Music reduces pain and increases resting state fMRI BOLD signal amplitude in the left angular gyrus in fibromyalgia patients

Individuals diagnosed with mild cognitive impairment (MCI) are at high risk of transition to Alzheimer’s disease (AD). However, little is known about functional characteristics of the conversion from MCI to AD. Resting-state functional magnetic resonance imaging was performed in 25 AD patients, 31 MCI patients, and 42 well-matched normal controls at baseline. Twenty-one of the 31 MCI patients converted to AD at approximately 24 months of follow-up. Functional connectivity strength (FCS) and seed-based functional connectivity analyses were used to assess the functional differences among the groups. Compared to controls, subjects with MCI and AD showed decreased FCS in the default-mode network and the occipital cortex. Importantly, the FCS of the left angular gyrus and middle occipital gyrus was significantly lower in MCI-converters as compared with MCI-nonconverters. Significantly decreased functional connectivity was found in MCI-converters compared to nonconverters between the left angular gyrus and bilateral inferior parietal lobules, dorsolateral prefrontal and lateral temporal cortices, and the left middle occipital gyrus and right middle occipital gyri. We demonstrated gradual but progressive functional changes during a median 2-year interval in patients converting from MCI to AD, which might serve as early indicators for the dysfunction and progression in the early stage of AD.

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WE-FG-206-02: Brief Variations of BOLD Signal in Resting State FMRI Leading to Functional Connectivity Pattern Identification

Medical Physics ◽

10.1118/1.4957932 ◽

2016 ◽

Vol 43 (6Part41) ◽

pp. 3831-3831

Author(s):

N Ball ◽

N Chen

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Pattern Identification ◽

Connectivity Pattern ◽

Bold Signal

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Additive and interaction effects of working memory and motor sequence training on brain functional connectivity

Scientific Reports ◽

10.1038/s41598-021-02492-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Priska Zuber ◽

Laura Gaetano ◽

Alessandra Griffa ◽

Manuel Huerbin ◽

Ludovico Pedullà ◽

...

Keyword(s):

Working Memory ◽

Functional Connectivity ◽

Processing Speed ◽

Resting State ◽

Resting State Fmri ◽

Interactive Effects ◽

Angular Gyrus ◽

Precentral Gyrus ◽

Motor Sequence ◽

Brain Functional Connectivity

AbstractAlthough shared behavioral and neural mechanisms between working memory (WM) and motor sequence learning (MSL) have been suggested, the additive and interactive effects of training have not been studied. This study aimed at investigating changes in brain functional connectivity (FC) induced by sequential (WM + MSL and MSL + WM) and combined (WM × MSL) training programs. 54 healthy subjects (27 women; mean age: 30.2 ± 8.6 years) allocated to three training groups underwent twenty-four 40-min training sessions over 6 weeks and four cognitive assessments including functional MRI. A double-baseline approach was applied to account for practice effects. Test performances were compared using linear mixed-effects models and t-tests. Resting state fMRI data were analysed using FSL. Processing speed, verbal WM and manual dexterity increased following training in all groups. MSL + WM training led to additive effects in processing speed and verbal WM. Increased FC was found after training in a network including the right angular gyrus, left superior temporal sulcus, right superior parietal gyrus, bilateral middle temporal gyri and left precentral gyrus. No difference in FC was found between double baselines. Results indicate distinct patterns of resting state FC modulation related to sequential and combined WM and MSL training suggesting a relevance of the order of training performance. These observations could provide new insight for the planning of effective training/rehabilitation.

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Deep learning-based classification of resting-state fMRI independent-component analysis

10.1101/2020.07.02.183772 ◽

2020 ◽

Author(s):

Victor Nozais ◽

Philippe Boutinaud ◽

Violaine Verrecchia ◽

Marie-Fateye Gueye ◽

Pierre Yves Hervé ◽

...

Keyword(s):

Deep Learning ◽

Resting State ◽

Resting State Fmri ◽

Brain Regions ◽

Independent Component ◽

Angular Gyrus ◽

Automated Classification ◽

Dimensional Parameter ◽

Spatial Level

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here, we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants respectively. We trained a multi-layer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92%. Predictions on the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96% of the gray matter. Second, we proposed an individual-based analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.

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SU-G-IeP1-11: Resting-State Fluctuation of BOLD Signal Amplitude for Mapping Cerebrovascular Reactivity in Presurgical Functional MRI

Medical Physics ◽

10.1118/1.4956971 ◽

2016 ◽

Vol 43 (6Part25) ◽

pp. 3646-3647 ◽

Cited By ~ 1

Author(s):

P Wang ◽

P Hou ◽

S Kesler ◽

R Colen ◽

A Kumar ◽

...

Keyword(s):

Functional Mri ◽

Resting State ◽

Signal Amplitude ◽

Cerebrovascular Reactivity ◽

Bold Signal

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Network Mapping of Connectivity Alterations in Disorder of Consciousness: Towards Targeted Neuromodulation

Journal of Clinical Medicine ◽

10.3390/jcm9030828 ◽

2020 ◽

Vol 9 (3) ◽

pp. 828 ◽

Cited By ~ 1

Author(s):

Lucia Mencarelli ◽

Maria Chiara Biagi ◽

Ricardo Salvador ◽

Sara Romanella ◽

Giulio Ruffini ◽

...

Keyword(s):

Brain Stimulation ◽

Resting State ◽

Meta Analysis ◽

Resting State Fmri ◽

Therapeutic Interventions ◽

Angular Gyrus ◽

Biophysical Modeling ◽

Disorder Of Consciousness ◽

Positron Emission ◽

Clinical Conditions

Disorder of consciousness (DoC) refers to a group of clinical conditions that may emerge after brain injury, characterized by a varying decrease in the level of consciousness that can last from days to years. An understanding of its neural correlates is crucial for the conceptualization and application of effective therapeutic interventions. Here we propose a quantitative meta-analysis of the neural substrate of DoC emerging from functional magnetic resonance (fMRI) and positron emission tomography (PET) studies. We also map the relevant networks of resulting areas to highlight similarities with Resting State Networks (RSNs) and hypothesize potential therapeutic solutions leveraging network-targeted noninvasive brain stimulation. Available literature was reviewed and analyzed through the activation likelihood estimate (ALE) statistical framework to describe resting-state or task-dependent brain activation patterns in DoC patients. Results show that task-related activity is limited to temporal regions resembling the auditory cortex, whereas resting-state fMRI data reveal a diffuse decreased activation affecting two subgroups of cortical (angular gyrus, middle frontal gyrus) and subcortical (thalamus, cingulate cortex, caudate nucleus) regions. Clustering of their cortical functional connectivity projections identify two main altered functional networks, related to decreased activity of (i) the default mode and frontoparietal networks, as well as (ii) the anterior salience and visual/auditory networks. Based on the strength and topography of their connectivity profile, biophysical modeling of potential brain stimulation solutions suggests the first network as the most feasible target for tES, tDCS neuromodulation in DoC patients.

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