Most recent paper

Subscribe to Most recent paper feed Most recent paper
NCBI: db=pubmed; Term="resting"[All Fields] AND "fMRI"[All Fields]
Updated: 4 hours 52 min ago

Theta-burst transcranial magnetic stimulation induced functional connectivity changes between dorsolateral prefrontal cortex and default-mode-network.

Sat, 06/15/2019 - 20:51
Related Articles

Theta-burst transcranial magnetic stimulation induced functional connectivity changes between dorsolateral prefrontal cortex and default-mode-network.

Brain Imaging Behav. 2019 Jun 13;:

Authors: Shang Y, Chang D, Zhang J, Peng W, Song D, Gao X, Wang Z

Abstract
Functional connectivity (FC) is fundamental to brain function and has been implicated in many neuropsychological and neuropsychiatric disorders. It is then of great scientific and clinical interest to find a non-invasive approach to modulate FC. Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulational tool that can affect the target region and remote brain areas. While the distributed effects of TMS are postulated to be through either structural or functional connectivity, an understudied but of great scientific interest question is whether TMS can change the FC between these regions. The purpose of this study was to address this question in normal healthy brain using TMS with continuous theta burst stimulation (cTBS) pulses, which are known to have long-lasting inhibition function. FC was calculated from resting state fMRI before and after real and control (SHAM) stimulation. Compared to SHAM, the repetitive TMS (rTMS) reduces FC between the cTBS target: the left dorsolateral prefrontal cortex (lDLPFC) and brain regions within the default mode network (DMN), proving the effects of rTMS on FC. The reduction of FC might be the results of the inhibitory effects of cTBS rTMS.

PMID: 31197581 [PubMed - as supplied by publisher]

Multi-slice passband bSSFP for human and rodent fMRI at ultra-high field.

Sat, 06/15/2019 - 02:51

Multi-slice passband bSSFP for human and rodent fMRI at ultra-high field.

J Magn Reson. 2019 May 30;305:31-40

Authors: Reynaud O, da Silva AR, Gruetter R, Jelescu IO

Abstract
Balanced steady-state free precession (bSSFP) can be used as an alternative to gradient-echo (GE) EPI for BOLD functional MRI when image distortions and signal drop-outs are severe such as at ultra-high field. However, 3D-bSSFP acquisitions have distinct drawbacks on either human or animal MR systems. On clinical scanners, 3D imaging is suboptimal for localized fMRI applications. It can also display distortions when acceleration methods such as spiral read-outs are used, and, compared to multi-slice acquisitions, suffers from increased sensitivity to motion or physiological noise which further results in blurring. On pre-clinical systems, 3D acquisitions have low temporal resolution due to limited acceleration options, while single slice often results in insufficient coverage. The aim of the present study was to implement a multi-slice bSSFP acquisition with Cartesian read-out to obtain non-distorted BOLD fMRI activation maps in the human and rat brain at ultra-high field. We show that, when using a new pseudo-steady-state, the bSSFP signal characteristics are preserved. In the human brain at 7 T, we demonstrate that both task- and resting-state fMRI can be performed with multi-slice bSSFP, with a temporal SNR that matches that of 3D-bSSFP, resulting in - at least - equal performance. In the rat brain at 14 T, we show that the multi-slice bSSFP protocol has similar sensitivity to gradient-echo EPI for task fMRI, while benefitting from much reduced distortions and drop-outs. The advantages of passband bSSFP at 14 T in comparison with GE-EPI are expected to be even more marked for mouse brain.

PMID: 31195214 [PubMed - as supplied by publisher]

Functional connectivity within lateral posterior parietal cortex in moderate to severe traumatic brain injury.

Sat, 06/15/2019 - 02:51

Functional connectivity within lateral posterior parietal cortex in moderate to severe traumatic brain injury.

Neuropsychology. 2019 Jun 13;:

Authors: Venkatesan UM, Hillary FG

Abstract
OBJECTIVE: Functional brain networks converge on areas of heteromodal processing such as lateral posterior parietal cortex (PPC). Traumatic brain injury (TBI) alters global connectivity patterns secondary to both focal and diffuse damage, but little is known about how it impacts regional environments. We examined local PPC functioning in individuals with moderate-severe TBI and controls during resting-state functional magnetic resonance imaging (rs-fMRI).
METHOD: Eighteen individuals with moderate-severe TBI and 19 healthy controls underwent rs-fMRI and neurocognitive testing. Seed-based analyses characterized remote connectivity of PPC subregions. Voxelwise graph theoretical approaches were used to probe local PPC connectivity and modularity within and between groups, and to examine relationships between local functioning and cognition.
RESULTS: Seed-based findings included increased connectivity from left and right hemispheric subregions to right-lateralized default mode and frontoparietal control networks in TBI compared to controls. Graph theoretical analyses revealed increased connection strength within right PPC relative to the contralateral region in TBI. Across groups, right PPC also showed decreased betweenness centrality compared with left PPC. Groups did not differ in the extent of modularity within left or right PPC, but there was less interindividual variability in modular structure within the TBI group. Right PPC modularity significantly predicted individual differences in cognitive performance.
CONCLUSIONS: Our findings substantiate hyperconnectivity on both local and global levels after TBI and propose a special role for local right hemispheric functioning in supporting cognition independent of neurologic status. Hyperconnectivity does not appear to result from breakdown in local modular organization and may reflect shared responses to neurologic disruption among those with TBI. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

PMID: 31192652 [PubMed - as supplied by publisher]

Frequency-Dependent Relationship Between Resting-State fMRI and Glucose Metabolism in the Elderly.

Sat, 06/15/2019 - 02:51

Frequency-Dependent Relationship Between Resting-State fMRI and Glucose Metabolism in the Elderly.

Front Neurol. 2019;10:566

Authors: Jiao F, Gao Z, Shi K, Jia X, Wu P, Jiang C, Ge J, Su H, Guan Y, Shi S, Zang YF, Zuo C

Abstract
Both glucose metabolism and resting-state fMRI (RS-fMRI) signal reflect hemodynamic features. The objective of this study was to investigate their relationship in the resting-state in healthy elderly participants (n = 18). For RS-fMRI signal, regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), and degree of centrality (DC) maps were generated in multiple frequency bands. Glucose uptake was acquired with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). Linear correlation of each pair of the FDG-PET and RS-fMRI metrics was explored both in across-voxel way and in across-subject way. We found a significant across-voxel correlation between the FDG-PET and BOLD-fMRI metrics. However, only a small portion of voxels showed significant across-subject correlation between FDG-PET and BOLD-fMRI metrics. All these results were similar across all frequency bands of RS-fMRI data. The current findings indicate that FDG-PET and RS-fMRI metrics share similar spatial pattern (significant across-voxel correlation) but have different underlying physiological importance (non-significant across-subject correlation). Specifically, FDG-PET measures the mean glucose metabolism over tens of minutes, while RS-fMRI measures the dynamic characteristics. The combination of FDG-PET and RS-fMRI provides complementary information to reveal the underlying mechanisms of the brain activity and may enable more comprehensive interpretation of clinical PET-fMRI studies. Future studies would attempt to reduce the artifacts of RS-fMRI and to analyze the dynamic feature of PET signal.

PMID: 31191447 [PubMed]

Martial Arts "Kendo" and the Motivation Network During Attention Processing: An fMRI Study.

Sat, 06/15/2019 - 02:51

Martial Arts "Kendo" and the Motivation Network During Attention Processing: An fMRI Study.

Front Hum Neurosci. 2019;13:170

Authors: Fujiwara H, Ueno T, Yoshimura S, Kobayashi K, Miyagi T, Oishi N, Murai T

Abstract
Japanese martial arts, Budo, have been reported to improve cognitive function, especially attention. However, the underlying neural mechanisms of the effect of Budo on attention processing has not yet been investigated. Kendo, a type of fencing using bamboo swords, is one of the most popular forms of Budo worldwide. We investigated the difference in functional connectivity (FC) between Kendo players (KPs) and non-KPs (NKPs) during an attention-related auditory oddball paradigm and during rest. The analyses focused on the brain network related to "motivation." Resting-state functional magnetic resonance imaging (rs-fMRI) and task-based fMRI using the oddball paradigm were performed in healthy male volunteers (14 KPs and 11 NKPs). Group differences in FC were tested using CONN-software within the motivation network, which consisted of 22 brain regions defined by a previous response-conflict task-based fMRI study with a reward cue. Daily general physical activities were assessed using the International Physical Activity Questionnaire (IPAQ). We also investigated the impact of major confounders, namely, smoking habits, alcohol consumption, IPAQ score, body mass index (BMI), and reaction time (RT) in the oddball paradigm. Resting-state fMRI revealed that KPs had a significantly lower FC than NKPs between the right nucleus accumbens and right frontal eye field (FEF) within the motivation network. Conversely, KPs exhibited a significantly higher FC than NKPs between the left intraparietal sulcus (IPS) and the left precentral gyrus (PCG) within the network during the auditory oddball paradigm [statistical thresholds, False Discovery Rate (FDR) < 0.05]. These results remained significant after controlling for major covariates. Our results suggest that attenuated motivation network integrity at rest together with enhanced motivation network integrity during attentional demands might underlie the instantaneous concentration abilities of KPs.

PMID: 31191277 [PubMed]

Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety).

Sat, 06/15/2019 - 02:51

Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety).

Front Hum Neurosci. 2019;13:164

Authors: Al-Zubaidi A, Mertins A, Heldmann M, Jauch-Chara K, Münte TF

Abstract
Objective: Resting-state functional magnetic resonance imaging (rs-fMRI) has become an essential measure to investigate the human brain's spontaneous activity and intrinsic functional connectivity. Several studies including our own previous work have shown that the brain controls the regulation of energy expenditure and food intake behavior. Accordingly, we expected different metabolic states to influence connectivity and activity patterns in neuronal networks.
Methods: The influence of hunger and satiety on rs-fMRI was investigated using three connectivity models (local connectivity, global connectivity and amplitude rs-fMRI signals). After extracting the connectivity parameters of 90 brain regions for each model, we used sequential forward floating selection strategy in conjunction with a linear support vector machine classifier and permutation tests to reveal which connectivity model differentiates best between metabolic states (hunger vs. satiety).
Results: We found that the amplitude of rs-fMRI signals is slightly more precise than local and global connectivity models in order to detect resting brain changes during hunger and satiety with a classification accuracy of 81%.
Conclusion: The amplitude of rs-fMRI signals serves as a suitable basis for machine learning based classification of brain activity. This opens up the possibility to apply this combination of algorithms to similar research questions, such as the characterization of brain states (e.g., sleep stages) or disease conditions (e.g., Alzheimer's disease, minimal cognitive impairment).

PMID: 31191274 [PubMed]

A multivoxel pattern analysis framework with mutual connectivity analysis investigating changes in resting state connectivity in patients with HIV associated neurocognitve disorder.

Thu, 06/13/2019 - 20:49
Related Articles

A multivoxel pattern analysis framework with mutual connectivity analysis investigating changes in resting state connectivity in patients with HIV associated neurocognitve disorder.

Magn Reson Imaging. 2019 Jun 09;:

Authors: DSouza AM, Abidin AZ, Schifitto G, Wismüller A

Abstract
Functional MRI (fMRI) quantifies brain activity non-invasively by measuring the blood oxygen level dependent (BOLD) response to neuronal activity. It was recently demonstrated, on realistic fMRI simulations, that nonlinear connectivity approaches, such as Mutual Connectivity Analysis with Local Models (MCA-LM), are better suited for extracting connectivity measures than conventional techniques of cross-correlating time-series pairs. In this work, we investigate the application of MCA-LM in extracting meaningful connectivity measures aiding in distinguishing healthy controls from individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND), which occurs as a result of HIV infection of the central nervous system. The pairwise connectivity measures provide a high-dimensional representation of connectivity profiles for subjects and are used as features for classification. We adopt feature selection (FS) techniques reducing the number of redundant and noisy features, while also controlling the complexity of the classifiers. We investigate three FS techniques: 1) Kendall's τ, 2) Information Gain Attribute selection 3) ReliefF and two classifiers:1) AdaBoost and 2) Random Forests. Our results demonstrate that MCA-LM consistently outperforms correlation in terms of Area under the Receiver Operating Characteristic Curve and accuracy. Improved performance with MCA-LM suggests that such a nonlinear approach is better at capturing meaningful connectivity relationships between brain regions. This demonstrates potential for developing novel neuroimaging-derived biomarkers for HAND. Furthermore, FS helps identify connections between anatomical regions that are affected by HAND. In this work, we show that the regions of the basal ganglia and frontal cortex, which are known to be affected by HAND according to current literature, are identified as most discriminative.

PMID: 31189074 [PubMed - as supplied by publisher]

Abnormal intrinsic functional hubs and connectivity in stable patients with COPD: a resting-state MRI study.

Thu, 06/13/2019 - 20:49
Related Articles

Abnormal intrinsic functional hubs and connectivity in stable patients with COPD: a resting-state MRI study.

Brain Imaging Behav. 2019 Jun 11;:

Authors: Li H, Xin H, Yu J, Yu H, Zhang J, Wang W, Peng D

Abstract
Chronic obstructive pulmonary disease (COPD) affects a large population and is closely associated with cognitive impairment. However, the mechanisms of cognitive impairment in COPD patients have not been unraveled. This study investigated the change in patterns of intrinsic functional hubs using a degree centrality (DC) analysis. The connectivity between these abnormal hubs with the remaining brain was also investigated using functional connectivity (FC). Nineteen stable patients with COPD and 20 normal controls(NC) underwent functional magnetic resonance imaging (MRI) examinations and clinical and neuropsychologic assessments. We measured the voxel-wise DC across the whole brain gray matter and the seed-based FC between these abnormal hubs in the remaining brain matter; the group difference was calculated. A partial correlation analysis was performed to assess the relationship between the abnormal DC and clinical variables in COPD patients. Compared to NC, the patients with COPD exhibited significantly decreased DC in the right lingual gyrus (LG), bilateral supplementary motor area (SMA), and right paracentral lobule (PCL). A further seed-based FC analysis found that COPD patients demonstrated significantly decreased FC between these abnormal hubs in several brain areas, including the left cerebellum anterior lobe, left lingual gyrus, left fusiform gyrus, right insula, right inferior frontal gyrus, limbic lobe, cingulate gyrus, left putamen, lentiform nucleus, right precuneus, and right paracentral lobule. A partial correlation analysis showed that the decreased DC in the right PCL was positively correlated with the FEV1 and FEV1/FVC, and the decreased DC in the SMA was positively correlated with naming and pH in COPD patients. This study demonstrates that there are intrinsic functional hubs and connectivity alterations that may reflect the aberrant information communication in the brain of COPD patients. These findings may help provide new insight for understanding the mechanisms of COPD-related cognitive impairment from whole brain functional connections.

PMID: 31187474 [PubMed - as supplied by publisher]

DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders.

Thu, 06/13/2019 - 20:49
Related Articles

DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders.

Neuroinformatics. 2019 Jun 11;:

Authors: Syed MA, Yang Z, Rangaprakash D, Hu X, Dretsch MN, Katz JS, Denney TS, Deshpande G

Abstract
There is a lack of objective biomarkers to accurately identify the underlying etiology and related pathophysiology of disparate brain-based disorders that are less distinguishable clinically. Brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been a popular tool for discovering candidate biomarkers. Specifically, independent component analysis (ICA) of rs-fMRI data is a powerful multivariate technique for investigating brain networks. However, ICA-derived brain networks that are not highly reproducible within heterogeneous clinical populations may exhibit mean statistical separation between groups, yet not be sufficiently discriminative at the individual-subject level. We hypothesize that functional brain networks that are most reproducible in subjects within clinical and control groups separately, but not when the two groups are merged, may possess the ability to discriminate effectively between the groups even at the individual-subject level. In this study, we present DisConICA or "Discover Confirm Independent Component Analysis", a software package that implements the methodology in support of our hypothesis. It relies on a "discover-confirm" approach based upon the assessment of reproducibility of independent components (representing brain networks) obtained from rs-fMRI (discover phase) using the gRAICAR (generalized Ranking and Averaging Independent Component Analysis by Reproducibility) algorithm, followed by unsupervised clustering analysis of these components to evaluate their ability to discriminate between groups (confirm phase). The unique feature of our software package is its ability to seamlessly interface with other software packages such as SPM and FSL, so that all related analyses utilizing features of other software can be performed within our package, thus providing a one-stop software solution starting with raw DICOM images to the final results. We showcase our software using rs-fMRI data acquired from US Army soldiers returning from the wars in Iraq and Afghanistan who were clinically grouped into the following groups: PTSD (posttraumatic stress disorder), comorbid PCS (post-concussion syndrome) + PTSD, and matched healthy combat controls. This software package along with test data sets is available for download at https://bitbucket.org/masauburn/disconica .

PMID: 31187352 [PubMed - as supplied by publisher]

Impaired functional connectivity of limbic system in migraine without aura.

Wed, 06/12/2019 - 23:49
Related Articles

Impaired functional connectivity of limbic system in migraine without aura.

Brain Imaging Behav. 2019 Jun 10;:

Authors: Wei HL, Chen J, Chen YC, Yu YS, Zhou GP, Qu LJ, Yin X, Li J, Zhang H

Abstract
Aberrant functional connectivity of brain networks has been demonstrated in migraine sufferers. Functional magnetic resonance imaging (fMRI) may illustrate altered connectivity in patients suffering from migraine without aura (MwoA). Here, we applied a seed-based approach based on limbic regions to investigate disrupted functional connectivity between spontaneous migraine attacks. Resting-state fMRI data were obtained from 28 migraine patients without aura and 23 well-matched healthy controls (HC). The functional connectivity of the limbic system was characterized using a seed-based whole-brain correlation method. The resulting functional connectivity measurements were assessed for correlations with other clinical features. Neuropsychological data revealed significantly increased connectivity between the limbic system (bilateral amygdala and right hippocampus) and left middle occipital gyrus (MOG), and a positive correlation was revealed between disease duration and connective intensity of the left amygdala and the ipsilateral MOG. There was decreased functional connectivity between the right amygdala and contralateral orbitofrontal cortex (OFC). In addition, resting-state fMRI showed that, compared to HC, patients without aura had significant functional connectivity consolidation between the bilateral hippocampus and cerebellum, and a negative correlation was detected between scores on the headache impact test (HIT) and connectivity intensity of the right hippocampus and bilateral cerebellum. There was decreased functional connectivity between the left hippocampus and three brain areas, encompassing the bilateral inferior parietal gyri (IPG) and contralateral supplementary motor area (SMA). There were no structural differences between the two groups. Our data suggest that migraine patients have disrupted limbic functional connectivity to pain-related regions of the modulatory and encoding cortices, which are associated with specific clinical characteristics. Disturbances of resting-state functional connectivity may play a key role in neuropathological features, perception and affection of migraine. The current study provides further insights into the complex scenario of migraine mechanisms. .

PMID: 31183773 [PubMed - as supplied by publisher]

The functional organisation of the hippocampus along its long axis is gradual and predicts recollection.

Tue, 06/11/2019 - 20:47
Related Articles

The functional organisation of the hippocampus along its long axis is gradual and predicts recollection.

Cortex. 2019 May 07;119:324-335

Authors: Przeździk I, Faber M, Fernández G, Beckmann CF, Haak KV

Abstract
Understanding the functional organisation of the hippocampus is crucial for understanding its role in cognition and disorders in which it is implicated. Different views have been proposed of how function is distributed along its long axis: one view suggests segregation, whereas the alternative view postulates a more gradual organisation. Here, we applied a novel 'connectopic mapping' data-analysis approach to the resting-state fMRI data of participants of the Human Connectome Project, and demonstrate that the functional organisation of the hippocampal longitudinal axis is gradual rather than segregated into parcels. In addition, we show that inter-individual variations in this gradual organisation predict variations in recollection memory better than a characterisation based on functional parcellation. These results present an important step forward in understanding the functional organisation of the human hippocampus and have important implications for translating between rodent and human research.

PMID: 31181420 [PubMed - as supplied by publisher]

In vivo widefield calcium imaging of the mouse cortex for analysis of network connectivity in health and brain disease.

Tue, 06/11/2019 - 20:47
Related Articles

In vivo widefield calcium imaging of the mouse cortex for analysis of network connectivity in health and brain disease.

Neuroimage. 2019 Jun 07;:

Authors: Cramer JV, Gesierich B, Roth S, Dichgans M, Düring M, Liesz A

Abstract
The organization of brain areas in functionally connected networks, their dynamic changes, and perturbations in disease states are subject of extensive investigations. Research on functional networks in humans predominantly uses functional magnetic resonance imaging (fMRI). However, adopting fMRI and other functional imaging methods to mice, the most widely used model to study brain physiology and disease, poses major technical challenges and faces important limitations. Hence, there is great demand for alternative imaging modalities for network characterization. Here, we present a refined protocol for in vivo widefield calcium imaging of both cerebral hemispheres in mice expressing a calcium sensor in excitatory neurons. We implemented a stringent protocol for minimizing anesthesia and excluding movement artifacts which both imposed problems in previous approaches. We further adopted a method for unbiased identification of functional cortical areas using independent component analysis (ICA) on resting-state imaging data. Biological relevance of identified components was confirmed using stimulus-dependent cortical activation. To explore this novel approach in a model of focal brain injury, we induced photothrombotic lesions of the motor cortex, determined changes in inter- and intrahemispheric connectivity at multiple time points up to 56 days post-stroke and correlated them with behavioral deficits. We observed a severe loss in interhemispheric connectivity after stroke, which was partially restored in the chronic phase and associated with corresponding behavioral motor deficits. Taken together, we present an improved widefield calcium imaging tool accounting for anesthesia and movement artifacts, adopting an advanced analysis pipeline based on human fMRI algorithms and with superior sensitivity to recovery mechanisms in mouse models compared to behavioral tests. This tool will enable new studies on interhemispheric connectivity in murine models with comparability to human imaging studies for a wide spectrum of neuroscience applications in health and disease.

PMID: 31181333 [PubMed - as supplied by publisher]

Multimodal imaging reveals a complex pattern of dysfunction in corticolimbic pathways in major depressive disorder.

Tue, 06/11/2019 - 20:47
Related Articles

Multimodal imaging reveals a complex pattern of dysfunction in corticolimbic pathways in major depressive disorder.

Hum Brain Mapp. 2019 Jun 09;:

Authors: Nugent AC, Farmer C, Evans JW, Snider SL, Banerjee D, Zarate CA

Abstract
Major depressive disorder (MDD) is highly prevalent and associated with considerable morbidity, yet its pathophysiology remains only partially understood. While numerous studies have investigated the neurobiological correlates of MDD, most have used only a single neuroimaging modality. In particular, diffusion tensor imaging (DTI) studies have failed to yield uniform results. In this context, examining key tracts and using information from multiple neuroimaging modalities may better characterize potential abnormalities in the MDD brain. This study analyzed data from 30 participants with MDD and 26 healthy participants who underwent DTI, magnetic resonance spectroscopy (MRS), resting-state functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG). Tracts connecting the subgenual anterior cingulate cortex (sgACC) and the left and right amygdala, as well as connections to the left and right hippocampus and thalamus, were examined as target areas. Reduced fractional anisotropy (FA) was observed in the studied tracts. Significant differences in the correlation between medial prefrontal glutamate concentrations and FA were also observed between MDD and healthy participants along tracts connecting the sgACC and right amygdala; healthy participants exhibited a strong correlation but MDD participants showed no such relationship. In the same tract, a correlation was observed between FA and subsequent antidepressant response to ketamine infusion in MDD participants. Exploratory models also suggested group differences in the relationship between DTI, fMRI, and MEG measures. This study is the first to combine MRS, DTI, fMRI, and MEG data to obtain multimodal indices of MDD and antidepressant response and may lay the foundation for similar future analyses.

PMID: 31179620 [PubMed - as supplied by publisher]

Stimuli, presentation modality, and load-specific brain activity patterns during n-back task.

Tue, 06/11/2019 - 20:47
Related Articles

Stimuli, presentation modality, and load-specific brain activity patterns during n-back task.

Hum Brain Mapp. 2019 Jun 09;:

Authors: Mencarelli L, Francesco N, Davide M, Arianna M, Simone R, Alessandro R, Emiliano S

Abstract
Working memory (WM) refers to a set of cognitive processes that allows for the temporary storage and manipulation of information, crucial for everyday life skills. WM deficits are present in several neurological, psychiatric, and neurodevelopmental disorders, thus making the full understanding of its neural correlates a key aspect for the implementation of cognitive training interventions. Here, we present a quantitative meta-analysis focusing on the underlying neural substrates upon which the n-back, one of the most commonly used tasks for WM assessment, is believed to rely on, as highlighted by functional magnetic resonance imaging and positron emission tomography findings. Relevant published work was scrutinized through the activation likelihood estimate (ALE) statistical framework in order to generate a set of task-specific activation maps, according to n-back difficulty. Our results confirm the known involvement of frontoparietal areas across different types of n-back tasks, as well as the recruitment of subcortical structures, cerebellum and precuneus. Specific activations maps for four stimuli types, six presentation modalities, three WM loads and their combination are provided and discussed. Moreover, functional overlap with resting-state networks highlighted a strong similarity between n-back nodes and the Dorsal Attention Network, with less overlap with other networks like Salience, Language, and Sensorimotor ones. Additionally, neural deactivations during n-back tasks and their functional connectivity profile were examined. Clinical and functional implications are discussed in the context of potential noninvasive brain stimulation and cognitive enhancement/rehabilitation programs.

PMID: 31179585 [PubMed - as supplied by publisher]

Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis.

Tue, 06/11/2019 - 20:47
Related Articles

Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis.

Med Image Comput Comput Assist Interv. 2018 Sep;11072:249-257

Authors: Yan W, Zhang H, Sui J, Shen D

Abstract
Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for disease diagnosis, where discriminating subjects with mild cognitive impairment (MCI) from normal controls (NC) is still one of the most challenging problems. Dynamic functional connectivity (dFC), consisting of time-varying spatiotemporal dynamics, may characterize "chronnectome" diagnostic information for improving MCI classification. However, most of the current dFC studies are based on detecting discrete major "brain status" via spatial clustering, which ignores rich spatiotemporal dynamics contained in such chronnectome. We propose Deep Chronnectome Learning for exhaustively mining the comprehensive information, especially the hidden higher-level features, i.e., the dFC time series that may add critical diagnostic power for MCI classification. To this end, we devise a new Fully-connected bidirectional Long Short-Term Memory (LSTM) network (Full-BiLSTM) to effectively learn the periodic brain status changes using both past and future information for each brief time segment and then fuse them to form the final output. We have applied our method to a rigorously built large-scale multi-site database (i.e., with 164 data from NCs and 330 from MCIs, which can be further augmented by 25 folds). Our method outperforms other state-of-the-art approaches with an accuracy of 73.6% under solid cross-validations. We also made extensive comparisons among multiple variants of LSTM models. The results suggest high feasibility of our method with promising value also for other brain disorder diagnoses.

PMID: 31179447 [PubMed - in process]

Alterations of Interhemispheric Functional Connectivity and Degree Centrality in Cervical Dystonia: A Resting-State fMRI Study.

Tue, 06/11/2019 - 20:47
Related Articles

Alterations of Interhemispheric Functional Connectivity and Degree Centrality in Cervical Dystonia: A Resting-State fMRI Study.

Neural Plast. 2019;2019:7349894

Authors: Jiang W, Lei Y, Wei J, Yang L, Wei S, Yin Q, Luo S, Guo W

Abstract
Background: Cervical dystonia (CD) is a neurological movement disorder characterized by involuntary head and neck movements and postures. Reports on microstructural and functional abnormalities in multiple brain regions not limited to the basal ganglia have been increasing in patients with CD. However, the neural bases of CD are unclear. This study is aimed at identifying cerebral functional abnormalities in CD by using resting-state functional magnetic resonance imaging (rs-fMRI).
Methods: Using rs-fMRI data, voxel-mirrored homotopic connectivity (VMHC) and degree centrality were used to compare the alterations of the rs-functional connectivity (FC) between 19 patients with CD and 21 healthy controls. Regions showing abnormal FCs from two measurements were the regions of interest for correlation analyses.
Results: Compared with healthy controls, patients with CD exhibited significantly decreased VMHC in the supplementary motor area (SMA), precuneus (PCu)/postcentral gyrus, and superior medial prefrontal cortex (MPFC). Significantly increased degree centrality in the right PCu and decreased degree centrality in the right lentiform nucleus and left ventral MPFC were observed in the patient group compared with the control group. Further correlation analyses showed that the VMHC values in the SMA were negatively correlated with dystonia severity.
Conclusion: Local abnormalities and interhemispheric interaction deficits in the sensorimotor network (SMA, postcentral gyrus, and PCu), default mode network (MPFC and PCu), and basal ganglia may be the key characteristics in the pathogenesis mechanism of CD.

PMID: 31178903 [PubMed - in process]

Impact of Hunger, Satiety, and Oral Glucose on the Association Between Insulin and Resting-State Human Brain Activity.

Tue, 06/11/2019 - 20:47
Related Articles

Impact of Hunger, Satiety, and Oral Glucose on the Association Between Insulin and Resting-State Human Brain Activity.

Front Hum Neurosci. 2019;13:162

Authors: Al-Zubaidi A, Heldmann M, Mertins A, Brabant G, Nolde JM, Jauch-Chara K, Münte TF

Abstract
To study the interplay of metabolic state (hungry vs. satiated) and glucose administration (including hormonal modulation) on brain function, resting-state functional magnetic resonance imaging (rs-fMRI) and blood samples were obtained in 24 healthy normal-weight men in a repeated measurement design. Participants were measured twice: once after a 36 h fast (except water) and once under satiation (three meals/day for 36 h). During each session, rs-fMRI and hormone concentrations were recorded before and after a 75 g oral dose of glucose. We calculated the amplitude map from blood-oxygen-level-dependent (BOLD) signals by using the fractional amplitude of low-frequency fluctuation (fALFF) approach for each volunteer per condition. Using multiple linear regression analysis (MLRA) the interdependence of brain activity, plasma insulin and blood glucose was investigated. We observed a modulatory impact of fasting state on intrinsic brain activity in the posterior cingulate cortex (PCC). Strikingly, differences in plasma insulin levels between hunger and satiety states after glucose administration at the time of the scan were negatively related to brain activity in the posterior insula and superior frontal gyrus (SFG), while plasma glucose levels were positively associated with activity changes in the fusiform gyrus. Furthermore, we could show that changes in plasma insulin enhanced the connectivity between the posterior insula and SFG. Our results indicate that hormonal signals like insulin alleviate an acute hemostatic energy deficit by modifying the homeostatic and frontal circuitry of the human brain.

PMID: 31178708 [PubMed]

Alteration of cerebello-thalamocortical spontaneous low frequency oscillations in juvenile myoclonic epilepsy.

Mon, 06/10/2019 - 23:46
Related Articles

Alteration of cerebello-thalamocortical spontaneous low frequency oscillations in juvenile myoclonic epilepsy.

Acta Neurol Scand. 2019 Jun 08;:

Authors: Kim JH, Kim JB, Suh SI

Abstract
OBJECTIVE: Altered thalamocortical network has been proposed to play a pivotal role in the principal pathophysiology underlying juvenile myoclonic epilepsy (JME). Recently, resting-state fMRI studies have provided converging evidence for thalamocortical dysconnectivity in patients with JME. Herein, we investigated the amplitude and spatial distribution of spontaneous low frequency oscillations using analysis of fractional amplitude of low-frequency fluctuation (fALFF) in a large group of JME patients in comparison with controls.
METHODS: Volumetric MRI and resting-state fMRI were acquired in 75 patients with JME and 62 matched controls. After preprocessing of MRI data, fALFF was computed and then Z-transformed for standardization. fALFF was compared between controls and patients, and correlation analysis between regional fALFF and clinical parameters were performed in patients.
RESULTS: Compared with controls, JME patients revealed significant fALFF increases in the bilateral medial thalamus, insular cortex/inferior frontal gyrus, and cerebellum vermis (false discovery rate-corrected P < .05). There was no region of fALFF reduction in JME patients relative to controls. No significant correlation was observed between regional fALFF and disease duration or cumulative number of generalized tonic-clonic seizures.
CONCLUSIONS: We have shown alterations of low frequency oscillations in the thalamus, insular cortex/inferior frontal gyrus, and cerebellum in patients with JME, implicating cerebello-thalamocortical network abnormality in the pathophysiology underlying JME. Our results could further support the recent concept that JME is a network epilepsy involving specific cortical and subcortical structures, especially the cerebello-thalamocortical network. This article is protected by copyright. All rights reserved.

PMID: 31177545 [PubMed - as supplied by publisher]

Resting State Abnormalities of the Default Mode Network in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.

Mon, 06/10/2019 - 23:46
Related Articles

Resting State Abnormalities of the Default Mode Network in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.

J Alzheimers Dis. 2019 Jun 04;:

Authors: Eyler LT, Elman JA, Hatton SN, Gough S, Mischel AK, Hagler DJ, Franz CE, Docherty A, Fennema-Notestine C, Gillespie N, Gustavson D, Lyons MJ, Neale MC, Panizzon MS, Dale AM, Kremen WS

Abstract
BACKGROUND: Large-scale brain networks such as the default mode network (DMN) are often disrupted in Alzheimer's disease (AD). Numerous studies have examined DMN functional connectivity in those with mild cognitive impairments (MCI), a presumed AD precursor, to discover a biomarker of AD risk. Prior reviews were qualitative or limited in scope or approach.
OBJECTIVE: We aimed to systematically and quantitatively review DMN resting state fMRI studies comparing MCI and healthy comparison (HC) groups.
METHODS: PubMed was searched for relevant articles. Study characteristics were abstracted and the number of studies showing no difference in hyper- versus hypo-connectivity in MCI was tallied. A voxel-wise (ES-SDM) meta-analysis was conducted to identify regional group differences.
RESULTS: Qualitatively, our review of 57 MCI versus HC comparisons suggests substantial inconsistency; 9 showed no group difference, 8 showed MCI > HC and 22 showed HC > MCI across the brain, and 18 showed regionally-mixed directions of effect. The meta-analysis of 31 studies revealed areas of significant hypo- and hyper-connectivity in MCI, including hypoconnectivity in the posterior cingulate cortex/precuneus (z = -3.1, p < 0.0001). Very few individual studies, however, showed patterns resembling the meta-analytic results. Methodological differences did not appear to explain inconsistencies.
CONCLUSIONS: The pattern of altered resting DMN function or connectivity in MCI is complex and variable across studies. To date, no index of DMN connectivity qualifies as a useful biomarker of MCI or risk for AD. Refinements to MCI diagnosis, including other biological markers, or longitudinal studies of progression to AD, might identify DMN alterations predictive of AD risk.

PMID: 31177210 [PubMed - as supplied by publisher]

Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain.

Sun, 06/09/2019 - 20:43

Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain.

Neuroimage Clin. 2019 May 28;23:101885

Authors: Tu Y, Ortiz A, Gollub RL, Cao J, Gerber J, Lang C, Park J, Wilson G, Shen W, Chan ST, Wasan AD, Edwards RR, Napadow V, Kaptchuk TJ, Rosen B, Kong J

Abstract
Despite the high prevalence and socioeconomic impact of chronic low back pain (cLBP), treatments for cLBP are often unsatisfactory, and effectiveness varies widely across patients. Recent neuroimaging studies have demonstrated abnormal resting-state functional connectivity (rsFC) of the default mode, salience, central executive, and sensorimotor networks in chronic pain patients, but their role as predictors of treatment responsiveness has not yet been explored. In this study, we used machine learning approaches to test if pre-treatment rsFC can predict responses to both real and sham acupuncture treatments in cLBP patients. Fifty cLBP patients participated in 4 weeks of either real (N = 24, age = 39.0 ± 12.6, 16 females) or sham acupuncture (N = 26, age = 40.0 ± 13.7, 15 females) treatment in a single-blinded trial, and a resting-state fMRI scan prior to treatment was used in data analysis. Both real and sham acupuncture can produce significant pain reduction, with those receiving real treatment experiencing greater pain relief than those receiving sham treatment. We found that pre-treatment rsFC could predict symptom changes with up to 34% and 29% variances for real and sham treatment, respectively, and the rsFC characteristics that were significantly predictive for real and sham treatment differed. These results suggest a potential way to predict treatment responses and may facilitate the development of treatment plans that optimize time, cost, and available resources.

PMID: 31176295 [PubMed - as supplied by publisher]