scholarly journals Evaluation of functional connectivity in subdivisions of the thalamus in schizophrenia

2019 ◽  
Vol 214 (5) ◽  
pp. 288-296 ◽  
Author(s):  
Jinnan Gong ◽  
Cheng Luo ◽  
Xiangkui Li ◽  
Sisi Jiang ◽  
Budhachandra S. Khundrakpam ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Anterior Cingulate ◽  
Data Driven ◽  
Thalamic Nuclei ◽  
Functional Connections ◽  
Duration Of Illness ◽  
Wide Range ◽  
Magnetic Resonance Imaging Mri ◽  
White Matter Tractography

BackgroundPrevious studies in schizophrenia revealed abnormalities in the cortico-cerebellar-thalamo-cortical circuit (CCTCC) pathway, suggesting the necessity for defining thalamic subdivisions in understanding alterations of brain connectivity.AimsTo parcellate the thalamus into several subdivisions using a data-driven method, and to evaluate the role of each subdivision in the alterations of CCTCC functional connectivity in patients with schizophrenia.MethodThere were 54 patients with schizophrenia and 42 healthy controls included in this study. First, the thalamic structural and functional connections computed, based on diffusion magnetic resonance imaging (MRI, white matter tractography) and resting-state functional MRI, were clustered to parcellate thalamus. Next, functional connectivity of each thalamus subdivision was investigated, and the alterations in thalamic functional connectivity for patients with schizophrenia were inspected.ResultsBased on the data-driven parcellation method, six thalamic subdivisions were defined. Loss of connectivity was observed between several thalamic subdivisions (superior-anterior, ventromedial and dorsolateral part of the thalamus) and the sensorimotor system, anterior cingulate cortex and cerebellum in patients with schizophrenia. A gradual pattern of dysconnectivity was observed across the thalamic subdivisions. Additionally, the altered connectivity negatively correlated with symptom scores and duration of illness in individuals with schizophrenia.ConclusionsThe findings of the study revealed a wide range of thalamic functional dysconnectivity in the CCTCC pathway, increasing our understanding of the relationship between the CCTCC pathway and symptoms associated with schizophrenia, and further indicating a potential alteration pattern in the thalamic nuclei in people with schizophrenia.Declaration of interestNone.

scholarly journals Regional abnormality of functional connectivity is associated with clinical manifestations in individuals with intractable focal epilepsy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yasuo Nakai ◽  
Hiroki Nishibayashi ◽  
Tomohiro Donishi ◽  
Masaki Terada ◽  
Naoyuki Nakao ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Focal Epilepsy ◽  
Clinical Manifestations ◽  
Eigenvector Centrality ◽  
Functional Connections ◽  
Duration Of Illness ◽  
Healthy Control ◽  
Matched Data ◽  
Clinical Conditions ◽  
Age And Sex

AbstractWe explored regional functional connectivity alterations in intractable focal epilepsy brains using resting-state functional MRI. Distributions of the network parameters (corresponding to degree and eigenvector centrality) measured at each brain region for all 25 patients were significantly different from age- and sex-matched control data that were estimated by a healthy control dataset (n = 582, 18–84 years old). The number of abnormal regions whose parameters exceeded the mean + 2 SD of age- and sex-matched data for each patient were associated with various clinical parameters such as the duration of illness and seizure severity. Furthermore, abnormal regions for each patient tended to have functional connections with each other (mean ± SD = 58.6 ± 20.2%), the magnitude of which was negatively related to the quality of life. The abnormal regions distributed within the default mode network with significantly higher probability (p < 0.05) in 7 of 25 patients. We consider that the detection of abnormal regions by functional connectivity analysis using a large number of control datasets is useful for the numerical assessment of each patient’s clinical conditions, although further study is necessary to elucidate etiology-specific abnormalities.

scholarly journals Cross-sex hormone treatment and own-body perception: behavioral and brain connectivity profiles

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Behzad S. Khorashad ◽  
Amirhossein Manzouri ◽  
Jamie D. Feusner ◽  
Ivanka Savic
Keyword(s):  
Brain Connectivity ◽  
Hormone Treatment ◽  
Sex Hormone ◽  
Anterior Cingulate ◽  
Angular Gyrus ◽  
Group Differences ◽  
Body Perception ◽  
Functional Connections ◽  
Before And After ◽  
Superior Parietal Cortex

AbstractReferrals for gender dysphoria (GD), characterized by a distressful incongruence between gender identity and at-birth assigned sex, are steadily increasing. The underlying neurobiology, and the mechanisms of the often-beneficial cross-sex hormone treatment are unknown. Here, we test hypothesis that own body perception networks (incorporated in the default mode network—DMN, and partly in the salience network—SN), are different in trans-compared with cis-gender persons. We also investigate whether these networks change with cross-sex hormone treatment. Forty transmen (TrM) and 25 transwomen (TrW) were scanned before and after cross-sex hormone institution. We used our own developed Body Morph test (BM), to assess the perception of own body as self. Fifteen cisgender persons were controls. Within and between-group differences in functional connectivity were calculated using independent components analysis within the DMN, SN, and motor network (a control network). Pretreatment, TrM and TrW scored lower “self” on the BM test than controls. Their functional connections were weaker in the anterior cingulate-, mesial prefrontal-cortex (mPFC), precuneus, the left angular gyrus, and superior parietal cortex of the DMN, and ACC in the SN “Self” identification and connectivity in the mPFC in both TrM and TrW increased from scan 1 to 2, and at scan 2 no group differences remained. The neurobiological underpinnings of GD seem subserved by cerebral structures composing major parts of the DMN.

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 Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large-Scale Neuronal Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Alessio Boschi ◽  
Martina Brofiga ◽  
Paolo Massobrio
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Neuronal Networks ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Synthetic Data ◽  
Connectivity Matrix ◽  
Causal Connection ◽  
Topological Properties ◽  
Functional Connections

The identification of the organization principles on the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. If structural connectivity is based on the detection of the morphological (synaptically mediated) links among neurons, functional and effective relationships derive from the recording of the patterns of electrophysiological activity (e.g., spikes, local field potentials). Correlation or information theory-based algorithms are typical routes pursued to find statistical dependencies and to build a functional connectivity matrix. As long as the matrix collects the possible associations among the network nodes, each interaction between the neuron i and j is different from zero, even though there was no morphological, statistical or causal connection between them. Hence, it becomes essential to find and identify only the significant functional connections that are predictive of the structural ones. For this reason, a robust, fast, and automatized procedure should be implemented to discard the “noisy” connections. In this work, we present a Double Threshold (DDT) algorithm based on the definition of two statistical thresholds. The main goal is not to lose weak but significant links, whose arbitrary exclusion could generate functional networks with a too small number of connections and altered topological properties. The algorithm allows overcoming the limits of the simplest threshold-based methods in terms of precision and guaranteeing excellent computational performances compared to shuffling-based approaches. The presented DDT algorithm was compared with other methods proposed in the literature by using a benchmarking procedure based on synthetic data coming from the simulations of large-scale neuronal networks with different structural topologies.

scholarly journals Sex-Related Neurocircuitry Supporting Camouflaging in Adults with Autism: Female Protection Insights

2021 ◽  
Author(s):  
Melissa Walsh ◽  
Broc Pagni ◽  
Leanna Monahan ◽  
Shanna Delaney ◽  
Christopher J Smith ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Control ◽  
Emotion Recognition ◽  
Brain Connectivity ◽  
Data Driven ◽  
Sex Bias ◽  
Adults With Autism ◽  
Connectivity Patterns ◽  
Females With Autism

Background: The male preponderance in autism led to the hypothesis that aspects of female biology are protective against autism. Females with autism report engaging in more compensatory behaviors (i.e., camouflaging) to overcome autism-related social differences, which may be a downstream result of protective pathways. No studies have examined sex-related brain pathways supporting camouflaging in females with autism, despite its potential to inform mechanisms underlying the sex bias in autism. Methods: This study included 45 non-intellectually-disabled adults with autism (male/female: 21/24) and 40 neurotypical adults (male/female: 19/21) ages 18-71. We used group multivariate voxel pattern analysis to conduct a data-driven, connectome-wide characterization of "sex-atypical" (sex-by-diagnosis) and "sex-typical" (sex) brain functional connectivity features linked to camouflaging, and validated findings in females with autism multi-modally via structural connectometry. Exploratory associations with cognitive control, memory, emotion recognition, and depression/anxiety examined the adaptive nature of functional connectivity patterns supporting camouflaging in females with autism. Results: We found 1) "sex-atypical" functional connectivity patterns predicting camouflaging in the hypothalamus and precuneus and 2) "sex-typical" patterns in the anterior cingulate and right anterior parahippocampus. Higher hypothalamic functional connectivity with a limbic reward cluster was the strongest predictor of camouflaging in females with autism (a "sex-atypical" pattern), and also predicted better cognitive control/emotion recognition. Structural connectometry validated functional connectivity results with consistent brain pathways/effect patterns implicated across multi-modal findings in females with autism. Conclusion: This data-driven, connectome-wide characterization of "sex-atypical" and "sex-typical" brain connectivity features supporting compensatory social behavior in autism suggests hormones may play a role in the autism sex bias. Furthermore, both "male-typical" and "female-typical" brain connectivity patterns are implicated in camouflaging in females with autism in circuits associated with reward, emotion, and memory processing. "Sex-atypical" results are consistent with the fetal steroidogenic hypothesis, which would result in masculinized brain features in females with autism. However, female genetics/biology may contribute to "female-typical" patterns implicated in camouflaging.

scholarly journals Auricular transcutaneous vagus nerve stimulation acutely modulates brain connectivity in mice

2021 ◽  
Author(s):  
Cecilia Brambilla Pisoni ◽  
Emma Munoz Moreno ◽  
Ianire Gallego Amaro ◽  
Rafael Maldonado ◽  
Antoni Ivorra ◽  
...  
Keyword(s):  
Vagus Nerve ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Familiarization Phase ◽  
Functional Connections ◽  
Cognitive Improvement ◽  
Transcutaneous Vagus Nerve Stimulation ◽  
Wide Range

Background: Brain electrical stimulation techniques take advantage of the intrinsic plasticity of the nervous system, opening a wide range of therapeutic applications. Vagus nerve stimulation (VNS) is an approved adjuvant for drug-resistant epilepsy and depression. Its non-invasive form, auricular transcutaneous VNS (atVNS), is under investigation for applications, including cognitive improvement. Objective: We aimed to study the effects of atVNS on brain connectivity, under conditions that improved memory persistence in CD-1 male mice. Methods: Acute atVNS in the cymba conchae of the left ear was performed using a standard stimulation protocol under light isoflurane anesthesia, immediately or 3 h after the training/familiarization phase of the novel object-recognition memory test (NORT). Another cohort of mice was used for bilateral c-Fos analysis after atVNS administration. Spearman correlation of c-Fos density between each pair of the thirty brain regions analyzed allowed obtaining the network of significant functional connections in stimulated and non-stimulated control brains. Results: NORT performance was enhanced when atVNS was delivered just after, but not 3 h after, the familiarization phase of the task. No alterations in c-Fos density were associated to electrostimulation, but a significant effect of atVNS was observed on c-Fos- based functional connectivity. atVNS induced a clear reorganization of the network, increasing the inter-hemisphere connections and the connectivity of locus coeruleus. Conclusion: Our results provide new insights in the effects of atVNS on memory performance and brain connectivity extending our knowledge of the biological mechanisms of bioelectronics in medicine.

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 Group-ICA with Functional Connectivity During Inhibition Control in Young Adults With Autistic-like Traits: an fMRI Study of a Stop-Signal Task

2020 ◽  
Author(s):  
Yaxu Yu ◽  
Li He ◽  
qiu jiang
Keyword(s):  
Young Adults ◽  
Functional Connectivity ◽  
Control Function ◽  
Brain Connectivity ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Functional Connections ◽  
The Difference ◽  
Group Ica ◽  
Inhibition Control

Abstract BackgroundFew studies explored response inhibition in autistic-like traits people, using task fMRI. In this study, we examine the functional connectivity of the brain using a stop-signal task based on fMRI among young adults with autistic-like traits and investigated their ability to achieve inhibition control.Methods29 of Chinese individuals measured with AQ. Then applied stop signal task to explore the difference in brain functional connectivity in individuals with autistic-like traits.ResultsThe results showed autistic-like traits people the longer the SSRT, the worse the inhibition ability. And we used networks obtained from groupICA analysis at the functional connectivity analysis level, the SN had a negative connection with left SMG; the DAN had a negative connection with left LG; the FPN had a positive connection with left PCG; the LN had a positive connection with vermis 4 5 and negative connection with left ITG. Furthermore, the SMG, LG, PCG, and temporal gyrus were also obtained in ROI-to-ROI analysis.LimitationsOur sample size smaller, still need to multicenter, large sample confirmed this conclusion. We want to use more task paradigms to explore inhibition control in autistic-like traits people.ConclusionsWe found that autistic-like traits people had atypical functional connectivity within brain networks in the SN, DAN, FPN, and LN, and had atypical brain areas centered on the SMG, LG, PCG, and temporal gyrus. And also highlight the importance of considering executive control function of whole-brain functional connections to better characterize brain connectivity in young adults with autistic-like traits.

scholarly journals Functional connectivity predicts the dispositional use of expressive suppression but not cognitive reappraisal

2019 ◽  
Author(s):  
Daisy A. Burr ◽  
Tracy d'Arbeloff ◽  
Maxwell Elliott ◽  
Annchen R. Knodt ◽  
Bartholomew D. Brigidi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Cognitive Reappraisal ◽  
Predictive Performance ◽  
Brain Regions ◽  
Expressive Suppression ◽  
Whole Brain ◽  
Functional Connections ◽  
Neural Signature

Previous research has identified specific brain regions associated with regulating emotion using common strategies such as expressive suppression and cognitive reappraisal. However, most research focuses on a priori regions and directs participants how to regulate, which may not reflect how people naturally regulate outside the laboratory. Here, we used a data-driven approach to investigate how individual differences in distributed intrinsic functional brain connectivity predict emotion regulation tendency. Specifically, we used connectome-based predictive modeling to extract functional connections in the brain significantly related to the dispositional use of suppression and reappraisal. These edges were then used in a predictive model and cross-validated in novel participants to identify a neural signature that reflects individual differences in the tendency to suppress and reappraise emotion. We found a significant neural signature for the dispositional use of suppression, but not reappraisal. Within this whole-brain signature, the intrinsic connectivity of the default mode network was most informative of suppression tendency. In addition, the predictive performance of this model was significant in males, but not females. These findings help inform how whole-brain networks of functional connectivity characterize how people tend to regulate emotion outside the laboratory.

scholarly journals Fatigue in multiple sclerosis is associated with multimodal interoceptive abnormalities

2019 ◽  
Vol 26 (14) ◽  
pp. 1845-1853 ◽  
Author(s):  
Cecilia Gonzalez Campo ◽  
Paula C Salamone ◽  
Nicolás Rodríguez-Arriagada ◽  
Fabian Richter ◽  
Eduar Herrera ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Functional Connectivity ◽  
Gray Matter Volume ◽  
Area Under The Curve ◽  
Anterior Cingulate ◽  
Impact Scale ◽  
Fatigue Impact Scale ◽  
Magnetic Resonance Imaging Mri ◽  
Interoceptive Accuracy

Background: Fatigue ranks among the most common and disabling symptoms in multiple sclerosis (MS). Recent theoretical works have surmised that this trait might be related to alterations across interoceptive mechanisms. However, this hypothesis has not been empirically evaluated. Objectives: To determine whether fatigue in MS patients is associated with specific behavioral, structural, and functional disruptions of the interoceptive domain. Methods: Fatigue levels were established via the Modified Fatigue Impact Scale. Interoception was evaluated through a robust measure indexed by the heartbeat detection task. Structural and functional connectivity properties of key interoceptive hubs were tested by magnetic resonance imaging (MRI) and resting-state functional MRI. Machine learning analyses were employed to perform pairwise classifications. Results: Only patients with fatigue presented with decreased interoceptive accuracy alongside decreased gray matter volume and increased functional connectivity in core interoceptive regions, the insula, and the anterior cingulate cortex. Each of these alterations was positively associated with fatigue. Finally, machine-learning analysis with a combination of the above interoceptive indices (behavioral, structural, and functional) successfully discriminated (area under the curve > 90%) fatigued patients from both non-fatigued and healthy controls. Conclusion: This study offers unprecedented evidence suggesting that disruptions of neurocognitive markers subserving interoception may constitute a signature of fatigue in MS.

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