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Segregation of salience network predicts treatment response of depression to repetitive transcranial magnetic stimulation.

Sat, 12/21/2019 - 19:09
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Segregation of salience network predicts treatment response of depression to repetitive transcranial magnetic stimulation.

Neuroimage Clin. 2019;22:101719

Authors: Fan J, Tso IF, Maixner DF, Abagis T, Hernandez-Garcia L, Taylor SF

Abstract
BACKGROUND: The present study tested the hypothesis that network segregation, a graph theoretic measure of functional organization of the brain, is correlated with treatment response in patients with major depressive disorder (MDD) undergoing repetitive transcranial magnetic stimulation (rTMS).
METHODS: Network segregation, calculated from resting state functional magnetic resonance imaging scans, was measured in 32 patients with MDD who entered a sham-controlled, double-blinded, randomized trial of rTMS to the left dorsolateral prefrontal cortex, and a cohort of 20 healthy controls (HCs). Half of the MDD patients received sham treatment in the blinded phase, followed by active rTMS in the open-label phase. The analyses focused on segregation of the following networks: default mode (DMN), salience (SN), fronto-parietal (FPN), cingulo-opercular (CON), and memory retrieval (MRN).
RESULTS: There was no differential change in network segregation comparing sham to active treatment. However, in the combined group of patients who completed active rTMS treatment (in the blinded plus open-label phases), higher baseline segregation of SN significantly predicted more symptom improvement after rTMS. Compared to HCs at baseline, MDD patients showed decreased segregation in DMN, and trend-level decreases in SN and MRN.
CONCLUSION: The results highlight the importance of network segregation in MDD, particularly in the SN, where more normal baseline segregation of SN may predict better treatment response to rTMS in depression.

PMID: 30776777 [PubMed - indexed for MEDLINE]

Little Change in Functional Brain Networks Following Acute Levodopa in Drug-Naïve Parkinson's Disease.

Fri, 12/20/2019 - 22:08
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Little Change in Functional Brain Networks Following Acute Levodopa in Drug-Naïve Parkinson's Disease.

Mov Disord. 2019 Dec 19;:

Authors: White RL, Campbell MC, Yang D, Shannon W, Snyder AZ, Perlmutter JS

Abstract
OBJECTIVE: The objective of this study was to investigate the effects of levodopa on functional brain networks in Parkinson's disease.
METHODS: We acquired resting state functional magnetic resonance imaging in 30 drug-naïve participants with Parkinson's disease and 20 age-matched healthy controls. Each participant was studied following administration of a single oral dose of either levodopa or placebo in a randomized, double-blind, crossover design.
RESULTS: The greatest observed differences in functional connectivity were between Parkinson's disease versus control participants, independent of pharmacologic intervention. By contrast, the effects of levodopa were much smaller and detectable only in the Parkinson's disease group. Moreover, although levodopa administration in the Parkinson's disease group measurably improved motor performance, it did not increase the similarity of functional connectivity in Parkinson's disease to the control group.
CONCLUSIONS: We found that a single, small dose of levodopa did not normalize functional connectivity in drug-naïve Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.

PMID: 31854465 [PubMed - as supplied by publisher]

A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks.

Fri, 12/20/2019 - 22:08
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A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks.

J Med Syst. 2019 Dec 18;44(2):37

Authors: Ramzan F, Khan MUG, Rehmat A, Iqbal S, Saba T, Rehman A, Mehmood Z

Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disorder accounting for 70%-80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases. In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages. However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied. This study explores the effectiveness of rs-fMRI for multi-class classification of AD and its associated stages including CN, SMC, EMCI, MCI, LMCI, and AD. A longitudinal cohort of resting-state fMRI of 138 subjects (25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, and 25 AD) from Alzheimer's Disease Neuroimaging Initiative (ADNI) is studied. To provide a better insight into deep learning approaches and their applications to AD classification, we investigate ResNet-18 architecture in detail. We consider the training of the network from scratch by using single-channel input as well as performed transfer learning with and without fine-tuning using an extended network architecture. We experimented with residual neural networks to perform AD classification task and compared it with former research in this domain. The performance of the models is evaluated using precision, recall, f1-measure, AUC and ROC curves. We found that our networks were able to significantly classify the subjects. We achieved improved results with our fine-tuned model for all the AD stages with an accuracy of 100%, 96.85%, 97.38%, 97.43%, 97.40% and 98.01% for CN, SMC, EMCI, LMCI, MCI, and AD respectively. However, in terms of overall performance, we achieved state-of-the-art results with an average accuracy of 97.92% and 97.88% for off-the-shelf and fine-tuned models respectively. The Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages.

PMID: 31853655 [PubMed - in process]

The Impact of Spatial Normalization Strategies on the Temporal Features of the Resting-State Functional MRI: Spatial Normalization Before rs-fMRI Features Calculation May Reduce the Reliability.

Thu, 12/19/2019 - 22:07
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The Impact of Spatial Normalization Strategies on the Temporal Features of the Resting-State Functional MRI: Spatial Normalization Before rs-fMRI Features Calculation May Reduce the Reliability.

Front Neurosci. 2019;13:1249

Authors: Qing Z, Zhang X, Ye M, Wu S, Wang X, Nedelska Z, Hort J, Zhu B, Zhang B

Abstract
Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies frequently applied the spatial normalization on fMRI time series before the calculation of temporal features (here referred to as "Prenorm"). We hypothesized that calculating the rs-fMRI features, for example, functional connectivity (FC), regional homogeneity (ReHo), or amplitude of low-frequency fluctuation (ALFF) in individual space, before the spatial normalization (referred to as "Postnorm") can be an improvement to avoid artifacts and increase the results' reliability. We utilized two datasets: (1) simulated images where temporal signal-to-noise ratio (tSNR) is kept a constant and (2) an empirical fMRI dataset with 50 healthy young subjects. For simulated images, the tSNR is constant as generated in individual space but increased after Prenorm and intersubject variability of tSNR was induced. In contrast, tSNR was kept constant after Postnorm. Consistently, for empirical images, higher tSNR, ReHo, and FC (default mode network, seed in precuneus) and lower ALFF were found after Prenorm compared to those of Postnorm. Coefficient of variability of tSNR and ALFF was higher after Prenorm compared to those of Postnorm. Moreover, the significant correlation was found between simulated tSNR after Prenorm and empirical tSNR, ALFF, and ReHo after Prenorm, indicating algorithmic variation in empirical rs-fMRI features. Furthermore, comparing to Prenorm, ALFF and ReHo showed higher intraclass correlation coefficients between two serial scans after Postnorm. Our results indicated that Prenorm may induce algorithmic intersubject variability on tSNR and reduce its reliability, which also significantly affected ALFF and ReHo. We suggest using Postnorm instead of Prenorm for future rs-fMRI studies using ALFF/ReHo.

PMID: 31849578 [PubMed]

Dynamic changes of amplitude of low-frequency fluctuations in patients with generalized anxiety disorder.

Thu, 12/19/2019 - 22:07
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Dynamic changes of amplitude of low-frequency fluctuations in patients with generalized anxiety disorder.

Hum Brain Mapp. 2019 Dec 17;:

Authors: Cui Q, Sheng W, Chen Y, Pang Y, Lu F, Tang Q, Han S, Shen Q, Wang Y, Xie A, Huang J, Li D, Lei T, He Z, Chen H

Abstract
Previous neuroimaging studies have mainly focused on alterations of static and dynamic functional connectivity in patients with generalized anxiety disorder (GAD). However, the characteristics of local brain activity over time in GAD are poorly understood. This study aimed to investigate the abnormal time-varying local brain activity of GAD by using the amplitude of low-frequency fluctuation (ALFF) method combined with sliding-window approach. Group comparison results showed that compared with healthy controls (HCs), patients with GAD exhibited increased dynamic ALFF (dALFF) variability in widespread regions, including the bilateral dorsomedial prefrontal cortex, hippocampus, thalamus, striatum; and left orbital frontal gyrus, inferior parietal lobule, temporal pole, inferior temporal gyrus, and fusiform gyrus. The abnormal dALFF could be used to distinguish between patients with GAD and HCs. Increased dALFF variability values in the striatum were positively correlated with GAD symptom severity. These findings suggest that GAD patients are associated with abnormal temporal variability of local brain activity in regions implicated in executive, emotional, and social function. This study provides insight into the brain dysfunction of GAD from the perspective of dynamic local brain activity, highlighting the important role of dALFF variability in understanding neurophysiological mechanisms and potentially informing the diagnosis of GAD.

PMID: 31849148 [PubMed - as supplied by publisher]

Effects of social subordination and estradiol on resting-state amygdala functional connectivity in adult female rhesus monkeys.

Wed, 12/18/2019 - 22:06
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Effects of social subordination and estradiol on resting-state amygdala functional connectivity in adult female rhesus monkeys.

J Neuroendocrinol. 2019 Dec 17;:e12822

Authors: Reding KM, Grayson DS, Miranda-Dominguez O, Ray S, Wilson ME, Toufexis D, Fair DA, Sanchez MM

Abstract
Preclinical studies demonstrate that chronic stress modulates the effects of oestradiol (E2) on behavior through the modification of amygdala and medial prefrontal cortex (mPFC) neuronal structure. Clinical studies suggest that alterations in amygdala functional connectivity (FC) with the mPFC may be associated with stress-related phenotypes, including mood and anxiety disorders. Thus, identifying the effects of stress and E2 on amygdala-mPFC circuits is critical to understanding the neurobiology underpinning vulnerability to stress-related disorders in women. Here, we used a well-validated rhesus monkey model of chronic psychosocial stress (subordinate social rank) to examine effects of E2 on subordinate (SUB) -high stress- and dominant (DOM) -low stress- female resting-state amygdala FC with the mPFC and with the whole-brain. In the non-E2 treatment control condition SUB was associated with stronger left amygdala FC to subgenual cingulate (Brodmann area [BA] 25: BA25), a region implicated in several psychopathologies in people. In SUB females E2 treatment strengthened right amygdala-BA25 FC, induced a net positive amygdala-visual cortex FC that was positively associated with frequency of submissive behaviors, and weakened positive amygdala-para/hippocampus FC. Our findings show that subordinate social rank alters amygdala FC and E2's impact on amygdala FC with BA25 and with regions involved in visual processing and memory encoding.

PMID: 31846515 [PubMed - as supplied by publisher]

Study on neuropathological mechanisms of primary monosymptomatic nocturnal enuresis in children using cerebral resting-state functional magnetic resonance imaging.

Wed, 12/18/2019 - 22:06
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Study on neuropathological mechanisms of primary monosymptomatic nocturnal enuresis in children using cerebral resting-state functional magnetic resonance imaging.

Sci Rep. 2019 Dec 16;9(1):19141

Authors: Zhu W, Che Y, Wang Y, Jia Z, Wan T, Wen J, Cheng J, Ren C, Wu J, Li Y, Wang Q

Abstract
Primary monosymptomatic nocturnal enuresis (PMNE) is a heterogeneous disorder, which remains a difficult condition to manage due to lack of knowledge on the underlying pathophysiological mechanisms. Here we investigated the underlying neuropathological mechanisms of PMNE with functional MRI (fMRI), combining the amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and seed-based functional connectivity (seed-based FC) analyses. Compared to the control group, PMNE group showed decreased ALFF value in the left medial orbital superior frontal gyrus (Frontal_Med_Orb_L), and increased ReHo value in the left superior occipital gyrus (Occipital_Sup_L). With left thalamus as the seed, PMNE group showed significantly decreased functional connectivity to the left medial superior frontal gyrus (Frontal_Sup_Medial_L). We conclude that these abnormal brain activities are probably important neuropathological mechanisms of PMNE in children. Furthermore, this study facilitated the understanding of underlying pathogenesis of PMNE and may provide an objective basis for the effective treatment.

PMID: 31844104 [PubMed - in process]

Simultaneous EEG/fMRI recorded during ketamine infusion in patients with major depressive disorder.

Wed, 12/18/2019 - 22:06
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Simultaneous EEG/fMRI recorded during ketamine infusion in patients with major depressive disorder.

Prog Neuropsychopharmacol Biol Psychiatry. 2019 Dec 13;:109838

Authors: McMillan R, Sumner R, Forsyth A, Campbell D, Malpas G, Maxwell E, Deng C, Hay J, Ponton R, Sundram F, Muthukumaraswamy S

Abstract
A single subanaesthetic dose of ketamine rapidly alleviates the symptoms of major depressive disorder (MDD). However, few studies have investigated the acute effects of ketamine on the BOLD pharmacological magnetic resonance imaging (phMRI) response and EEG spectra. In a randomised, double-blind, active placebo-controlled crossover trial, resting-state simultaneous EEG/fMRI was collected during infusion of ketamine or active placebo (remifentanil) in 30 participants with MDD. Montgomery-Asberg depression rating scale scores showed a significant antidepressant effect of ketamine compared to placebo (69% response rate). phMRI analyses showed BOLD signal increases in the anterior cingulate and medial prefrontal cortices and sensitivity of the decrease in subgenual anterior cingulate cortex (sgACC) BOLD signal to noise correction. EEG spectral analysis showed increased theta, high beta, low and high gamma power, and decreased delta, alpha, and low beta power with differing time-courses. Low beta and high gamma power time courses explained significant variance in the BOLD signal. Interestingly, the variance explained by high gamma power was significantly associated with non-response to ketamine, but significant associations were not found for other neurophysiological markers when noise correction was implemented. The results suggest that the decrease in sgACC BOLD signal is potentially noise and unrelated to ketamine's antidepressant effect, highlighting the importance of noise correction and multiple temporal regressors for phMRI analyses. The lack of effects significantly associated with antidepressant response suggests the phMRI methodology employed was unable to detect such effects, the effect sizes are relatively small, or that other processes, e.g. neural plasticity, underlie ketamine's antidepressant effect.

PMID: 31843628 [PubMed - as supplied by publisher]

Default mode-visual network hypoconnectivity in an autism subtype with pronounced social visual engagement difficulties.

Wed, 12/18/2019 - 22:06
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Default mode-visual network hypoconnectivity in an autism subtype with pronounced social visual engagement difficulties.

Elife. 2019 12 17;8:

Authors: Lombardo MV, Eyler L, Moore A, Datko M, Carter Barnes C, Cha D, Courchesne E, Pierce K

Abstract
Social visual engagement difficulties are hallmark early signs of autism (ASD) and are easily quantified using eye tracking methods. However, it is unclear how these difficulties are linked to atypical early functional brain organization in ASD. With resting state fMRI data in a large sample of ASD toddlers and other non-ASD comparison groups, we find ASD-related functional hypoconnnectivity between 'social brain' circuitry such as the default mode network (DMN) and visual and attention networks. An eye tracking-identified ASD subtype with pronounced early social visual engagement difficulties (GeoPref ASD) is characterized by marked DMN-occipito-temporal cortex (OTC) hypoconnectivity. Increased DMN-OTC hypoconnectivity is also related to increased severity of social-communication difficulties, but only in GeoPref ASD. Early and pronounced social-visual circuit hypoconnectivity is a key underlying neurobiological feature describing GeoPref ASD and may be critical for future social-communicative development and represent new treatment targets for early intervention in these individuals.

PMID: 31843053 [PubMed - in process]

Plasma luteinizing hormone level affects the brain activity of patients with polycystic ovary syndrome.

Tue, 12/17/2019 - 19:05

Plasma luteinizing hormone level affects the brain activity of patients with polycystic ovary syndrome.

Psychoneuroendocrinology. 2019 Nov 28;112:104535

Authors: Lai W, Li X, Zhu H, Zhu X, Tan H, Feng P, Chen L, Luo C

Abstract
OBJECTIVE: Cognitive function has been reported to be impaired in women with polycystic ovary syndrome (PCOS). This study aimed to investigate the effect of PCOS on brain activity and explore the relationship between brain activity and sex hormone levels in women with PCOS (WPCOS).
METHODS: Twenty-one women aged 18-45 years old with new-diagnosed PCOS were enrolled. Plasma levels of six sex hormones including luteinizing hormone (LH) and follicle-stimulating hormone (FSH) were tested during the 2-5 days of their menstrual periods. Twenty-seven healthy controls (HC) were recruited. Every subject underwent a resting-state functional magnetic resonance imaging (fMRI). The amplitude of low-frequency fluctuation (ALFF) of the whole brain was evaluated followed by the functional connectivity (FC) analysis. Finally, the correlation between the ALFF, FC of the significant areas and the plasma hormone levels were analyzed.
RESULTS: The patients showed increased ALFF value in the left inferior temporal gyrus (ITG.L) and decreased ALFF value in the left inferior occipital gyrus (IOG.L) as well as the superior frontal gyrus (SFG.R, P < 0.005). For the FC analysis, patients showed decreased FC in SFG.R with the right middle frontal gyrus (MFG.R, P < 0.05). The FC between SFG.R and MFG.R was negatively correlated with LH level (R=-0.594, P = 0.005) and with the LH/FSH ratio (R=-0.521, P = 0.015).
CONCLUSION: PCOS can induce changes in activities of brain regions responsible for visuospatial working memory, face processing and episodic memory. The reduced functional connectivity within the right frontal lobe is related with the high LH level in WPCOS.

PMID: 31841986 [PubMed - as supplied by publisher]

Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI.

Tue, 12/17/2019 - 19:05

Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI.

Neuroimage. 2019 Dec 10;:116433

Authors: Bolton TAW, Kebets V, Glerean E, Zöller D, Li J, Yeo BTT, Caballero-Gaudes C, Van De Ville D

Abstract
The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-of-the-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise displacement (FD) score, to regress out models of nuisance variables, and to include average FD as a covariate in group-level analyses. Here, we studied individual motion time courses at time points typically retained in fMRI analyses. We observed that even in this set of putatively clean time points, motion exhibited a very clear spatio-temporal structure, so that we could distinguish subjects into separate groups of movers with varying characteristics. Then, we showed that this spatio-temporal motion cartography tightly relates to a broad array of anthropometric and cognitive factors. Convergent results were obtained from two different analytical perspectives: univariate assessment of behavioural differences across mover subgroups unraveled defining markers, while subsequent multivariate analysis broadened the range of involved factors and clarified that multiple motion/behaviour modes of covariance overlap in the data. Our results demonstrate that even the smaller episodes of motion typically retained in fMRI analyses carry structured, behaviourally relevant information. They call for further examinations of possible biases in current regression-based motion correction strategies.

PMID: 31841680 [PubMed - as supplied by publisher]

Different Patterns of Functional Connectivity Alterations Within the Default-Mode Network and Sensorimotor Network in Basal Ganglia and Pontine Stroke.

Mon, 12/16/2019 - 22:04
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Different Patterns of Functional Connectivity Alterations Within the Default-Mode Network and Sensorimotor Network in Basal Ganglia and Pontine Stroke.

Med Sci Monit. 2019 Dec 15;25:9585-9593

Authors: Chen H, Shi M, Zhang H, Zhang YD, Geng W, Jiang L, Wang Z, Chen YC, Yin X

Abstract
BACKGROUND The aim of this study was to investigate whether patients with basal ganglia stroke and patients with pontine stroke have different types of functional connectivity (FC) alterations in the early chronic phase. MATERIAL AND METHODS We included 14 patients with pontine stroke, 17 patients with basal ganglia stroke, and 20 well-matched healthy controls (HCs). All of them underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. The independent component analysis (ICA) approach was applied to extract information regarding the default-mode network (DMN), including anterior DMN (aDMN) and posterior DMN (pDMN) components and the sensorimotor network (SMN). RESULTS Compared with HCs, patients with basal ganglia stroke exhibited significantly reduced FC in the left precuneus of the pDMN, right supplementary motor area (SMA), and right superior frontal gyrus (SFG) of the SMN. Additionally, FC in the left medial prefrontal gyrus (MFG) of the aDMN, right precuneus and right posterior cingulate cortex (PCC) of the pDMN, and left middle cingulate gyrus (mid-CC) of the SMN decreased in patients with pontine stroke. CONCLUSIONS The different patterns of FC damage in patients with basal ganglia stroke and patients with pontine stroke in the early chronic phase may provide a new method for investigating lesion-induced network plasticity.

PMID: 31838483 [PubMed - in process]