resting state connectivity
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2022 ◽  
Author(s):  
Haiteng Jiang ◽  
Vasileios Kokkinos ◽  
Shuai Ye ◽  
Alexandra Urban ◽  
Anto Bagic ◽  
...  

Stereotactic-electroencephalography (SEEG) is a common neurosurgical method to localize epileptogenic zone in drug resistant epilepsy patients and inform treatment recommendations. In the current clinical practice, localization of epileptogenic zone typically requires prolonged recordings to capture seizure, which may take days to weeks. Although epilepsy surgery has been proven to be effective in general, the percentage of unsatisfactory seizure outcomes is still concerning. We developed a method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state SEEG data. In a cohort of 43 drug resistant epilepsy patients, we estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. We hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. We found higher excitability in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions, probably reflecting inhibitory input from non-SOZ to prevent seizure initiation. Greater differences in information flow between SOZ and non-SOZ regions were associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, our method localized the SOZ with an accuracy of 85% and predicted the seizure outcome with an accuracy of 77% using clinically determined SOZ. Overall, our study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.


2021 ◽  
Vol 15 ◽  
Author(s):  
Kristian M. Eschenburg ◽  
Thomas J. Grabowski ◽  
David R. Haynor

Deep learning has been applied to magnetic resonance imaging (MRI) for a variety of purposes, ranging from the acceleration of image acquisition and image denoising to tissue segmentation and disease diagnosis. Convolutional neural networks have been particularly useful for analyzing MRI data due to the regularly sampled spatial and temporal nature of the data. However, advances in the field of brain imaging have led to network- and surface-based analyses that are often better represented in the graph domain. In this analysis, we propose a general purpose cortical segmentation method that, given resting-state connectivity features readily computed during conventional MRI pre-processing and a set of corresponding training labels, can generate cortical parcellations for new MRI data. We applied recent advances in the field of graph neural networks to the problem of cortical surface segmentation, using resting-state connectivity to learn discrete maps of the human neocortex. We found that graph neural networks accurately learn low-dimensional representations of functional brain connectivity that can be naturally extended to map the cortices of new datasets. After optimizing over algorithm type, network architecture, and training features, our approach yielded mean classification accuracies of 79.91% relative to a previously published parcellation. We describe how some hyperparameter choices including training and testing data duration, network architecture, and algorithm choice affect model performance.


2021 ◽  
Vol 46 (6) ◽  
pp. E702-E710
Author(s):  
Gregory Overbeek ◽  
Timothy J. Gawne ◽  
Meredith A. Reid ◽  
Nina V. Kraguljac ◽  
Adrienne C. Lahti

2021 ◽  
Author(s):  
Dragoş Cȋrneci ◽  
Mihaela Onu ◽  
Claudiu C. Papasteri ◽  
Dana Georgescu ◽  
Catalina Poalelungi ◽  
...  

Abstract Training of autobiographical memory has been proposed as intervention to improve cognitive functions. The neural substrates for such improvements are poorly understood. Several brain networks have been previously linked to autobiographical recollections, including the default mode network (DMN) and the sensorimotor network. Here we tested the hypothesis that different neural networks support distinct aspects of memory improvement in response to training on a group of 59 subjects. We found that memory training using olfactory cues increases resting-state intra-network DMN connectivity, and this associates with improved recollection of cue-specific memories. On the contrary, training decreased resting-state connectivity within the sensorimotor network, a decrease that correlated with improved ability for voluntary recall. Moreover, only the decrease in sensorimotor connectivity associated with the training-induced decrease in the TNFα factor, an immune modulation previously linked to improved cognitive performance. We identified functional and biochemical factors that associate with distinct memory processes improved by autobiographical training. Pathways which connect autobiographical memory to both high level cognition and somatic physiology are discussed.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Oliver Grimm ◽  
Daan van Rooij ◽  
Asya Tshagharyan ◽  
Dilek Yildiz ◽  
Jan Leonards ◽  
...  

AbstractADHD is a neurodevelopmental disorder with a long trajectory into adulthood where it is often comorbid with depression, substance use disorder (SUD) or obesity. Previous studies described a dysregulated dopaminergic system, reflected by abnormal reward processing, both in ADHD as well as in depression, SUD or obesity. No study so far however tested systematically whether pathologies in the brain’s reward system explain the frequent comorbidity in adult ADHD. To test this, we acquired MRI scans from 137 participants probing the reward system by a monetary incentive delay task (MIDT) as well as assessing resting-state connectivity with ventral striatum as a seed mask. No differences were found between comorbid disorders, but a significant linear effect pointed toward less left intrastriatal connectivity in patients depending on the number of comorbidities. This points towards a neurobiologically impaired reward- and decision-making ability in patients with more comorbid disorders. This suggests that less intrastriatal connectivity parallels disorder severity but not disorder specificity, while MIDT abnormalities seem mainly to be driven by ADHD.


2021 ◽  
Vol 17 (S5) ◽  
Author(s):  
Raffaele Cacciaglia ◽  
José Maria González‐de‐Echávarri ◽  
Marta Milà‐Alomà ◽  
Greg Operto ◽  
Carles Falcon ◽  
...  

2021 ◽  
Author(s):  
Michael A. Mooney ◽  
Robert J.M. Hermosillo ◽  
Eric Feczko ◽  
Oscar Miranda-Dominguez ◽  
Lucille A. Moore ◽  
...  

Background The clinical utility of MRI neuroimaging studies of psychopathology has been limited by a constellation of factors—small sample sizes, small effect sizes, and heterogeneity of methods and samples across studies—that hinder generalizability and specific replication. An analogy is early genomics studies of complex traits, wherein a move to large, multi-site samples and a focus on cumulative effects (polygenic scores) led to reproducible and clinically applicable effects from genome-wide association studies. A similar logic in MRI may provide a way to improve reproducibility, precision, and clinical utility for brain-wide MRI association studies. Methods Polyneuro scores (PNS) represent the cumulative effect of brain-wide measures—in the present case, resting-state functional connectivity (rs-fcMRI) associated with ADHD symptoms. These scores were constructed and validated using baseline data from the Adolescent Brain Cognitive Development (ABCD, N=5666) study, with a reproducible matched subset as the discovery cohort (N=2801). Association between the PNS and ADHD symptoms was further tested in an independent case-control cohort, the Oregon-ADHD-1000 (N=533). Results The ADHD PNS was significantly associated with ADHD symptoms in both the ABCD and Oregon cohorts after accounting for relevant covariates (p-values < 0.001). While the strongest effects contributing to the PNS were concentrated among connections involving the default mode and cingulo-opercular networks, the most predictive PNS involved connectivity across all brain networks. These findings were robust to stringent motion thresholds. In the longitudinal Oregon-ADHD-1000, non-ADHD comparison youth had significantly lower ADHD PNS (β=-0.309, p=0.00142) than children with persistent ADHD (met diagnostic criteria at two or more time points from age 7 to 19). The ADHD PNS, however, did not reliably mediate polygenic risk for ADHD. Instead, the PNS and an ADHD polygenic score were independently associated with ADHD symptoms. Conclusions A polyneuro risk score representing cumulative ADHD-associated resting-state connectivity was robustly associated with ADHD symptoms in two independent cohorts using distinct sampling designs, yet was independent of polygenic liability for ADHD, suggesting the need to examine environmental influences. The polyneuro score approach holds promise for improving the reproducibility of neuroimaging studies, identifying their clinical utility, and unraveling the complex relationships between brain connectivity and the etiology of behavioral disorders.


2021 ◽  
Author(s):  
Selene Gallo ◽  
Ahmed ElGazzar ◽  
Paul Zhutovsky ◽  
Rajat Mani Thomas ◽  
Nooshin Javaheripour ◽  
...  

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. Resting-state functional magnetic resonance imaging data were obtained from the REST-meta-MDD (N=2338) and PsyMRI (N=1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN) and performance was evaluated using 5-fold cross-validation. Results were visualized using GCN-Explainer, an ablation study and univariate t-testing.Mean classification accuracy was 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes.Whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.


2021 ◽  
Vol 168 ◽  
pp. S139
Author(s):  
Anna Tabueva ◽  
Ilya Zakharov ◽  
Victoria Ismatullina ◽  
Inna Feklicheva ◽  
Nadezda Chipeeva ◽  
...  

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