scholarly journals Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy

2019 ◽  
Author(s):  
Chaitanya Ganne ◽  
Walter Hinds ◽  
James Kragel ◽  
Xiaosong He ◽  
Noah Sideman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Verbal Memory ◽  
Resting State ◽  
High Frequency ◽  
Cognitive Networks ◽  
Gamma Activity ◽  
List Type ◽  
Healthy Controls ◽  
Memory Encoding ◽  
Memory Network

AbstractHigh-frequency gamma activity of verbal-memory encoding using invasive-electroencephalogram coupled has laid the foundation for numerous studies testing the integrity of memory in diseased populations. Yet, the functional connectivity characteristics of networks subserving these HFA-memory linkages remains uncertain. By integrating this electrophysiological biomarker of memory encoding from IEEG with resting-state BOLD fluctuations, we estimated the segregation and hubness of HFA-memory regions in drug-resistant epilepsy patients and matched healthy controls. HFA-memory regions express distinctly different hubness compared to neighboring regions in health and in epilepsy, and this hubness was more relevant than segregation in predicting verbal memory encoding. The HFA-memory network comprised regions from both the cognitive control and primary processing networks, validating that effective verbal-memory encoding requires multiple functions, and is not dominated by a central cognitive core. Our results demonstrate a tonic intrinsic set of functional connectivity, which provides the necessary conditions for effective, phasic, task-dependent memory encoding.HighlightsHigh frequency memory activity in IEEG corresponds to specific BOLD changes in resting-state data.HFA-memory regions had lower hubness relative to control brain nodes in both epilepsy patients and healthy controls.HFA-memory network displayed hubness and participation (interaction) values distinct from other cognitive networks.HFA-memory network shared regional membership and interacted with other cognitive networks for successful memory encoding.HFA-memory network hubness predicted both concurrent task (phasic) and baseline (tonic) verbal-memory encoding success.

scholarly journals The human claustrum is functionally connected to cognitive networks and involved in cognitive control

2018 ◽  
Author(s):  
Samuel R. Krimmel ◽  
Michael G. White ◽  
Matthew H. Panicker ◽  
Frederick S. Barrett ◽  
Brian N. Mathur ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Control ◽  
Resting State ◽  
Cognitive Task ◽  
Cognitive Networks ◽  
Anterior Cingulate ◽  
Mammalian Brain ◽  
List Type ◽  
Sensorimotor Processing ◽  
Difficult Condition

AbstractThe claustrum is among the most highly connected structures in the mammalian brain. However, the function of the claustrum is unknown, which is due to its peculiar anatomical arrangement. Here, we use resting state and task functional magnetic resonance imaging (fMRI) to elucidate claustrum function in human subjects. We first describe a method to reveal claustrum signal with no linear relationship with adjacent regions. We applied this approach to resting state functional connectivity (RSFC) analysis of the claustrum at high resolution (1.5 mm isotropic voxels) using a 7T dataset (n=20) and a separate 3T dataset for replication (n=35). We then assessed claustrum activation during performance of a cognitive task, the multi-source interference task, at 3T (n=33). Extensive functional connectivity was observed between claustrum and cortical regions associated with cognitive control, including anterior cingulate, prefrontal and parietal cortices. Cognitive task performance was associated with widespread activation and deactivation that overlapped with the cortical areas showing functional connectivity to the claustrum. Furthermore, the claustrum was significantly activated at the onset of the difficult condition of the task, but not during the remainder of the difficult condition. These data suggest that the claustrum can be functionally isolated with fMRI, and that it is involved in cognitive control in humans independent of sensorimotor processing.HighlightsRemoving signal from neighboring structures isolates claustrum BOLD signal at 7T and 3T field strengthClaustrum is extensively functionally connected with cortex, including cognitive networksClaustrum is activated at the onset of a cognitive conflict taskClaustrum may be involved in cognition independent of sensorimotor processing

scholarly journals Tonic Resting State Hubness Supports High Gamma Activity Defined Verbal Memory Encoding Network in Epilepsy

Neuroscience ◽  
2020 ◽  
Vol 425 ◽  
pp. 194-216 ◽  
Author(s):  
Ganne Chaitanya ◽  
Walter Hinds ◽  
James Kragel ◽  
Xiaosong He ◽  
Noah Sideman ◽  
...  
Keyword(s):  
Verbal Memory ◽  
Resting State ◽  
Gamma Activity ◽  
Memory Encoding ◽  
High Gamma

scholarly journals A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Federico Calesella ◽  
Alberto Testolin ◽  
Michele De Filippo De Grazia ◽  
Marco Zorzi
Keyword(s):  
Feature Extraction ◽  
Functional Connectivity ◽  
Dimensionality Reduction ◽  
Verbal Memory ◽  
Resting State ◽  
Component Analysis ◽  
Model Complexity ◽  
Stroke Patients ◽  
Prediction Ability ◽  
The Impact

AbstractMultivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four well-known dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated deficits based on cross-validated regularized regression. In particular, we investigated the prediction ability over different neuropsychological scores referring to language, verbal memory, and spatial memory domains. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were the two best methods at extracting representative features, followed by Dictionary Learning (DL) and Non-Negative Matrix Factorization (NNMF). Consistent with these results, features extracted by PCA and ICA were found to be the best predictors of the neuropsychological scores across all the considered cognitive domains. For each feature extraction method, we also examined the impact of the regularization method, model complexity (in terms of number of features that entered in the model) and quality of the maps that display predictive edges in the resting state networks. We conclude that PCA-based models, especially when combined with L1 (LASSO) regularization, provide optimal balance between prediction accuracy, model complexity, and interpretability.

scholarly journals Mechanisms of transcranial magnetic stimulation in the treatment of anorexia nervosa

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S49-S50
Author(s):  
Lydia Shackshaft
Keyword(s):  
Anorexia Nervosa ◽  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Resting State ◽  
High Frequency ◽  
Magnetic Stimulation ◽  
Dorsolateral Prefrontal Cortex ◽  
Resting State Functional Connectivity ◽  
Dorsolateral Prefrontal ◽  
Neurophysiological Mechanisms

AimsSevere and Enduring Anorexia Nervosa (SE-AN) is a challenging condition to treat, with limited therapeutic options, high morbidity, and the highest mortality rates of any psychiatric illness. Repetitive Transcranial Magnetic Stimulation (rTMS) is an emerging treatment option, as evidence demonstrates promising efficacy in improving mood and reducing core Anorexia Nervosa symptoms, as well as safety and tolerability to patients. We aimed to investigate the neurophysiological mechanisms of rTMS use in SE-AN patients by assessing changes in resting state functional connectivity, in the first functional neuroimaging analysis investigating rTMS effects in Anorexia Nervosa patients.Method26 females with a current diagnosis of SE-AN received 20 sessions of sham or real high frequency rTMS (10 hertz) to the left dorsolateral prefrontal cortex in a randomised double-blind trial. Resting-state functional magnetic resonance imaging was performed before and after rTMS. Neural correlates of rTMS treatment were identified using a seed-based functional connectivity analysis with the left dorsolateral prefrontal cortex and bilateral amygdalae as regions of interest. Functional connectivity differences were analysed using t-contrasts in a mixed ANOVA (flexible factorial analysis) to assess interactions between treatment group (real rTMS vs sham) and time-point (pre or post TMS).ResultNo statistically significant changes in resting-state functional connectivity were observed post-rTMS compared to baseline in participants receiving active rTMS compared to sham. Increased functional connectivity between the left amygdala and left pre-supplementary motor area was observed to reach cluster-wise significance (PFWE < 0.05). However, after Bonferroni correction for multiple comparisons (3 seed regions), this did not reach the significance threshold PFWE <0.017.ConclusionThis study highlights the need for further investigation of neurophysiological mechanisms, including resting-state functional connectivity modulation, resulting from rTMS to the dorsolateral prefrontal cortex in SE-AN patients. This requires higher powered studies to account for heterogeneity in treatment response. We have provided some indication that high frequency rTMS may have therapeutic benefit in SE-AN by modification of functional connectivity between prefrontal and limbic brain regions, resulting in improved top-down cognitive control over emotional processing and ability to enact goal-directed behaviours, enabling secondary reductions in eating disorder behaviours.

scholarly journals Dynamic Interhemispheric Desynchronization in Marmosets and Humans With Disorders of the Corpus Callosum

2020 ◽  
Vol 14 ◽  
Author(s):  
Diego Szczupak ◽  
Cecil C. Yen ◽  
Cirong Liu ◽  
Xiaoguang Tian ◽  
Roberto Lent ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Corpus Callosum ◽  
Resting State ◽  
Human Subjects ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Periodic Patterns ◽  
Lower Strength ◽  
Neuronal Communication ◽  
Clear Explanation

The corpus callosum, the principal structural avenue for interhemispheric neuronal communication, controls the brain’s lateralization. Developmental malformations of the corpus callosum (CCD) can lead to learning and intellectual disabilities. Currently, there is no clear explanation for these symptoms. Here, we used resting-state functional MRI (rsfMRI) to evaluate the dynamic resting-state functional connectivity (rsFC) in both the cingulate cortex (CG) and the sensory areas (S1, S2, A1) in three marmosets (Callithrix jacchus) with spontaneous CCD. We also performed rsfMRI in 10 CCD human subjects (six hypoplasic and four agenesic). We observed no differences in the strength of rsFC between homotopic CG and sensory areas in both species when comparing them to healthy controls. However, in CCD marmosets, we found lower strength of quasi-periodic patterns (QPP) correlation in the posterior interhemispheric sensory areas. We also found a significant lag of interhemispheric communication in the medial CG, suggesting asynchrony between the two hemispheres. Correspondingly, in human subjects, we found that the CG of acallosal subjects had a higher QPP correlation than controls. In comparison, hypoplasic subjects had a lower QPP correlation and a delay of 1.6 s in the sensory regions. These results show that CCD affects the interhemispheric synchrony of both CG and sensory areas and that, in both species, its impact on cortical communication varies along the CC development gradient. Our study shines a light on how CCD misconnects homotopic regions and opens a line of research to explain the causes of the symptoms exhibited by CCD patients and how to mitigate them.

High-Order Functional Connectivity Network for Differential Diagnosis of Depression

2019 ◽  
Vol 9 (6) ◽  
pp. 1095-1102
Author(s):  
Jian Yang ◽  
Xu Mao ◽  
Ning Liu ◽  
Ning Zhong
Keyword(s):  
Time Series ◽  
Differential Diagnosis ◽  
Functional Connectivity ◽  
Resting State ◽  
Correlation Time ◽  
Time Window ◽  
Resting State Fmri ◽  
High Order ◽  
Healthy Controls ◽  
Resting State Functional Connectivity

Resting-state functional connectivity (FC) changes dynamically and major depressive disorder (MDD) has abnormality in functional connectivity networks (FCNs), but few existing resting-state fMRI study on MDD utilizes the dynamics, especially for identifying depressive individuals from healthy controls. In this paper, we propose a methodological procedure for differential diagnosis of depression, called HN3D, which is based on high-order functional connectivity networks (HFCN). Firstly, HN3D extracts time series by independent component analysis, and partitions them into overlapped short series by sliding time window. Secondly, it constructs a FCN for each time window and concatenates correlation matrices of all FCNs to generate correlation time series. Then, correlation time series are grouped into different clusters and high-order correlations for HFCN is calculated based on their means. Finally, graph based features of HFCNs are extracted and selected for a linear discriminative classifier. Tested on 21 healthy controls and 20 MDD patients, HN3D achieved its best 100% classification accuracy, which is much higher than results based on stationary FCNs. In addition, most discriminative components of HN3D locate in default mode network and visual network, which are consistent with existing stationary-based results on depression. Though HN3D needs to be studied further, it is helpful for the differential diagnosis of depression and might have potentiality in identifying relevant biomarkers.

Altered functional connectivity and performance variability in relapsing–remitting multiple sclerosis

2014 ◽  
Vol 20 (11) ◽  
pp. 1453-1463 ◽  
Author(s):  
Magdalena Wojtowicz ◽  
Erin L Mazerolle ◽  
Virender Bhan ◽  
John D Fisk
Keyword(s):  
Multiple Sclerosis ◽  
Information Processing ◽  
Functional Connectivity ◽  
Processing Speed ◽  
Resting State ◽  
Healthy Controls ◽  
Information Processing Speed ◽  
Frontal Pole ◽  
Performance Variability ◽  
Relapsing Remitting

Background: Patients with multiple sclerosis (MS) demonstrate slower and more variable performance on attention and information processing speed tasks. Greater variability in cognitive task performance has been shown to be an important predictor of neurologic status and provides a unique measure of cognitive performance in MS patients. Objectives: This study investigated alterations in resting-state functional connectivity associated with within-person performance variability in MS patients. Methods: Relapsing–remitting MS patients and matched healthy controls completed structural MRI and resting-state fMRI (rsfMRI) scans, as well as tests of information processing speed. Performance variability was calculated from reaction time tests of processing speed. rsfMRI connectivity was investigated within regions associated with the default mode network (DMN). Relations between performance variability and functional connectivity in the DMN within MS patients were evaluated. Results: MS patients demonstrated greater reaction time performance variability compared to healthy controls ( p<0.05). For MS patients, more stable performance on a complex processing speed task was associated with greater resting-state connectivity between the ventral medial prefrontal cortex and the frontal pole. Conclusions: Among MS patients, greater functional connectivity between medial prefrontal and frontal pole regions appears to facilitate performance stability on complex speed-dependent information processing tasks.

scholarly journals Altered Intra- and Interregional Synchronization in Resting-State Cerebral Networks Associated with Chronic Tinnitus

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Chen Chen ◽  
Jian Zhang ◽  
Xiao-Wei Li ◽  
Wenqing Xia ◽  
Xu Feng ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Chronic Tinnitus ◽  
Healthy Controls ◽  
Tinnitus Distress ◽  
The Right

Objective. Subjective tinnitus is hypothesized to arise from aberrant neural activity; however, its neural bases are poorly understood. To identify aberrant neural networks involved in chronic tinnitus, we compared the resting-state functional magnetic resonance imaging (fMRI) patterns of tinnitus patients and healthy controls.Materials and Methods. Resting-state fMRI measurements were obtained from a group of chronic tinnitus patients (n=29) with normal hearing and well-matched healthy controls (n=30). Regional homogeneity (ReHo) analysis and functional connectivity analysis were used to identify abnormal brain activity; these abnormalities were compared to tinnitus distress.Results. Relative to healthy controls, tinnitus patients had significant greater ReHo values in several brain regions including the bilateral anterior insula (AI), left inferior frontal gyrus, and right supramarginal gyrus. Furthermore, the left AI showed enhanced functional connectivity with the left middle frontal gyrus (MFG), while the right AI had enhanced functional connectivity with the right MFG; these measures were positively correlated with Tinnitus Handicap Questionnaires (r=0.459,P=0.012andr=0.479,P=0.009, resp.).Conclusions. Chronic tinnitus patients showed abnormal intra- and interregional synchronization in several resting-state cerebral networks; these abnormalities were correlated with clinical tinnitus distress. These results suggest that tinnitus distress is exacerbated by attention networks that focus on internally generated phantom sounds.

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