Resting-state functional connectivity in multiple sclerosis: An examination of group differences and individual differences

2013 ◽  
Vol 51 (13) ◽  
pp. 2918-2929 ◽  
Author(s):  
Alisha L. Janssen ◽  
Aaron Boster ◽  
Beth A. Patterson ◽  
Amir Abduljalil ◽  
Ruchika Shaurya Prakash
2019 ◽  
Vol 31 ◽  
pp. 101-105 ◽  
Author(s):  
Patricia Stefancin ◽  
Sindhuja T Govindarajan ◽  
Lauren Krupp ◽  
Leigh Charvet ◽  
Timothy Q Duong

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S4-S4
Author(s):  
Jose Maximo ◽  
Frederic Briend ◽  
William Armstrong ◽  
Nina Kraguljac ◽  
Adrienne Lahti

Abstract Background Schizophrenia is thought to be a disorder of brain dysconnectivity. An imbalance between cortical excitation/inhibition is also implicated, but the link between these abnormalities remains unclear. The present study used resting state functional connectivity MRI (rs-fcMRI) and magnetic resonance spectroscopy (MRS) to investigate how measurements of glutamate + glutamine (Glx) in the anterior cingulate cortex (ACC) relate to rs-fcMRI in medication-naïve first episode psychosis (FEP) subjects compared to healthy controls (HC). Based on our previous findings, we hypothesized that in HC would show correlations between Glx and rs-fMRI in the salience and default mode network, but these relationships would be altered in FEP. Methods Data from 53 HC (age = 24.70 ±6.23, 34M/19F) and 60 FEP (age = 24.08 ±6.29, 38M/22F) were analyzed. To obtain MRS data, a voxel was placed in the ACC (PRESS, TR/TE = 2000/80ms). Metabolite concentrations were quantified with respect to internal water using the AMARES algorithm in jMRUI. rs-fMRI data were processed using a standard preprocessing pipeline in the CONN toolbox. BOLD signal from a priori brain regions of interest from posterior cingulate cortex (default mode network, DMN), anterior cingulate cortex (salience network, SN), and right posterior parietal cortex (central executive network, CEN) were extracted and correlated with the rest of the brain to measure functional connectivity (FC). Group analyses were performed on Glx, FC, and Glx-FC interactions while controlling for age, gender, and motion when applicable. FC and Glx-FC analyses were performed using small volume correction [(p < 0.01, threshold-free cluster enhancement corrected (TFCE)]. Results No significant between-group differences were found in Glx concentration in the ACC [F(1, 108) = 0.34, p = 0.56], but reduced FC was found on each network in FEP compared to HC (pTFCE corrected). Group Glx-FC interactions were found in the form of positive correlations between Glx and FC in DMN and SN in the HC group, but not in FEP; and negative correlations in CEN in HC, but not in FEP. Discussion While we did not find significant group differences in ACC Glx measurements, ACC Glx modulated FC differentially in FEP and HC. Positive correlations between Glx and FC were found in the SN and DMN, suggesting long range modulation of the two networks in HC, but not in FEP. Additionally, negative correlations between Glx and FC were found in CEN in HC, but not in FEP. Overall, these results suggest that even in the absence of group differences in Glx concentration, the long-range modulation of these 3 networks by ACC Glx is altered in FEP.


2013 ◽  
Vol 34 (12) ◽  
pp. 2304-2311 ◽  
Author(s):  
K.A. Koenig ◽  
M.J. Lowe ◽  
J. Lin ◽  
K.E. Sakaie ◽  
L. Stone ◽  
...  

Author(s):  
Bernardo Canedo Bizzo ◽  
Tiago Arruda‐Sanchez ◽  
Sean M Tobyne ◽  
John Daniel Bireley ◽  
Michael Howard Lev ◽  
...  

2017 ◽  
Vol 24 (13) ◽  
pp. 1696-1705 ◽  
Author(s):  
Alvino Bisecco ◽  
Federica Di Nardo ◽  
Renato Docimo ◽  
Giuseppina Caiazzo ◽  
Alessandro d’Ambrosio ◽  
...  

Objectives: To investigate resting-state functional connectivity (RS-FC) of the default-mode network (DMN) and of sensorimotor network (SMN) network in relapsing remitting (RR) multiple sclerosis (MS) patients with fatigue (F) and without fatigue(NF). Methods: In all, 59 RRMS patients and 29 healthy controls (HC) underwent magnetic resonance imaging (MRI) protocol including resting-state fMRI (RS-fMRI). Functional connectivity of the DMN and SMN was evaluated by independent component analysis (ICA). A linear regression analysis was performed to explore whether fatigue was mainly driven by changes observed in the DMN or in the SMN. Regional gray matter atrophy was assessed by voxel-based morphometry (VBM). Results: Compared to HC, F-MS patients showed a stronger RS-FC in the posterior cingulate cortex (PCC) and a reduced RS-FC in the anterior cingulated cortex (ACC) of the DMN. F-MS patients, compared to NF-MS patients, revealed (1) an increased RS-FC in the PCC and a reduced RS-FC in the ACC of the DMN and (2) an increased RS-FC in the primary motor cortex and in the supplementary motor cortex of the SMN. The regression analysis suggested that fatigue is mainly driven by RS-FC changes of the DMN. Conclusions: Fatigue in RRMS is mainly associated to a functional rearrangement of non-motor RS networks.


2021 ◽  
Author(s):  
Austin L Boroshok ◽  
Anne T Park ◽  
Panagiotis Fotiadis ◽  
Gerardo H Velasquez ◽  
Ursula A Tooley ◽  
...  

Neuroplasticity, defined as the brain's ability to change in response to its environment, has been extensively studied at the cellular and molecular levels. Work in animal models suggests that stimulation to the ventral tegmental area (VTA) enhances plasticity, and that myelination constrains plasticity. Little is known, however, about whether proxy measures of these properties in the human brain are associated with learning. Here we investigated the plasticity of the frontoparietal system (FPS), which supports complex cognition. We asked whether VTA resting-state functional connectivity and myelin map (T1-w/T2-w ratio) values predicted learning after short-term training on a FPS-dependent task: the adaptive n-back (n = 46, ages 18-25). We found that stronger connectivity between VTA and lateral prefrontal cortex at baseline predicted greater improvements in accuracy. Lower myelin map values predicted improvement in response times, but not accuracy. Our findings suggest that proxy markers of neural plasticity can predict learning in humans.


2018 ◽  
Author(s):  
Maxwell L. Elliott ◽  
Annchen R. Knodt ◽  
Megan Cooke ◽  
M. Justin Kim ◽  
Tracy R. Melzer ◽  
...  

AbstractIntrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displays higher heritability on average than resting-state functional connectivity. We also show that predictions of cognitive ability from GFC generalize across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.


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