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Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study.

Wed, 02/10/2021 - 01:12
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Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study.

Front Neural Circuits. 2020;14:593263

Authors: Sendi MSE, Zendehrouh E, Miller RL, Fu Z, Du Y, Liu J, Mormino EC, Salat DH, Calhoun VD

Abstract
Background: Alzheimer's disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart.
Method: We used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM.
Results: All states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks.
Conclusion: Our results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

PMID: 33551754 [PubMed - in process]

Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain.

Tue, 02/09/2021 - 19:11
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Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain.

Neuroimage. 2021 Feb 04;:117827

Authors: Yousefi B, Keilholz S

Abstract
The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as functional networks and gradients. Dynamic analysis techniques have shown that functional connectivity is a mere summary of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain's intrinsic activity that cannot be inferred by functional connectivity or the spatial maps derived from it, such as functional networks and gradients. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ∼20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale functional gradient, simultaneously across the cerebral cortex and in most other brain regions. In some regions, like the thalamus, the propagation suggests previously-undescribed gradients. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between functional networks is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.

PMID: 33549755 [PubMed - as supplied by publisher]

Hemodynamic Matrix Factorization for Functional Magnetic Resonance Imaging.

Tue, 02/09/2021 - 19:11
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Hemodynamic Matrix Factorization for Functional Magnetic Resonance Imaging.

Neuroimage. 2021 Feb 04;:117814

Authors: Hütel M, Antonelli M, Melbourne A, Ourselin S

Abstract
The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. First, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Second, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, which decomposes fMRI data into modes. Each mode comprises of a neural activation time course and a spatial mapping. Two versions of HMF are proposed in which the neural activation time course of each mode is convolved with either the canonical HRF or predetermined subject-specific HRFs. Firstly, HMF with the canonical HRF is applied to two open-source cohorts. These cohorts comprise of several task experiments including motor, incidental memory, spatial coherence discrimination, verbal discrimination task and a very short localization task, engaging multiple parts of the eloquent cortex. HMF modes were obtained whose neural activation time course followed original task timings and whose corresponding spatial map matched cortical areas known to be involved in the respective task processing. Secondly, the alignment of these neural activation time courses to task timings were further improved by replacing the canonical HRF with subject-specific HRFs during HMF mode computation. In addition to task-related modes, HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks. The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.

PMID: 33549748 [PubMed - as supplied by publisher]

Increased grey matter volume and associated resting-state functional connectivity in chronic spontaneous urticaria: A structural and functional MRI study.

Tue, 02/09/2021 - 19:11
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Increased grey matter volume and associated resting-state functional connectivity in chronic spontaneous urticaria: A structural and functional MRI study.

J Neuroradiol. 2021 Feb 04;:

Authors: Wang Y, Gao D, Cui B, Yu B, Fang J, Wang Z, Tang R, Cao Z, Song W, Song P, Li S

Abstract
BACKGROUND AND PURPOSE: Chronic itch is one of the most common irritating sensations, yet its mechanisms have not been fully elucidated. Although some studies have revealed relationships between itching and brain function, the structural changes in the brain induced by chronic itching, such as those accompanying chronic spontaneous urticaria (CSU), remain unclear. In this study, we aimed to explore the potential changes in brain structure and the associated functional circuitry in CSU patients to generate insights to aid chronic itch management.
METHODS: Forty CSU patients and forty healthy controls (HCs) were recruited. Seven-day urticaria activity score (UAS7) values were collected to evaluate clinical symptoms. Voxel-based morphometry (VBM) and seed-based resting-state functional connectivity (rs-FC) analysis were used to assess structural changes in the brain and associated changes in functional circuitry.
RESULTS: Compared with HCs, CSU patients had significantly increased grey matter (GM) volume in the right premotor cortex, left fusiform cortex, and cerebellum. UAS7 values were positively associated with GM volume in the left fusiform cortex. In CSU patients relative to HCs, the left fusiform cortex as extracted by VBM analysis demonstrated decreased functional connectivity with the right orbitofrontal cortex, medial prefrontal cortex (mPFC), premotor cortex, primary motor cortex (MI), and cerebellum and increased functional connectivity with the right posterior insular cortex, primary somatosensory cortex (SI), and secondary somatosensory cortex (SII). The left cerebellum as extracted from VBM analysis demonstrated decreased functional connectivity with the right supplementary motor area (SMA) and MI in CSU patients relative to HCs.
CONCLUSIONS: Our findings indicate that patients suffering from chronic itching conditions, such as CSU, are likely to demonstrate altered GM volume in some brain regions. These changes may affect not only the sensorimotor area but also brain regions associated with cognitive function.

PMID: 33549611 [PubMed - as supplied by publisher]

Connectome-based functional connectivity markers of suicide attempt.

Tue, 02/09/2021 - 19:11
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Connectome-based functional connectivity markers of suicide attempt.

J Affect Disord. 2020 Nov 12;:

Authors: Stumps A, Jagger-Rickels A, Rothlein D, Amick M, Park H, Evans T, Fortenbaugh FC, Fortier CB, Fonda JR, Lee D, Milberg W, McGlinchey R, DeGutis J, Esterman M

Abstract
BACKGROUND: Functional brain markers of suicidality can help identify at-risk individuals and uncover underlying neurocognitive mechanism(s). Although some converging evidence has implicated dysfunction in several brain networks, suicide-related neuroimaging markers are inconsistent across studies, due to heterogeneity of neuroimaging approaches, clinical populations, and experimental methods.
METHODS: The current study aimed to address these limitations by examining resting-fMRI connectivity in a sample of post-9/11 veterans with a past suicide attempt (SA; n = 16) compared to a psychiatric control group (PC; n = 124) with no SA history but comparable past and present symptomatology, as well as a trauma control group (TC; n = 66) of trauma-exposed healthy controls. We used both a novel graph-analytic and seed-based approach to characterize SA-related connectivity differences across brain networks.
RESULTS: First, the graph-analytic approach identified the right amygdala and a region in the cognitive control network (right middle temporal gyrus; MTG) as regional SA-related hubs of dysfunction (HoD), or regions that exhibited a high number of SA-related connections. Aberrant SA-related connectivity between these hubs spanned multiple networks, including the cognitive control, default mode and visual networks. Second, the seed-based connectivity analysis that identifies SA-related differences in the strength of neural connections across the whole brain further implicated the right amygdala.
LIMITATIONS: Small sample size and potential underreporting of SA.
CONCLUSIONS: These two analytic approaches preliminarily suggest that the right amygdala and right MTG may be specific neural markers of SA that can be differentiated from neural markers of psychopathology more broadly.

PMID: 33549365 [PubMed - as supplied by publisher]

Serotonin 2A receptor polymorphism rs3803189 mediated by dynamics of default mode network: a potential biomarker for antidepressant early response.

Sun, 02/07/2021 - 19:10

Serotonin 2A receptor polymorphism rs3803189 mediated by dynamics of default mode network: a potential biomarker for antidepressant early response.

J Affect Disord. 2021 Jan 27;283:130-138

Authors: Sun Y, Tao S, Tian S, Shao J, Mo Z, Wang X, Wang H, Zhao P, Chen Z, Yao Z, Lu Q

Abstract
BACKGROUND: Serotonin 2A receptors (HTR2A) play a crucial role in the therapeutic response to antidepressant. The activity of serotonergic system could modulate the connectivity of the default mode network (DMN) in human brain. Our research investigated the influence of the single nucleotide polymorphism (SNP) of HTR2A on the early treatment response of antidepressant and their relation to dynamic changes of DMN for the first time.
METHODS: A total of 134 major depressive disorder patients and 95 healthy controls from two independent datasets were enrolled. All subjects have genotyped candidate HTR2A polymorphisms, dynamic brain parameters flexibility and integration were calculated according to the resting-state functional magnetic resonance imaging (rs-fMRI) at baseline. Patients received selective serotonin reuptake inhibitors (SSRIs) treatment with conventional dose in the next two weeks.
RESULTS: We found the correlation of the risk-associated variant belonged to HTR2A polymorphism rs3803189 with the achievements of antidepressant early response, and also with the stronger dynamic changes of DMN. Further mediation analysis indicated that the bond between rs3803189 and antidepressant early response was mediated by the integration between the right angular gyrus (AG.R) and the subcortical network (SCN), which were validated over both the main and replication datasets.
LIMITATIONS: Except the AG.R-SCN circuit, other factors which influence the relationship between rs3803189 and antidepressant therapy deserve to be explored further. Besides, heterogeneity of samples limited the power of the current result.
CONCLUSIONS: Our findings provided a potential biomarker for individual treatment sensitivity and produced positive effects on revealing the complicated gene-brain-disorder relationship.

PMID: 33548906 [PubMed - as supplied by publisher]

Neural changes following equine-assisted therapy for posttraumatic stress disorder: A longitudinal multimodal imaging study.

Sun, 02/07/2021 - 19:10
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Neural changes following equine-assisted therapy for posttraumatic stress disorder: A longitudinal multimodal imaging study.

Hum Brain Mapp. 2021 Feb 05;:

Authors: Zhu X, Suarez-Jimenez B, Zilcha-Mano S, Lazarov A, Arnon S, Lowell AL, Bergman M, Ryba M, Hamilton AJ, Hamilton JF, Turner JB, Markowitz JC, Fisher PW, Neria Y

Abstract
BACKGROUND: While effective treatments for posttraumatic stress disorder (PTSD) exist, many individuals, including military personnel and veterans fail to respond to them. Equine-assisted therapy (EAT), a novel PTSD treatment, may complement existing PTSD interventions. This study employs longitudinal neuro-imaging, including structural magnetic resonance imaging (sMRI), resting state-fMRI (rs-fMRI), and diffusion tensor imaging (DTI), to determine mechanisms and predictors of EAT outcomes for PTSD.
METHOD: Nineteen veterans with PTSD completed eight weekly group sessions of EAT undergoing multimodal MRI assessments before and after treatment. Clinical assessments were conducted at baseline, post-treatment and at 3-month follow-up.
RESULTS: At post-treatment patients showed a significant increase in caudate functional connectivity (FC) and reduction in the gray matter density of the thalamus and the caudate. The increase of caudate FC was positively associated with clinical improvement seen immediately at post-treatment and at 3-month follow-up. In addition, higher baseline caudate FC was associated with greater PTSD symptom reduction post-treatment.
CONCLUSIONS: This exploratory study is the first to demonstrate that EAT can affect functional and structural changes in the brains of patients with PTSD. The findings suggest that EAT may target reward circuitry responsiveness and produce a caudate pruning effect from pre- to post-treatment.

PMID: 33547694 [PubMed - as supplied by publisher]

Structural and Functional Changes in the Cerebellum and Brainstem in Patients with Benign Paroxysmal Positional Vertigo.

Sun, 02/07/2021 - 19:10
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Structural and Functional Changes in the Cerebellum and Brainstem in Patients with Benign Paroxysmal Positional Vertigo.

Cerebellum. 2021 Feb 06;:

Authors: Zhu Q, Chen W, Cui Y, Wu J, Shu L, Sun X, Qin Z, Tang W, Gao T, Xu Q, Jiang CY, Liu J, Du X

Abstract
Benign paroxysmal positional vertigo (BPPV) is one of the most common peripheral vestibular diseases. Since the peripheral vestibular system connects with the cerebellum via the brainstem, repeated episodic vertigo may result in progressive structural and functional changes in the cerebellum and brainstem. In the present work, voxel-based morphometry (VBM) of T1-weighted images and resting-state functional magnetic resonance imaging (fMRI) in 32 patients with BPPV and 32 matched healthy controls were used to assess cerebellar and brainstem anatomical and spontaneous resting-state brain activity alterations associated with BPPV. We used a spatially unbiased infratentorial template toolbox in combination with VBM to analyze cerebellar and brainstem gray matter volume (GMV), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo). Patients with BPPV showed decreased GMV in the right cerebellum posterior lobe/cerebellar tonsil extending to the cerebellum anterior lobe and pons relative to healthy controls. BPPV patients also exhibited significantly higher fALFF values in the right pons and left pons and higher ReHo values in the left cerebellum posterior lobe/Crus2 than the controls. Furthermore, the fALFF z-scores in the pons were positively correlated with the duration of vertigo at baseline and dizziness visual analog scale scores 1 week after canalith repositioning procedures (CRPs). BPPV patients exhibited structural and functional changes in the cerebellum and pons, which may reflect the adaptation and plasticity of these anatomical structures after repeated attacks of episodic vertigo. These results indicate that the changes in pons function may be closely related to residual dizziness after CRPs.

PMID: 33547587 [PubMed - as supplied by publisher]

Abnormal regional homogeneity and its relationship with symptom severity in cervical dystonia: a rest state fMRI study.

Sun, 02/07/2021 - 19:10
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Abnormal regional homogeneity and its relationship with symptom severity in cervical dystonia: a rest state fMRI study.

BMC Neurol. 2021 Feb 05;21(1):55

Authors: Wei S, Lu C, Chen X, Yang L, Wei J, Jiang W, Liu Y, Li HH, Qin Y, Lei Y, Qin C, Hu C, Luo S

Abstract
BACKGROUND: Although several brain networks play important roles in cervical dystonia (CD) patients, regional homogeneity (ReHo) changes in CD patients have not been clarified. We investigated to explore ReHo in CD patients at rest and analyzed its correlations with symptom severity as measured by Tsui scale.
METHODS: A total of 19 CD patients and 21 gender-, age-, and education-matched healthy controls underwent fMRI scans at rest state. Data were analyzed by ReHo method.
RESULTS: Patients showed increased ReHo in the right cerebellum crus I and decreased ReHo in the right superior medial prefrontal cortex (MPFC). Moreover, the right precentral gyrus, right insula, and bilateral middle cingulate gyrus also showed increased ReHo values. A significantly positive correlation was observed between ReHo value in the right cerebellum crus I and symptom severity (p < 0.05).
CONCLUSIONS: Our investigation suggested abnormal ReHo existed in brain regions of the "pain matrix" and salience network (the right insula and bilateral middle cingulate gyrus), the motor network (the right precentral gyrus), the cerebellum and MPFC and further highlighted the significance of these networks in the pathology of CD.

PMID: 33546628 [PubMed - as supplied by publisher]

Alterations of functional connectivity in auditory and sensorimotor neural networks: A case report in a patient with cortical deafness after bilateral putaminal hemorrhagic stroke.

Sun, 02/07/2021 - 19:10
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Alterations of functional connectivity in auditory and sensorimotor neural networks: A case report in a patient with cortical deafness after bilateral putaminal hemorrhagic stroke.

Medicine (Baltimore). 2021 Jan 22;100(3):e24302

Authors: Gwak DW, Park E, Park JS, Kim E, Kang MG, Kim AR, Lee JE, Jung SH, Jeong JG, Lee KY, Chang Y, Jung TD

Abstract
RATIONALE: Cortical deafness is a rare auditory dysfunction caused by damage to brain auditory networks. The aim was to report alterations of functional connectivity in intrinsic auditory, motor, and sensory networks in a cortical deafness patient.
PATIENT CONCERNS: A 41-year-old woman suffered a right putaminal hemorrhage. Eight years earlier, she had suffered a left putaminal hemorrhage and had minimal sequelae. She had quadriparesis, imbalance, hypoesthesia, and complete hearing loss.
DIAGNOSES: She was diagnosed with cortical deafness. After 6 months, resting-state functional magnetic resonance imaging (rs-fMRI) and diffuse tensor imaging (DTI) were performed. DTI revealed that the acoustic radiation was disrupted while the corticospinal tract and somatosensory track were intact using deterministic tracking methods. Furthermore, the patient showed decreased functional connectivity between auditory and sensorimotor networks.
INTERVENTIONS: The patient underwent in-patient stroke rehabilitation therapy for 2 months.
OUTCOMES: Gait function and ability for activities of daily living were improved. However, complete hearing impairment persisted in 6 months after bilateral putaminal hemorrhagic stroke.
LESSONS: Our case report seems to suggest that functional alterations of spontaneous neuronal activity in auditory and sensorimotor networks are related to motor and sensory impairments in a patient with cortical deafness.

PMID: 33546056 [PubMed - as supplied by publisher]

Learning Dynamic Graph Embeddings for Accurate Detection of Cognitive State Changes in Functional Brain Networks.

Sun, 02/07/2021 - 07:10
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Learning Dynamic Graph Embeddings for Accurate Detection of Cognitive State Changes in Functional Brain Networks.

Neuroimage. 2021 Feb 02;:117791

Authors: Lin Y, Hou J, Yang D, Yan C, Kim M, Laurienti PJ, Wu G

Abstract
Mounting evidence shows that brain functions and cognitive states are dynamically changing even in the resting state rather than remaining at a single constant state. Due to the relatively small changes in BOLD (blood-oxygen-level-dependent) signals across tasks, it is difficult to detect the change of cognitive status without requiring prior knowledge of the experimental design. To address this challenge, we present a dynamic graph learning approach to generate an ensemble of subject-specific dynamic graph embeddings, which allows us to use brain networks to disentangle cognitive events more accurately than using raw BOLD signals. The backbone of our method is essentially a representation learning process for projecting BOLD signals into a latent vertex-temporal domain with the greater biological underpinning of brain activities. Specifically, the learned representation domain is jointly formed by (1) a set of harmonic waves that govern the topology of whole-brain functional connectivities and (2) a set of Fourier bases that characterize the temporal dynamics of functional changes. In this regard, our dynamic graph embeddings provide a new methodology to investigate how these self-organized functional fluctuation patterns oscillate along with the evolving cognitive status. We have evaluated our proposed method on both simulated data and working memory task-based fMRI datasets, where our dynamic graph embeddings achieve higher accuracy in detecting multiple cognitive states than other state-of-the-art methods.

PMID: 33545348 [PubMed - as supplied by publisher]

Resting cerebral oxygen metabolism exhibits archetypal network features.

Sun, 02/07/2021 - 07:10
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Resting cerebral oxygen metabolism exhibits archetypal network features.

Hum Brain Mapp. 2021 Feb 05;:

Authors: Hubbard NA, Turner MP, Sitek KR, West KL, Kaczmarzyk JR, Himes L, Thomas BP, Lu H, Rypma B

Abstract
Standard magnetic resonance imaging approaches offer high-resolution but indirect measures of neural activity, limiting understanding of the physiological processes associated with imaging findings. Here, we used calibrated functional magnetic resonance imaging during the resting state to recover low-frequency fluctuations of the cerebral metabolic rate of oxygen (CMRO2 ). We tested whether functional connections derived from these fluctuations exhibited organization properties similar to those established by previous standard functional and anatomical connectivity studies. Seventeen participants underwent 20 min of resting imaging during dual-echo, pseudocontinuous arterial spin labeling, and blood-oxygen-level dependent (BOLD) signal acquisition. Participants also underwent a 10 min normocapnic and hypercapnic procedure. Brain-wide, CMRO2 low-frequency fluctuations were subjected to graph-based and voxel-wise functional connectivity analyses. Results demonstrated that connections derived from resting CMRO2 fluctuations exhibited complex, small-world topological properties (i.e., high integration and segregation, cost efficiency) consistent with those observed in previous studies using functional and anatomical connectivity approaches. Voxel-wise CMRO2 connectivity also exhibited spatial patterns consistent with four targeted resting-state subnetworks: two association (i.e., frontoparietal and default mode) and two perceptual (i.e., auditory and occipital-visual). These are the first findings to support the use of calibration-derived CMRO2 low-frequency fluctuations for detecting brain-wide organizational properties typical of healthy participants. We discuss interpretations, advantages, and challenges in using calibration-derived oxygen metabolism signals for examining the intrinsic organization of the human brain.

PMID: 33544446 [PubMed - as supplied by publisher]

Developing Multimodal Dynamic Functional Connectivity as a Neuroimaging Biomarker.

Sun, 02/07/2021 - 07:10
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Developing Multimodal Dynamic Functional Connectivity as a Neuroimaging Biomarker.

Brain Connect. 2021 Feb 05;:

Authors: Kundu S, Ming J, Stevens J

Abstract
BACKGROUND: In spite of increasing evidence highlighting the role of dynamic functional connectivity (FC) in characterizing mental disorders, there is a lack of (a) reliable statistical methods to compute dynamic connectivity; and (b) rigorous dynamic FC-based approaches for predicting mental health outcomes in heterogeneous disorders such as PTSD.
METHODS: In one of the first such efforts, we develop a reliable and accurate approach for estimating dynamic FC guided by brain structural connectivity computed using diffusion tensor imaging data (DTI) data and investigate the potential of the proposed multimodal dynamic FC to predict continuous mental health outcomes. We develop concrete measures of temporal network variability that are predictive of PTSD resilience and identify regions whose temporal connectivity fluctuations are significantly related with resilience.
RESULTS: Our results illustrate that the multimodal approach is more sensitive to connectivity change points, it can clearly detect localized brain regions where the dynamic network features such as small-worldedness, clustering coefficients and efficiency associated with resilience, and that it has superior predictive performance compared to existing static and dynamic network models when modeling PTSD resilience.
DISCUSSION: While the majority of resting state network modeling in psychiatry has focused on static functional connectivity, our novel multimodal dynamic network analyses that are sensitive to network fluctuations allowed us to provide a model of neural correlates of resilience with high accuracy compared to existing static connectivity approaches or those that do not use brain SC information, and provided us with an expanded understanding of the neurobiological causes for PTSD.

PMID: 33544014 [PubMed - as supplied by publisher]

Inhibition of emotions in healthy aging: age-related differences in brain network connectivity.

Sun, 02/07/2021 - 07:10
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Inhibition of emotions in healthy aging: age-related differences in brain network connectivity.

Brain Behav. 2021 Feb 04;:e02052

Authors: Almdahl IS, Martinussen LJ, Agartz I, Hugdahl K, Korsnes MS

Abstract
INTRODUCTION: Successful inhibition of distracting emotions is important for preserving well-being and daily functioning. There is conflicting evidence regarding the impact of healthy aging on emotional inhibition, and possible age-related alterations in the neuronal underpinnings of emotional interference processing are unexplored.
METHODS: Thirty younger (mean age 26 years; 15 women) and 30 older (mean age 71 years; 13 women) healthy adults performed a face-word emotional Stroop task while undergoing functional magnetic resonance imaging of the brain. A resting-state scan was acquired for calculating the amplitude of low-frequency fluctuations as an estimate of vascular reactivity. Comparisons of brain activation during the task were assessed in a whole-brain, voxel-wise analysis, contrasting congruent, and incongruent conditions. The canonical regions of the frontoparietal, salience, dorsal attention, and default mode networks were used as seed regions for assessing functional connectivity within and between large-scale brain networks. Task performance was evaluated using response accuracy and response time.
RESULTS: The older adults had longer response times and lower task accuracy than the younger adults, but the emotional interference effect was not significantly different between the groups. Whole-brain analysis revealed no significant age-related differences in brain activation patterns. Rescaling the data for estimated variability in vascular reactivity did not affect the results. In older adults, there was relatively stronger functional connectivity with the default mode network, the sensorimotor network, and the dorsal attention network for the frontoparietal and salience network seeds during the task. Conversely, younger adults had relatively stronger connections within and between the frontoparietal and salience networks.
CONCLUSION: In this first fMRI study of emotional Stroop interference in older and younger adults, we found that the emotional interference effect was unchanged in healthy aging and replicated the finding from non-emotional task studies that older adults have greater between-network and less within-network connectivity compared to younger adults.

PMID: 33543596 [PubMed - as supplied by publisher]

Deep Learning-based Classification of Resting-state fMRI Independent-component Analysis.

Sun, 02/07/2021 - 07:10
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Deep Learning-based Classification of Resting-state fMRI Independent-component Analysis.

Neuroinformatics. 2021 Feb 05;:

Authors: Nozais V, Boutinaud P, Verrecchia V, Gueye MF, Hervé PY, Tzourio C, Mazoyer B, Joliot M

Abstract
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 multilayer 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 for 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.

PMID: 33543442 [PubMed - as supplied by publisher]

Distinction of High- and Low-Frequency Repetitive Transcranial Magnetic Stimulation on the Functional Reorganization of the Motor Network in Stroke Patients.

Sun, 02/07/2021 - 07:10
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Distinction of High- and Low-Frequency Repetitive Transcranial Magnetic Stimulation on the Functional Reorganization of the Motor Network in Stroke Patients.

Neural Plast. 2021;2021:8873221

Authors: Guo Z, Jin Y, Bai X, Jiang B, He L, McClure MA, Mu Q

Abstract
Objective: To investigate the functional reorganization of the motor network after repetitive transcranial magnetic stimulation (rTMS) in stroke patients with motor dysfunction and the distinction between high-frequency rTMS (HF-rTMS) and low-frequency rTMS (LF-rTMS).
Methods: Thirty-three subcortical stroke patients were enrolled and assigned to the HF-rTMS group, LF-rTMS group, and sham group. Each patient of rTMS groups received either 10.0 Hz rTMS over the ipsilesional primary motor cortex (M1) or 1.0 Hz rTMS over the contralesional M1 for 10 consecutive days. A resting-state functional magnetic resonance imaging (fMRI) scan and neurological examinations were performed at baseline and after rTMS. The motor network and functional connectivities intramotor network with the core brain regions including the bilateral M1, premotor area (PMA), and supplementary motor area (SMA) were calculated. Comparisons of functional connectivities and Pearson correlation analysis between functional connectivity changes and behavioral improvement were calculated.
Results: Significant motor improvement was found after rTMS in all groups which was larger in two rTMS groups than in the sham group. The functional connectivities of the motor network were significantly increased in bilateral M1, SMA, and contralesional PMA after real rTMS. These changes were only detected in the regions of the ipsilesional hemisphere in the HF-rTMS group and in the regions of the contralesional hemisphere in the LF-rTMS group. Significantly changed functional connectivities of the intramotor network were found between the ipsilesional M1 and SMA and contralesional PMA, between contralesional M1 and contralesional SMA, between contralesional SMA and ipsilesional SMA and contralesional PMA in the HF-rTMS group in which the changed connectivity between ipsilesional M1 and contralesional PMA was obviously correlated with the motor improvement. In addition, the functional connectivity of the intramotor network between ipsilesional M1 and contralesional PMA was significantly higher in the HF-rTMS group than in the LF-rTMS group.
Conclusion: Both HF-rTMS and LF-rTMS have a positive effect on motor recovery in patients with subcortical stroke and could promote the reorganization of the motor network. HF-rTMS may contribute more to the functional connectivity reorganization of the ipsilesional motor network and realize greater benefit to the motor recovery.

PMID: 33542729 [PubMed - in process]

The neuroprogressive nature of major depressive disorder: evidence from an intrinsic connectome analysis.

Sun, 02/07/2021 - 07:10
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The neuroprogressive nature of major depressive disorder: evidence from an intrinsic connectome analysis.

Transl Psychiatry. 2021 Feb 04;11(1):102

Authors: Liu J, Fan Y, Ling-Li Zeng, Liu B, Ju Y, Wang M, Dong Q, Lu X, Sun J, Zhang L, Guo H, Futao Zhao, Weihui Li, Zhang L, Li Z, Liao M, Zhang Y, Hu D, Li L

Abstract
Major depressive disorder (MDD) is a prevailing chronic mental disorder with lifetime recurring episodes. Recurrent depression (RD) has been reported to be associated with greater severity of depression, higher relapse rate and prominent functioning impairments than first-episode depression (FED), suggesting the progressive nature of depression. However, there is still little evidence regarding brain functional connectome. In this study, 95 medication-free MDD patients (35 with FED and 60 with RD) and 111 matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (fMRI) scanning. After six months of treatment with paroxetine, 56 patients achieved clinical remission and finished their second scan. Network-based statistics analysis was used to explore the changes in functional connectivity. The results revealed that, compared with HCs, patients with FED exhibited hypoconnectivity in the somatomotor, default mode and dorsal attention networks, and RD exhibited hyperconnectivity in the somatomotor, salience, executive control, default mode and dorsal attention networks, as well as within and between salience and executive control networks. Moreover, the disrupted components in patients with current MDD did not change significantly when the patients achieved remission after treatment, and sub-hyperconnectivity and sub-hypoconnectivity were still found in those with remitted RD. Additionally, the hypoconnectivity in FED and hyperconnectivity in RD were associated with the number of episodes and total illness duration. This study provides initial evidence supporting that impairment of intrinsic functional connectivity across the course of depression is a progressive process.

PMID: 33542206 [PubMed - in process]

Functional network alterations differently associated with suicidal ideas and acts in depressed patients: an indirect support to the transition model.

Sun, 02/07/2021 - 07:10
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Functional network alterations differently associated with suicidal ideas and acts in depressed patients: an indirect support to the transition model.

Transl Psychiatry. 2021 Feb 04;11(1):100

Authors: Wagner G, Li M, Sacchet MD, Richard-Devantoy S, Turecki G, Bär KJ, Gotlib IH, Walter M, Jollant F

Abstract
The transition from suicidal ideas to a suicide act is an important topic of research for the identification of those patients at risk of acting out. We investigated here whether specific brain activity and connectivity measures at rest may be differently associated with suicidal thoughts and behaviors. A large sample of acutely depressed patients with major depressive disorder was recruited in three different centers (Montreal/Canada, Stanford/USA, and Jena/Germany), covering four different phenotypes: patients with a past history of suicide attempt (n = 53), patients with current suicidal ideas but no past history of suicide attempt (n = 40), patients without current suicidal ideation nor past suicide attempts (n = 42), and healthy comparison subjects (n = 107). 3-T resting-state functional magnetic resonance imaging (fMRI) measures of the amplitude of low-frequency fluctuation (ALFF) and degree centrality (DC) were obtained and examined in a whole-brain data-driven analysis. Past suicide attempt was associated with a double cortico-subcortical dissociation in ALFF values. Decreased ALFF and DC values mainly in a frontoparietal network and increased ALFF values in some subcortical regions (hippocampus and thalamus) distinguished suicide attempters from suicide ideators, patient controls, and healthy controls. No clear neural differences were identified in relation to suicidal ideas. Suicide attempters appear to be a distinct subgroup of patients with widespread brain alterations in functional activity and connectivity that could represent factors of vulnerability. Our results also indirectly support at the neurobiological level the relevance of the transition model described at the psychological and clinical levels. The brain bases of suicidal ideas occurrence in depressed individuals needs further investigations.

PMID: 33542184 [PubMed - in process]

The functional relevance of task-state functional connectivity.

Sun, 02/07/2021 - 07:10
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The functional relevance of task-state functional connectivity.

J Neurosci. 2021 Feb 03;:

Authors: Cole MW, Ito T, Cocuzza C, Sanchez-Romero R

Abstract
Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the human brain's ability to adaptively alter its functionality via rapid changes in inter-regional relationships. We utilized activity flow mapping - an approach for building empirically-derived network models - to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how task-evoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance.Significance StatementHuman cognition is highly dynamic, yet the human brain's functional network organization is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the brain's intrinsic (resting-state) network organization strongly contribute to brain activations during cognitive task performance. Given that cognitive task activations emerge through network interactions, we leveraged connectivity-based models to predict independent cognitive task activations using resting-state versus task-state functional connectivity. This revealed that task-related changes in functional network organization increased prediction accuracy of cognitive task activations substantially, demonstrating their likely functional relevance for dynamic cognitive processes despite the small size of these task-related network changes.

PMID: 33542083 [PubMed - as supplied by publisher]

Regression dynamic causal modeling for resting-state fMRI.

Fri, 02/05/2021 - 19:08

Regression dynamic causal modeling for resting-state fMRI.

Hum Brain Mapp. 2021 Feb 04;:

Authors: Frässle S, Harrison SJ, Heinzle J, Clementz BA, Tamminga CA, Sweeney JA, Gershon ES, Keshavan MS, Pearlson GD, Powers A, Stephan KE

Abstract
"Resting-state" functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI-regression dynamic causal modeling (rDCM)-extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

PMID: 33539625 [PubMed - as supplied by publisher]