scholarly journals Dance Intervention Impact on Brain Plasticity: A Randomized 6-Month fMRI Study in Non-expert Older Adults

2021 ◽  
Vol 13 ◽  
Author(s):  
Zuzana Balazova ◽  
Radek Marecek ◽  
L’ubomíra Novakova ◽  
Nela Nemcova-Elfmarkova ◽  
Sylvie Kropacova ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Plasticity ◽  
Network Connectivity ◽  
Behavioral Effect ◽  
Resting State Functional Connectivity ◽  
Complex Activity ◽  
Cognitive Changes ◽  
Z Scores ◽  
Dance Intervention

Background: Dance is a complex activity combining physical exercise with cognitive, social, and artistic stimulation.Objectives: We aimed to assess the effects of dance intervention (DI) on intra and inter-network resting-state functional connectivity (rs-FC) and its association to cognitive changes in a group of non-demented elderly participants.Methods: Participants were randomly assigned into two groups: DI and life as usual (LAU). Six-month-long DI consisted of supervised 60 min lessons three times per week. Resting-state fMRI data were processed using independent component analysis to evaluate the intra and inter-network connectivity of large-scale brain networks. Interaction between group (DI, LAU) and visit (baseline, follow-up) was assessed using ANOVA, and DI-induced changes in rs-FC were correlated with cognitive outcomes.Results: Data were analyzed in 68 participants (DI; n = 36 and LAU; n = 32). A significant behavioral effect was found in the attention domain, with Z scores increasing in the DI group and decreasing in the LAU group (p = 0.017). The DI as compared to LAU led to a significant rs-FC increase of the default mode network (DMN) and specific inter-network pairings, including insulo-opercular and right frontoparietal/frontoparietal control networks (p = 0.019 and p = 0.023), visual and language/DMN networks (p = 0.012 and p = 0.015), and cerebellar and visual/language networks (p = 0.015 and p = 0.003). The crosstalk of the insulo-opercular and right frontoparietal networks were associated with attention/executive domain Z-scores (R = 0.401, p = 0.015, and R = 0.412, p = 0.012).Conclusion: The DI led to intervention-specific complex brain plasticity changes that were of cognitive relevance.

scholarly journals Reconfiguration of directed functional connectivity among triple networks with aging: Considering the role of thalamo-cortical interactions

2019 ◽  
Author(s):  
Moumita Das ◽  
Vanshika Singh ◽  
Lucina Uddin ◽  
Arpan Banerjee ◽  
Dipanjan Roy
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Effective Connectivity ◽  
Network Connectivity ◽  
Young Group ◽  
Resting State Functional Connectivity ◽  
Functional Changes ◽  
Age Related ◽  
Directional Connectivity

AbstractThe human brain undergoes significant structural and functional changes across the lifespan. Our current understanding of the underlying causal relationships of dynamical changes in functional connectivity with age is limited. On average, functional connectivity within resting-state networks (RSNs) weakens in magnitude, while connections between RSNs tend to increase with age. Recent studies show that effective connectivity within and between large scale resting-state functional networks changes over the healthy lifespan. The vast majority of previous studies have focused primarily on characterizing cortical networks, with little work exploring the influence of subcortical nodes such as the thalamus on large-scale network interactions across the lifespan. Using directed connectivity and weighted net causal outflow measures applied to resting-state fMRI data, we examine the age-related changes in both cortical and thalamocortical causal interactions within and between RSNs. The three core neurocognitive networks from the triple network theory (default mode: DMN, salience: SN, and central executive: CEN) were identified independently using ICA and spatial matching of hub regions with these important RSNs previously reported in the literature. Multivariate granger causal analysis (GCA) was performed to test for directional connectivity and weighted causal outflow between selected nodes of RSNs accounting for thalamo-cortical interactions. Firstly, we observe that within-network causal connections become progressively weaker with age, and network dynamics are substantially reconfigured via strong thalamic drive particularly in the young group. Our findings manifest stronger between-network directional connectivity, which is further strongly mediated by the SN in flexible co-ordination with the CEN, and DMN in the old group compared with the young group. Hence, causal within- and between- triple network connectivity largely reflects age-associated effects of resting-state functional connectivity. Thalamo-cortical causality effects on the triple networks with age were next explored. We discovered that left and right thalamus exhibit substantial interactions with the triple networks and play a crucial role in the reconfiguration of directed connections and within network causal outflow. The SN displayed directed functional connectivity in strongly driving both the CEN and DMN to a greater extent in the older group. Notably, these results were largely replicated on an independent dataset of matched young and old individuals. Our findings based on directed functional connectivity and weighted causal outflow measures strengthen the hypothesis that balancing within and between network connectivity is perhaps critical for the preservation and flexibility of cognitive functioning with aging.

scholarly journals Attention network modulation via tRNS correlates with attention gain

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Federica Contò ◽  
Grace Edwards ◽  
Sarah Tyler ◽  
Danielle Parrott ◽  
Emily Grossman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Random Noise ◽  
Cognitive Capacity ◽  
Resting State Functional Connectivity ◽  
Attention Network ◽  
Transcranial Random Noise Stimulation ◽  
The Impact ◽  
Behavioral Improvement

Transcranial random noise stimulation (tRNS) can enhance vision in the healthy and diseased brain. Yet, the impact of multi-day tRNS on large-scale cortical networks is still unknown. We investigated the impact of tRNS coupled with behavioral training on resting-state functional connectivity and attention. We trained human subjects for 4 consecutive days on two attention tasks, while receiving tRNS over the intraparietal sulci, the middle temporal areas, or Sham stimulation. We measured resting-state functional connectivity of nodes of the dorsal and ventral attention network (DVAN) before and after training. We found a strong behavioral improvement and increased connectivity within the DVAN after parietal stimulation only. Crucially, behavioral improvement positively correlated with connectivity measures. We conclude changes in connectivity are a marker for the enduring effect of tRNS upon behavior. Our results suggest that tRNS has strong potential to augment cognitive capacity in healthy individuals and promote recovery in the neurological population.

scholarly journals The effect of amyloid deposition on longitudinal resting-state functional connectivity in cognitively normal older adults

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Chemin Lin ◽  
Maria Ly ◽  
Helmet T. Karim ◽  
Wenjing Wei ◽  
Beth E. Snitz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Older Adults ◽  
Cognitive Function ◽  
Functional Connectivity ◽  
Resting State ◽  
Clinical Symptoms ◽  
Rate Of Change ◽  
Resting State Functional Connectivity ◽  
Cognitive Changes ◽  
Cognitively Normal

Abstract Background Pathological processes contributing to Alzheimer’s disease begin decades prior to the onset of clinical symptoms. There is significant variation in cognitive changes in the presence of pathology, functional connectivity may be a marker of compensation to amyloid; however, this is not well understood. Methods We recruited 64 cognitively normal older adults who underwent neuropsychological testing and biannual magnetic resonance imaging (MRI), amyloid imaging with Pittsburgh compound B (PiB)-PET, and glucose metabolism (FDG)-PET imaging for up to 6 years. Resting-state MRI was used to estimate connectivity of seven canonical neural networks using template-based rotation. Using voxel-wise paired t-tests, we identified neural networks that displayed significant changes in connectivity across time. We investigated associations among amyloid and longitudinal changes in connectivity and cognitive function by domains. Results Left middle frontal gyrus connectivity within the memory encoding network increased over time, but the rate of change was lower with greater amyloid. This was no longer significant in an analysis where we limited the sample to only those with two time points. We found limited decline in cognitive domains overall. Greater functional connectivity was associated with better attention/processing speed and executive function (independent of time) in those with lower amyloid but was associated with worse function with greater amyloid. Conclusions Increased functional connectivity serves to preserve cognitive function in normal aging and may fail in the presence of pathology consistent with compensatory models.

scholarly journals S147. FUNCTIONAL BRAIN CONNECTIVITY DATA IMPROVE CLINICAL OUTCOME PREDICTION IN YOUTH AT RISK FOR PSYCHOSIS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S92-S92
Author(s):  
Guusje Collin ◽  
Alfonso Nieto-Castanon ◽  
Martha Shenton ◽  
Ofer Pasternak ◽  
Sinead Kelly ◽  
...  
Keyword(s):  
Prediction Model ◽  
Resting State ◽  
Outcome Prediction ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Resting State Functional Connectivity ◽  
Clinical Predictors ◽  
Functional Brain ◽  
Improved Outcome ◽  
Better Than

Abstract Background Improved outcome prediction in individuals at high risk for psychosis may facilitate targeted early intervention. Studies suggest that improved outcome prediction may be achieved through the use of neurocognitive or neuroimaging data, on their own or in addition to clinical data. This study examines whether adding resting-state functional connectivity data to validated clinical predictors of psychosis improve outcome prediction in the prodromal stage. Methods This study involves 137 adolescents and young adults at Clinical High Risk (CHR) for psychosis from the Shanghai At Risk for Psychosis (SHARP) program. Based on outcome after one-year follow-up, participants were separated into three outcome categories: good outcome (symptom remission, N = 71), intermediate outcome (ongoing CHR symptoms, N = 30), and poor outcome (conversion to psychosis or treatment-refractory, N = 36). Resting-state fMRI data were acquired for each participant and processed using the Conn toolbox, including rigorous motion correction. Multinomial logistic regression analysis and leave-one-out cross-validation were used to assess the performance of three prediction models: 1) a clinical-only model using validated clinical predictors from the NAPLS-2 psychosis-risk calculator, 2) an fMRI-only model using measures of functional connectome organization and within/between-network connectivity among established resting-state networks, and 3) a combined clinical and fMRI prediction model. Model performance was assessed using the harmonic mean of the positive predictive value and sensitivity for each outcome category. This F1 measure was compared to expected chance-levels using a permutation test with 1,000 sampled permutations in order to evaluate the statistical significance of the model’s prediction. Results The clinical-only prediction model failed to achieve a significant level of outcome prediction (F1 = 0.32, F1-chance = 0.26 □ 0.06, p = .154). The fMRI-only model did predict clinical outcome to a significant degree (F1 = 0.41, F1-chance = 0.29 □ 0.06, p = .016), but the combined clinical and fMRI prediction model showed the best performance (F1 = 0.46, F1-chance = 0.29 □ 0.06, p < .001). On average, positive predictive values (reflecting the probability that an outcome label predicted by the model was correct) were 39% better than chance-level and 32% better than the clinical-only model. Analyzing the contribution of individual predictor variables showed that GAF functional decline, a family history of psychosis, and performance on the Hopkins Verbal Learning Test were the most influential clinical predictors, whereas modular connectome organization, default-mode and fronto-parietal within-network connectivity, and between-network connectivity among language, salience, dorsal attention, cerebellum, and sensorimotor networks were the leading fMRI predictors. Discussion This study’s findings suggest that functional brain abnormalities reflected by alterations in resting-state functional connectivity precede and may drive subsequent changes in clinical functioning. Moreover, the findings show that markers of functional brain connectivity may be useful for improving early identification and clinical decision-making in prodromal psychosis.

scholarly journals Initial Evidence for Brain Plasticity Following a Digital Therapeutic Intervention for Depression

2019 ◽  
Vol 3 ◽  
pp. 247054701987788
Author(s):  
Megan M. Hoch ◽  
Gaelle E. Doucet ◽  
Dominik A. Moser ◽  
Won Hee Lee ◽  
Katherine A. Collins ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Resting State ◽  
Brain Plasticity ◽  
Memory Task ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Major Depressive ◽  
Emotional Faces ◽  
Task Training

Background Digital therapeutics such as cognitive–emotional training have begun to show promise for the treatment of major depressive disorder. Available clinical trial data suggest that monotherapy with cognitive–emotional training using the Emotional Faces Memory Task is beneficial in reducing depressive symptoms in patients with major depressive disorder. The aim of this study was to investigate whether Emotional Faces Memory Task training for major depressive disorder is associated with changes in brain connectivity and whether changes in connectivity parameters are related to symptomatic improvement. Methods Fourteen major depressive disorder patients received Emotional Faces Memory Task training as monotherapy over a six-week period. Patients were scanned at baseline and posttreatment to identify changes in resting-state functional connectivity and effective connectivity during emotional working memory processing. Results Compared to baseline, patients showed posttreatment reduced connectivity within resting-state networks involved in self-referential and salience processing and greater integration across the functional connectome at rest. Moreover, we observed a posttreatment increase in the Emotional Faces Memory Task-induced modulation of connectivity between cortical control and limbic brain regions, which was associated with clinical improvement. Discussion These findings provide initial evidence that cognitive–emotional training may be associated with changes in short-term plasticity of brain networks implicated in major depressive disorder. Conclusion Our findings pave the way for the principled design of large clinical and neuroimaging studies.

scholarly journals Using resting-state intrinsic network connectivity to identify suicide risk in mood disorders

2019 ◽  
Vol 50 (14) ◽  
pp. 2324-2334 ◽  
Author(s):  
Jonathan P. Stange ◽  
Lisanne M. Jenkins ◽  
Stephanie Pocius ◽  
Kayla Kreutzer ◽  
Katie L. Bessette ◽  
...  
Keyword(s):  
Mood Disorder ◽  
Mood Disorders ◽  
Resting State ◽  
Suicide Risk ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Neural Substrates ◽  
Resting State Functional Connectivity ◽  
Promising Tool ◽  
History Of

AbstractBackgroundLittle is known about the neural substrates of suicide risk in mood disorders. Improving the identification of biomarkers of suicide risk, as indicated by a history of suicide-related behavior (SB), could lead to more targeted treatments to reduce risk.MethodsParticipants were 18 young adults with a mood disorder with a history of SB (as indicated by endorsing a past suicide attempt), 60 with a mood disorder with a history of suicidal ideation (SI) but not SB, 52 with a mood disorder with no history of SI or SB (MD), and 82 healthy comparison participants (HC). Resting-state functional connectivity within and between intrinsic neural networks, including cognitive control network (CCN), salience and emotion network (SEN), and default mode network (DMN), was compared between groups.ResultsSeveral fronto-parietal regions (k > 57, p < 0.005) were identified in which individuals with SB demonstrated distinct patterns of connectivity within (in the CCN) and across networks (CCN-SEN and CCN-DMN). Connectivity with some of these same regions also distinguished the SB group when participants were re-scanned after 1–4 months. Extracted data defined SB group membership with good accuracy, sensitivity, and specificity (79–88%).ConclusionsThese results suggest that individuals with a history of SB in the context of mood disorders may show reliably distinct patterns of intrinsic network connectivity, even when compared to those with mood disorders without SB. Resting-state fMRI is a promising tool for identifying subtypes of patients with mood disorders who may be at risk for suicidal behavior.

scholarly journals Effects of dopamine agonist treatment on resting-state network connectivity in Parkinson’s disease

2020 ◽  
Author(s):  
David M. Cole ◽  
Bahram Mohammadi ◽  
Maria Milenkova ◽  
Katja Kollewe ◽  
Christoph Schrader ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Decision Making ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Dopamine Agonist ◽  
Resting State ◽  
Large Scale ◽  
Network Connectivity ◽  
Resting State Network ◽  
Large Scale Networks

ABSTRACTDopamine agonist (DA) medications commonly used to treat, or ‘normalise’, motor symptoms of Parkinson’s disease (PD) may lead to cognitive-neuropsychiatric side effects, such as increased impulsivity in decision-making. Subject-dependent variation in the neural response to dopamine modulation within cortico-basal ganglia circuitry is thought to play a key role in these latter, non-motor DA effects. This neuroimaging study combined resting-state functional magnetic resonance imaging (fMRI) with DA modification in patients with idiopathic PD, investigating whether brain ‘resting-state network’ (RSN) functional connectivity metrics identify disease-relevant effects of dopamine on systems-level neural processing. By comparing patients both ‘On’ and ‘Off’ their DA medications with age-matched, un-medicated healthy control subjects (HCs), we identified multiple non-normalising DA effects on frontal and basal ganglia RSN cortico-subcortical connectivity patterns in PD. Only a single isolated, potentially ‘normalising’, DA effect on RSN connectivity in sensori-motor systems was observed, within cerebro-cerebellar neurocircuitry. Impulsivity in reward-based decision-making was positively correlated with ventral striatal connectivity within basal ganglia circuitry in HCs, but not in PD patients. Overall, we provide brain systems-level evidence for anomalous DA effects in PD on large-scale networks supporting cognition and motivated behaviour. Moreover, findings suggest that dysfunctional striatal and basal ganglia signalling patterns in PD are compensated for by increased recruitment of other cortico-subcortical and cerebro-cerebellar systems.

scholarly journals White-Matter Hyperintensity Load and Differences in Resting-State Network Connectivity Based on Mild Cognitive Impairment Subtype

2021 ◽  
Vol 13 ◽  
Author(s):  
Martina Vettore ◽  
Matteo De Marco ◽  
Claudia Pallucca ◽  
Matteo Bendini ◽  
Maurizio Gallucci ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Resting State ◽  
Large Scale ◽  
Network Connectivity ◽  
Functional Networks ◽  
Frontoparietal Network ◽  
Definition Of ◽  
The Right

“Mild cognitive impairment” (MCI) is a diagnosis characterised by deficits in episodic memory (aMCI) or in other non-memory domains (naMCI). Although the definition of subtypes is helpful in clinical classification, it provides little insight on the variability of neurofunctional mechanisms (i.e., resting-state brain networks) at the basis of symptoms. In particular, it is unknown whether the presence of a high load of white-matter hyperintensities (WMHs) has a comparable effect on these functional networks in aMCI and naMCI patients. This question was addressed in a cohort of 123 MCI patients who had completed an MRI protocol inclusive of T1-weighted, fluid-attenuated inversion recovery (FLAIR) and resting-state fMRI sequences. T1-weighted and FLAIR images were processed with the Lesion Segmentation Toolbox to quantify whole-brain WMH volumes. The CONN toolbox was used to preprocess all fMRI images and to run an independent component analysis for the identification of four large-scale haemodynamic networks of cognitive relevance (i.e., default-mode, salience, left frontoparietal, and right frontoparietal networks) and one control network (i.e., visual network). Patients were classified based on MCI subtype (i.e., aMCI vs. naMCI) and WMH burden (i.e., low vs. high). Maps of large-scale networks were then modelled as a function of the MCI subtype-by-WMH burden interaction. Beyond the main effects of MCI subtype and WMH burden, a significant interaction was found in the salience and left frontoparietal networks. Having a low WMH burden was significantly more associated with stronger salience-network connectivity in aMCI (than in naMCI) in the right insula, and with stronger left frontoparietal-network connectivity in the right frontoinsular cortex. Vice versa, having a low WMH burden was significantly more associated with left-frontoparietal network connectivity in naMCI (than in aMCI) in the left mediotemporal lobe. The association between WMH burden and strength of connectivity of resting-state functional networks differs between aMCI and naMCI patients. Although exploratory in nature, these findings indicate that clinical profiles reflect mechanistic interactions that may play a central role in the definition of diagnostic and prognostic statuses.

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