain of the model monkeys. A tractography analysis revealed a decrease in the structural connectivity in the ipsilesional thalamocortical tract, and rTMS had no effect on the structural connectivity. A region of interest analysis using resting-state functional magnetic resonance imaging revealed inappropriately strengthened functional connectivity between the ipsilesional mediodorsal nucleus of the thalamus and the amygdala, which are regions associated with emotion and memory, suggesting that this may be the cause of CPSP development. Moreover, rTMS normalizes this strengthened connectivity, which may be a possible therapeutic mechanism of rTMS for CPSP.

scholarly journals Multimodal Classification of Mild Traumatic Brain Injury Reveals Local Coupling Between Structural and Functional Connectomes

2021 ◽  
Author(s):  
Ajay Peddada ◽  
Kevin Holly ◽  
Tejaswi D Sudhakar ◽  
Christina Ledbetter ◽  
Christopher E. Talbot ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Temporal Lobe ◽  
Resting State ◽  
Classification Accuracy ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Group Differences ◽  
Functional Connections ◽  
White Matter Tractography

Background: Following mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction . Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy. Methods: 16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation. Results: Group comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity. Conclusions: Our multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.

scholarly journals Surgically disconnected temporal pole exhibits resting functional connectivity with remote brain regions

2017 ◽  
Author(s):  
David E. Warren ◽  
Matthew J. Sutterer ◽  
Joel Bruss ◽  
Taylor J. Abel ◽  
Andrew Jones ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Structural Connectivity ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Bold Signal ◽  
Disease States ◽  
Behavior Development ◽  
Temporal Pole ◽  
Relationship Of

AbstractFunctional connectivity, as measured by resting-state fMRI, has proven a powerful method for studying brain systems in the context of behavior, development, and disease states. However, the relationship of functional connectivity to structural connectivity remains unclear. If functional connectivity relies on structural connectivity, then anatomical isolation of a brain region should eliminate functional connectivity with other brain regions. We tested this by measuring functional connectivity of the surgically disconnected temporal pole in resection patients (N=5; mean age 37; 2F, 3M). Functional connectivity was evaluated based on coactivation of whole-brain fMRI data with the average low-frequency BOLD signal from disconnected tissue in each patient. In sharp contrast to our prediction, we observed significant functional connectivity between the disconnected temporal pole and remote brain regions in each disconnection case. These findings raise important questions about the neural bases of functional connectivity measures derived from the fMRI BOLD signal.

scholarly journals New graph-theoretical-multimodal approach using temporal and structural correlations reveal disruption in the thalamo-cortical network in patients with schizophrenia

2018 ◽  
Author(s):  
Paolo Finotteli ◽  
Caroline Garcia Forlim ◽  
Paolo Dulio ◽  
Leonie Klock ◽  
Alessia Pini ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cortical Network ◽  
Future Research ◽  
Connectivity Matrix

Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modelled independently: functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural and topological information of MRI measurements (structural and resting state imaging) to patients with schizophrenia (N=35) and matched healthy individuals (N=41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole brain connectivity including functional, structural and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting state fMRI, and can be applied to EEG, MEG etc.

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