New resting-state fMRI related studies at PubMed

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Functional Magnetic Resonance Imaging in Huntington's Disease.

Sat, 11/10/2018 - 11:40
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Functional Magnetic Resonance Imaging in Huntington's Disease.

Int Rev Neurobiol. 2018;142:381-408

Authors: Gregory S, Scahill RI

Abstract
Huntington's disease is an inherited neurodegenerative condition characterized by motor dysfunction, cognitive impairment and neuropsychiatric disturbance. The effects of the underlying pathology on brain morphology are relatively well understood. Numerous structural Magnetic Resonance Imaging (MRI) studies have demonstrated macrostructural change with widespread striatal and cortical atrophy and microstructural white matter loss in premanifest and manifest HD gene carriers. However, disease effects on brain function are less well characterized. Functional MRI provides an opportunity to examine differences in brain activity either in response to a particular task or in the brain at rest. There is increasing evidence that HD gene carriers exhibit altered activation patterns and functional connectivity between brain regions in response to the neurodegenerative process. Here we review the growing literature in this area and critically evaluate the utility of this imaging modality.

PMID: 30409260 [PubMed - in process]

Functional MRI in Atypical Parkinsonisms.

Sat, 11/10/2018 - 11:40
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Functional MRI in Atypical Parkinsonisms.

Int Rev Neurobiol. 2018;142:149-173

Authors: Agosta F, Sarasso E, Filippi M

Abstract
The present chapter reports the current knowledge on the use of functional MRI (fMRI) in patients with atypical parkinsonisms, including Multiple System Atrophy, Corticobasal Syndrome and Progressive Supranuclear Palsy syndrome. Both resting state functional connectivity and task-based brain activity abnormalities are reported in atypical parkinsonisms relative to healthy controls and Parkinson's disease patients. Functional alterations were observed earlier than structural damage and may help to make early diagnosis. The chapter also examines the few longitudinal evidence on fMRI changes in patients with these conditions. The potential use of fMRI techniques in aiding the differential diagnosis, accurately measuring disease progression and assessing the effectiveness of therapeutic interventions is discussed.

PMID: 30409252 [PubMed - in process]

Test-retest reliability of task-based and resting-state blood oxygen level dependence and cerebral blood flow measures.

Fri, 11/09/2018 - 17:00
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Test-retest reliability of task-based and resting-state blood oxygen level dependence and cerebral blood flow measures.

PLoS One. 2018;13(11):e0206583

Authors: Holiga Š, Sambataro F, Luzy C, Greig G, Sarkar N, Renken RJ, Marsman JC, Schobel SA, Bertolino A, Dukart J

Abstract
Despite their wide-spread use, only limited information is available on the comparative test-retest reliability of task-based functional and resting state magnetic resonance imaging measures of blood oxygen level dependence (tb-fMRI and rs-fMRI) and cerebral blood flow (CBF) using arterial spin labeling. This information is critical to designing properly powered longitudinal studies. Here we comprehensively quantified and compared the test-retest reliability and reproducibility performance of 8 commonly applied fMRI tasks, 6 rs-fMRI metrics and CBF in 30 healthy volunteers. We find large variability in test-retest reliability performance across the different tb-fMRI paradigms and rs-fMRI metrics, ranging from poor to excellent. A larger extent of activation in tb-fMRI is linked to higher between-subject reliability of the respective task suggesting that differences in the amount of activation may be used as a first reliability estimate of novel tb-fMRI paradigms. For rs-fMRI, a good reliability of local activity estimates is paralleled by poor performance of global connectivity metrics. Evaluated CBF measures provide in general a good to excellent test-reliability matching or surpassing the best performing tb-fMRI and rs-fMRI metrics. This comprehensive effort allows for direct comparisons of test-retest reliability between the evaluated MRI domains and measures to aid the design of future tb-fMRI, rs-fMRI and CBF studies.

PMID: 30408072 [PubMed - in process]

Classification of Alzheimer's Disease, Mild Cognitive Impairment and Normal Control Subjects Using Resting-State fMRI Based Network Connectivity Analysis.

Fri, 11/09/2018 - 17:00
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Classification of Alzheimer's Disease, Mild Cognitive Impairment and Normal Control Subjects Using Resting-State fMRI Based Network Connectivity Analysis.

IEEE J Transl Eng Health Med. 2018;6:1801009

Authors: Wang Z, Zheng Y, Zhu DC, Bozoki AC, Li T

Abstract
This paper proposes a robust method for the Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control subject classification under size limited fMRI data samples by exploiting the brain network connectivity pattern analysis. First, we select the regions of interest (ROIs) within the default mode network and calculate the correlation coefficients between all possible ROI pairs to form a feature vector for each subject. Second, we propose a regularized linear discriminant analysis (LDA) approach to reduce the noise effect due to the limited sample size. The feature vectors are then projected onto a one-dimensional axis using the proposed regularized LDA. Finally, an AdaBoost classifier is applied to carry out the classification task. The numerical analysis demonstrates that the purposed approach can increase the classification accuracy significantly. Our analysis confirms the previous findings that the hippocampus and the isthmus of the cingulate cortex are closely involved in the development of AD and MCI.

PMID: 30405975 [PubMed]

Multimodal Neuroimaging Approach to Variability of Functional Connectivity in Disorders of Consciousness: A PET/MRI Pilot Study.

Fri, 11/09/2018 - 17:00
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Multimodal Neuroimaging Approach to Variability of Functional Connectivity in Disorders of Consciousness: A PET/MRI Pilot Study.

Front Neurol. 2018;9:861

Authors: Cavaliere C, Kandeepan S, Aiello M, Ribeiro de Paula D, Marchitelli R, Fiorenza S, Orsini M, Trojano L, Masotta O, St Lawrence K, Loreto V, Chronik BA, Nicolai E, Soddu A, Estraneo A

Abstract
Behavioral assessments could not suffice to provide accurate diagnostic information in individuals with disorders of consciousness (DoC). Multimodal neuroimaging markers have been developed to support clinical assessments of these patients. Here we present findings obtained by hybrid fludeoxyglucose (FDG-)PET/MR imaging in three severely brain-injured patients, one in an unresponsive wakefulness syndrome (UWS), one in a minimally conscious state (MCS), and one patient emerged from MCS (EMCS). Repeated behavioral assessment by means of Coma Recovery Scale-Revised and neurophysiological evaluation were performed in the two weeks before and after neuroimaging acquisition, to ascertain that clinical diagnosis was stable. The three patients underwent one imaging session, during which two resting-state fMRI (rs-fMRI) blocks were run with a temporal gap of about 30 min. rs-fMRI data were analyzed with a graph theory approach applied to nine independent networks. We also analyzed the benefits of concatenating the two acquisitions for each patient or to select for each network the graph strength map with a higher ratio of fitness. Finally, as for clinical assessment, we considered the best functional connectivity pattern for each network and correlated graph strength maps to FDG uptake. Functional connectivity analysis showed several differences between the two rs-fMRI acquisitions, affecting in a different way each network and with a different variability for the three patients, as assessed by ratio of fitness. Moreover, combined PET/fMRI analysis demonstrated a higher functional/metabolic correlation for patients in EMCS and MCS compared to UWS. In conclusion, we observed for the first time, through a test-retest approach, a variability in the appearance and temporal/spatial patterns of resting-state networks in severely brain-injured patients, proposing a new method to select the most informative connectivity pattern.

PMID: 30405513 [PubMed]

Increased Inhibition of the Amygdala by the mPFC may Reflect a Resilience Factor in Post-traumatic Stress Disorder: A Resting-State fMRI Granger Causality Analysis.

Fri, 11/09/2018 - 17:00
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Increased Inhibition of the Amygdala by the mPFC may Reflect a Resilience Factor in Post-traumatic Stress Disorder: A Resting-State fMRI Granger Causality Analysis.

Front Psychiatry. 2018;9:516

Authors: Chen F, Ke J, Qi R, Xu Q, Zhong Y, Liu T, Li J, Zhang L, Lu G

Abstract
Purpose: To determine whether effective connectivity of the amygdala is altered in traumatized subjects with and without post-traumatic stress disorder (PTSD). Materials and Methods: Resting-state functional MRI data were obtained for 27 patients with typhoon-related PTSD, 33 trauma-exposed controls (TEC), and 30 healthy controls (HC). Effective connectivity of the bilateral amygdala was examined with Granger causality analysis and then compared between groups by conducting an analysis of variance. Results: Compared to the HC group, both the PTSD group and the TEC group showed increased effective connectivity from the amygdala to the medial prefrontal cortex (mPFC). The TEC group showed increased effective connectivity from the mPFC to the amygdala relative to the HC group. Compared to the TEC group, the PTSD group showed increased effective connectivity from the amygdala to the supplementary motor area (SMA), whereas decreased effective connectivity was detected from the SMA to the amygdala. Both the PTSD group and the TEC group showed decreased effective connectivity from the superior temporal gyrus (STG) to the amygdala relative to the HC group. Compared to the HC group, the TEC group showed increased effective connectivity from the amygdala to the dorsolateral prefrontal cortex (dlPFC), while both the PTSD group and the TEC group showed decreased effective connectivity from the dlPFC to the amygdala. The PTSD group showed decreased effective connectivity from the precuneus to the amygdala relative to both control groups, but increased effective connectivity from the amygdala to the precuneus relative to the HC group. Conclusion: Trauma leads to an increased down-top excitation from the amygdala to the mPFC and less regulation of the amygdala by the dlPFC. The results suggest that increased inhibition of the amygdala by the mPFC may reflect a resilience factor, and altered amygdala-SMA and amygdala-STG effective connectivity may reflect compensatory mechanisms of brain function. These data raise the possibility that insufficient inhibition of the amygdala by the mPFC might lead to PTSD in those who have been exposed to traumatic incidents, and may inform future therapeutic interventions.

PMID: 30405457 [PubMed]

Functional Connectivity Within the Executive Control Network Mediates the Effects of Long-Term Tai Chi Exercise on Elders' Emotion Regulation.

Fri, 11/09/2018 - 17:00
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Functional Connectivity Within the Executive Control Network Mediates the Effects of Long-Term Tai Chi Exercise on Elders' Emotion Regulation.

Front Aging Neurosci. 2018;10:315

Authors: Liu Z, Wu Y, Li L, Guo X

Abstract
Previous research has identified the effects of tai chi exercise on elders' executive control or on their emotion regulation. However, few works have attempted to reveal the relationships between tai chi, executive control, and emotion regulation in the same study. The current resting-state study investigated whether the impact of tai chi on elders' emotion regulation was mediated by the resting-state functional connectivity within the executive control network. A total of 26 elders with long-term tai chi experience and 26 demographically matched healthy elders were recruited. After the resting-state scan, both groups were required to complete a series of questionnaires, including the Five Facets Mindfulness Questionnaire (FFMQ), and a sequential decision task, which offered an index of the subjects' emotion-regulation ability by calculating how their emotional response could be affected by the objective outcomes of their decisions. Compared to the control group, the tai chi group showed higher levels of non-judgment of inner experiences (a component of the FFMQ), stronger emotion-regulation ability, and a weaker resting-state functional connectivity between the dorsolateral prefrontal cortex (DLPFC) and the middle frontal gyrus (MFG). Moreover, the functional connectivity between the DLPFC and the MFG in the tai chi group fully mediated the impact of non-judgment of inner experience on their emotion-regulation ability. These findings highlighted that the modulation of non-judgment of inner experience on long-term tai chi practitioners' emotion regulation was achieved through decreased functional connectivity within the executive control network.

PMID: 30405392 [PubMed]

Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective.

Fri, 11/09/2018 - 17:00
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Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective.

Front Hum Neurosci. 2018;12:422

Authors: Wei J, Chen T, Li C, Liu G, Qiu J, Wei D

Abstract
Studies have demonstrated that there are widespread significant differences in spontaneous brain activity between eyes-open (EO) and eyes-closed (EC) resting states. However, it remains largely unclear whether spontaneous brain activity is effectively related to EO and EC resting states. The amplitude, local functional concordance, inter-hemisphere functional synchronization, and network centrality of spontaneous brain activity were measured by the fraction amplitude of low frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC), respectively. Using the public Eyes-open/Eyes-closed dataset, we employed the support vector machine (SVM) and bootstrap technique to establish linking models for the fALFF, ReHo, VMHC and DC dimensions. The classification accuracies of linking models are 0.72 (0.59, 0.82), 0.88 (0.79, 0.97), 0.82 (0.74, 0.91) and 0.70 (0.62, 0.79), respectively. Specifically, we observed that brain activity in the EO condition is significantly greater in attentional system areas, including the fusiform gyrus, occipital and parietal cortex, but significantly lower in sensorimotor system areas, including the precentral/postcentral gyrus, paracentral lobule (PCL) and temporal cortex compared to the EC condition from the four dimensions. The results consistently indicated that spontaneous brain activity is effectively related to EO and EC resting states, and the two resting states are of opposite brain activity in sensorimotor and occipital regions. It may provide new insight into the neural substrate of the resting state and help computational neuroscientists or neuropsychologists to choose an appropriate resting state condition to investigate various mental disorders from the resting state functional magnetic resonance imaging (fMRI) technique.

PMID: 30405376 [PubMed]

Default Mode Network Complexity and Cognitive Decline in Mild Alzheimer's Disease.

Fri, 11/09/2018 - 17:00
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Default Mode Network Complexity and Cognitive Decline in Mild Alzheimer's Disease.

Front Neurosci. 2018;12:770

Authors: Grieder M, Wang DJJ, Dierks T, Wahlund LO, Jann K

Abstract
The human resting-state is characterized by spatially coherent brain activity at a low temporal frequency. The default mode network (DMN), one of so-called resting-state networks, has been associated with cognitive processes that are directed toward the self, such as introspection and autobiographic memory. The DMN's integrity appears to be crucial for mental health. For example, patients with Alzheimer's disease or other psychiatric conditions show disruptions of functional connectivity within the brain regions of the DMN. However, in prodromal or early stages of Alzheimer's disease, physiological alterations are sometimes elusive, despite manifested cognitive impairment. While functional connectivity assesses the signal correlation between brain areas, multi-scale entropy (MSE) measures the complexity of the blood-oxygen level dependent signal within an area and thus might show local changes before connectivity is affected. Hence, we investigated alterations of functional connectivity and MSE within the DMN in fifteen mild Alzheimer's disease patients as compared to fourteen controls. Potential associations of MSE with functional connectivity and cognitive abilities [i.e., mini-mental state examination (MMSE)] were assessed. A moderate decrease of DMN functional connectivity between posterior cingulate cortex and right hippocampus in Alzheimer's disease was found, whereas no differences were evident for whole-network functional connectivity. In contrast, the Alzheimer's disease group yielded lower global DMN-MSE than the control group. The most pronounced regional effects were localized in left and right hippocampi, and this was true for most scales. Moreover, MSE significantly correlated with functional connectivity, and DMN-MSE correlated positively with the MMSE in Alzheimer's disease. Most interestingly, the right hippocampal MSE was positively associated with semantic memory performance. Thus, our results suggested that cognitive decline in Alzheimer's disease is reflected by decreased signal complexity in DMN nodes, which might further lead to disrupted DMN functional connectivity. Additionally, altered entropy in Alzheimer's disease found in the majority of the scales indicated a disturbance of both local information processing and information transfer between distal areas. Conclusively, a loss of nodal signal complexity potentially impairs synchronization across nodes and thus preempts functional connectivity changes. MSE presents a putative functional marker for cognitive decline that might be more sensitive than functional connectivity alone.

PMID: 30405347 [PubMed]

Connecting Openness and the Resting-State Brain Network: A Discover-Validate Approach.

Fri, 11/09/2018 - 17:00
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Connecting Openness and the Resting-State Brain Network: A Discover-Validate Approach.

Front Neurosci. 2018;12:762

Authors: Wang J, Hu Y, Li H, Ge L, Li J, Cheng L, Yang Z, Zuo X, Xu Y

Abstract
In personality neuroscience, the openness-brain association has been a topic of interest. Previous studies usually started from difference in openness trait and used it to infer brain functional activity characteristics, but no study has used a "brain-first" research strategy to explore that association based on more objective brain imaging data. In this study, we used a fully data-driven approach to discover and validate the association between openness and the resting-state brain network. We collected data of 120 subjects as a discovery sample and 56 subjects as a validation sample. The Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI) was used to measure the personality characteristics of all the subjects. Using an exploratory approach based on independent component analysis of resting-state functional magnetic resonance imaging (fMRI) data, we identified a parietal network that consisted of the precuneus and inferior parietal lobe. The inter-subject similarity of the parietal memory network exhibited significant associations with openness trait, and this association was validated using the 56-subject independent sample. This finding connects the openness trait to the characteristics of a neural network and helps to understand the underlying biology of the openness trait.

PMID: 30405342 [PubMed]

Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal.

Fri, 11/09/2018 - 17:00
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Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal.

Neuroimage. 2018 02 15;167:297-308

Authors: Yousefi B, Shin J, Schumacher EH, Keilholz SD

Abstract
Quasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain's intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated. QPP type could be predicted by an individual's global signal, with lower global signal corresponding to QPPs with strong anti-correlation. After regression of global signal, all QPPs showed strong anti-correlation between DMN and TPN. QPP occurrence and type was similar between a subgroup of individuals with extremely low motion and the rest of the sample, which shows that motion is not a major contributor to the QPPs. After regression of estimates of slow respiratory and cardiac induced signal fluctuations, more QPPs showed strong anti-correlation between DMN and TPN, an indication that while physiological noise influences the QPP type, it is not the primary source of the QPP itself. QPPs were more similar for the same subjects scanned on different days than for different subjects. These results provide the first assessment of the variability in individual QPPs and their relationship to physiological parameters.

PMID: 29175200 [PubMed - indexed for MEDLINE]

Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies-Quantifying noise removal and neural signal preservation.

Thu, 11/08/2018 - 16:00
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Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies-Quantifying noise removal and neural signal preservation.

Hum Brain Mapp. 2018 Nov 07;:

Authors: Bartoň M, Mareček R, Krajčovičová L, Slavíček T, Kašpárek T, Zemánková P, Říha P, Mikl M

Abstract
This study examines the impact of using different cerebrospinal fluid (CSF) and white matter (WM) nuisance signals for data-driven filtering of functional magnetic resonance imaging (fMRI) data as a cleanup method before analyzing intrinsic brain fluctuations. The routinely used temporal signal-to-noise ratio metric is inappropriate for assessing fMRI filtering suitability, as it evaluates only the reduction of data variability and does not assess the preservation of signals of interest. We defined a new metric that evaluates the preservation of selected neural signal correlates, and we compared its performance with a recently published signal-noise separation metric. These two methods provided converging evidence of the unfavorable impact of commonly used filtering approaches that exploit higher numbers of principal components from CSF and WM compartments (typically 5 + 5 for CSF and WM, respectively). When using only the principal components as nuisance signals, using a lower number of signals results in a better performance (i.e., 1 + 1 performed best). However, there was evidence that this routinely used approach consisting of 1 + 1 principal components may not be optimal for filtering resting-state (RS) fMRI data, especially when RETROICOR filtering is applied during the data preprocessing. The evaluation of task data indicated the appropriateness of 1 + 1 principal components, but when RETROICOR was applied, there was a change in the optimal filtering strategy. The suggested change for extracting WM (and also CSF in RETROICOR-corrected RS data) is using local signals instead of extracting signals from a large mask using principal component analysis.

PMID: 30403309 [PubMed - as supplied by publisher]

Two-Year Longitudinal Monitoring of Amnestic Mild Cognitive Impairment Patients with Prodromal Alzheimer's Disease Using Topographical Biomarkers Derived from Functional Magnetic Resonance Imaging and Electroencephalographic Activity.

Thu, 11/08/2018 - 16:00
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Two-Year Longitudinal Monitoring of Amnestic Mild Cognitive Impairment Patients with Prodromal Alzheimer's Disease Using Topographical Biomarkers Derived from Functional Magnetic Resonance Imaging and Electroencephalographic Activity.

J Alzheimers Dis. 2018 Oct 29;:

Authors: Jovicich J, Babiloni C, Ferrari C, Marizzoni M, Moretti DV, Del Percio C, Lizio R, Lopez S, Galluzzi S, Albani D, Cavaliere L, Minati L, Didic M, Fiedler U, Forloni G, Hensch T, Molinuevo JL, Bartrés Faz D, Nobili F, Orlandi D, Parnetti L, Farotti L, Costa C, Payoux P, Rossini PM, Marra C, Schönknecht P, Soricelli A, Noce G, Salvatore M, Tsolaki M, Visser PJ, Richardson JC, Wiltfang J, Bordet R, Blin O, Frisoniand GB

Abstract
Auditory "oddball" event-related potentials (aoERPs), resting state functional magnetic resonance imaging (rsfMRI) connectivity, and electroencephalographic (rsEEG) rhythms were tested as longitudinal functional biomarkers of prodromal Alzheimer's disease (AD). Data were collected at baseline and four follow-ups at 6, 12, 18, and 24 months in amnesic mild cognitive impairment (aMCI) patients classified in two groups: "positive" (i.e., "prodromal AD"; n = 81) or "negative" (n = 63) based on a diagnostic marker of AD derived from cerebrospinal samples (Aβ42/P-tau ratio). A linear mixed model design was used to test functional biomarkers for Group, Time, and Group×Time effects adjusted by nuisance covariates (only data until conversion to dementia was used). Functional biomarkers that showed significant Group effects ("positive" versus "negative", p <  0.05) regardless of Time were 1) reduced rsfMRI connectivity in both the default mode network (DMN) and the posterior cingulate cortex (PCC), both also giving significant Time effects (connectivity decay regardless of Group); 2) increased rsEEG source activity at delta (<4 Hz) and theta (4-8 Hz) rhythms and decreased source activity at low-frequency alpha (8-10.5 Hz) rhythms; and 3) reduced parietal and posterior cingulate source activities of aoERPs. Time×Group effects showed differential functional biomarker progression between groups: 1) increased rsfMRI connectivity in the left parietal cortex of the DMN nodes, consistent with compensatory effects and 2) increased limbic source activity at theta rhythms. These findings represent the first longitudinal characterization of functional biomarkers of prodromal AD relative to "negative" aMCI patients based on 5 serial recording sessions over 2 years.

PMID: 30400088 [PubMed - as supplied by publisher]

Identification of Subclinical Language Deficit using Machine Learning Classification based on Post-stroke Functional Connectivity derived from Low Frequency Oscillations.

Wed, 11/07/2018 - 14:40
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Identification of Subclinical Language Deficit using Machine Learning Classification based on Post-stroke Functional Connectivity derived from Low Frequency Oscillations.

Brain Connect. 2018 Nov 06;:

Authors: Mohanty R, Nair VA, Tellapragada N, Wiliams LM, Kang TJ, Prabhakaran V

Abstract
Post-stroke neuropsychological evaluation can take a long time to assess impairments in subjects without overt clinical deficits. We utilized functional connectivity (FC) from ten-minute non-invasive resting-state functional MRI (rs-fMRI) to identify stroke subjects at risk for subclinical language deficit (SLD) using a machine learning classifier. Discriminative ability of FC derived from slow-4 (0.027-0.073 Hz), slow-5 (0.01-0.027 Hz) and low frequency oscillations (LFO; 0.01-0.1 Hz) were compared. Sixty clinically non-aphasic right-handed subjects were categorized into three groups based on stroke status and normalized verbal fluency score (VFS): 20 ischemic stroke subjects at a higher risk of SLD (LD+; mean VFS=-1.77), 20 ischemic stroke subjects with lower risk of SLD (LD-; mean VFS=-0.05), 20 healthy controls (HC; mean VFS=0.29). T1-weighted and rs-fMRI scans were acquired within 30 days of stroke onset. Blood-oxygen-level-dependent signal was extracted from regions in the language network and FC based on Pearson's correlation was evaluated. Selected features were used by a multiclass support vector machine to classify test subject into one of the subgroups. Classifier performance was assessed using a nested leave-one-out cross-validation. FC derived from slow-4 (70%) band provided the best accuracy in comparison to LFO (65%) and slow-5 (50%) , reasonably higher than random chance (33.33%). Based on subgroup-specific accuracy, classification was best realized within the slow-4 band for LD+ (81.6%) and LD- (78.3%) and slow-4 and LFO bands for HC (80%), i.e., early stage stroke subjects showed a slow-4 FC dominance whereas HC also indicated the normalized involvement of FC in LFO. While frontal FC differentiated between stroke and healthy, occipital FC differentiated between the two stroke groups. We demonstrated that stroke subjects at risk for SLD can be differentiated from control subjects using rs-fMRI with a classifier with reasonable accuracy in an expedited manner, which otherwise could take longer to identify via neuropsychological assessments.

PMID: 30398379 [PubMed - as supplied by publisher]

Commute Time as a Method to Explore Brain Functional Connectomes.

Wed, 11/07/2018 - 14:40
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Commute Time as a Method to Explore Brain Functional Connectomes.

Brain Connect. 2018 Nov 06;:

Authors: Sato JR, Sato CM, Silva MC, Biazoli CE

Abstract
Graph theory has been extensively applied to investigate the brain complex networks in current neuroscience research. Many metrics derived from graph theory, such as local and global efficiencies, are based on the path length between nodes. These approaches are commonly used in the analyses of brain networks assessed by resting-state fMRI, though relying on the strong assumption that information flow throughout the network is restricted to the shortest paths. In this study, we propose the utilization of the commute time as a tool to investigate regional centrality on the functional Connectome. Our initial hypothesis was that an alternative approach that considers alternative routes (such as the commute time) could provide further information into the organization of functional networks. However, our empirical findings on the ADHD-200 database suggest that, at the group level, the commute time and shortest path are highly correlated. In contrast, at the subject level, we discovered that the commute time is much less susceptible to head motion artifacts when compared to metric based on shortest paths. Given the overall similarity between the measures, we argue that commute time might be advantageous particularly for connectomic studies in populations where motion artifacts are a major issue.

PMID: 30398376 [PubMed - as supplied by publisher]

Effective Connectivity Within the Default Mode Network In Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project.

Wed, 11/07/2018 - 14:40
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Effective Connectivity Within the Default Mode Network In Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project.

Brain Connect. 2018 Nov 06;:

Authors: Cook CJ, Hwang G, Mathis J, Nair VA, Conant L, Allen L, Almane DN, Birn R, DeYoe E, Felton E, Forseth C, Humphries C, Kraegel P, Nencka A, Nwoke O, Raghavan M, Rivera-Bonet C, Rozman M, Tellapragada N, Ustine C, Ward D, Struck A, Maganti R, Hermann B, Prabhakaran V, Binder J, Meyerand ME

Abstract
The Epilepsy Connectome Project examines the differences in connectomes between temporal lobe epilepsy (TLE) patients and healthy controls. Using this data, the effective connectivity of the default mode network in patients with left TLE compared to healthy controls was investigated using spectral dynamic causal modeling of resting state functional magnetic resonance imaging data. Group comparisons were made using two parametric empirical Bayes (PEB) models. The first level of each PEB model consisted of each participant's spectral dynamic causal modeling. Two different second level models were constructed: the first comparing effective connectivity of the groups directly and the second using the Rey Auditory Verbal Learning Test (RAVLT) delayed free recall index as a covariate at the second level in order to assess effective connectivity controlling for the poor memory performance of left TLE patients. After an automated search over the nested parameter space and thresholding parameters at 95% posterior probability, both models revealed numerous connections in the DMN which lead to inhibition of the left hippocampal formation. Left hippocampal formation inhibition may be an inherent result of the left temporal epileptogenic focus as memory differences were controlled for in one model and the same connections remained. An excitatory connection from the posterior cingulate cortex to the medial prefrontal cortex was found to be concomitant with left hippocampal formation inhibition in TLE patients when including RAVLT delayed free recall at the second level.

PMID: 30398367 [PubMed - as supplied by publisher]

Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data.

Wed, 11/07/2018 - 14:40
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Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data.

Sci Rep. 2018 Nov 05;8(1):16362

Authors: Goelman G, Dan R, Keadan T

Abstract
A multivariate measure of directed functional connectivity is used with resting-state fMRI data of 40 healthy subjects to identify directed pathways of signal progression in the human visual system. The method utilizes 4-nodes networks of mutual interacted BOLD signals to obtains their temporal hierarchy and functional connectivity. Patterns of signal progression were defined at frequency windows by appealing to a hierarchy based upon phase differences, and their significance was assessed by permutation testing. Assuming consistent phase relationship between neuronal and fMRI signals and unidirectional coupling, we were able to characterize directed pathways in the visual system. The ventral and dorsal systems were found to have different functional organizations. The dorsal system, particularly of the left hemisphere, had numerous feedforward pathways connecting the striate and extrastriate cortices with non-visual regions. The ventral system had fewer pathways primarily of two types: (1) feedback pathways initiated in the fusiform gyrus that were either confined to the striate and the extrastriate cortices or connected to the temporal cortex, (2) feedforward pathways initiated in V2, excluded the striate cortex, and connected to non-visual regions. The multivariate measure demonstrated higher specificity than bivariate (pairwise) measure. The analysis can be applied to other neuroimaging and electrophysiological data.

PMID: 30397245 [PubMed - in process]

Intrinsic insula network engagement underlying children's reading and arithmetic skills.

Wed, 11/07/2018 - 14:40
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Intrinsic insula network engagement underlying children's reading and arithmetic skills.

Neuroimage. 2018 02 15;167:162-177

Authors: Chang TT, Lee PH, Metcalfe AWS

Abstract
The neural substrates of children's reading and arithmetic skills have long been of great interest to cognitive neuroscientists. However, most previous studies have focused on the contrast between these skills as specific domains. Here, we investigate the potentially shared processes across these domains by focusing on how the neural circuits associated with cognitive control influence reading and arithmetic proficiency in 8-to-10-year-old children. Using a task-free resting state approach, we correlated the intrinsic functional connectivity of the right anterior insula (rAI) network with performance on assessments of Chinese character recognition, reading comprehension, subtraction, and multiplication performance. A common rAI network strengthened for reading and arithmetic skill, including the right middle temporal gyrus (MTG) and superior temporal gyrus (STG) in the lateral temporal cortex, as well as the inferior frontal gyrus (IFG). In addition, performance measures evidenced rAI network specializations. Single character recognition was uniquely associated with connectivity to the right superior parietal lobule (SPL). Reading comprehension only, rather than character recognition, was associated with connectivity to the right IFG, MTG and angular gyrus (AG). Furthermore, subtraction was associated with connectivity to premotor cortex whereas multiplication was associated with the supramarginal gyrus. Only reading comprehension and multiplication were associated with hyper connectivity within local rAI network. These results indicate that during a critical period for children's acquisition of reading and arithmetic, these skills are supported by both intra-network synchronization and inter-network connectivity of rAI circuits. Domain-general intrinsic insular connectivity at rest contained also, functional components that segregated into different sets of skill-related networks. The embedded components of cognitive control may be essential to understanding the interplay of multiple functional circuits necessary to more fully characterize cognitive skill acquisition.

PMID: 29162521 [PubMed - indexed for MEDLINE]

resting state fMRI; +21 new citations

Tue, 11/06/2018 - 13:07

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resting state fMRI

These pubmed results were generated on 2018/11/06

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Decreased subregional specificity of the putamen in Parkinson's Disease revealed by dynamic connectivity-derived parcellation.

Sat, 11/03/2018 - 16:20

Decreased subregional specificity of the putamen in Parkinson's Disease revealed by dynamic connectivity-derived parcellation.

Neuroimage Clin. 2018 Oct 23;20:1163-1175

Authors: Liu A, Lin SJ, Mi T, Chen X, Chan P, Wang ZJ, McKeown MJ

Abstract
Parkinson's Disease (PD) is associated with decreased ability to perform habitual tasks, relying instead on goal-directed behaviour subserved by different cortical/subcortical circuits, including parts of the putamen. We explored the functional subunits in the putamen in PD using novel dynamic connectivity features derived from resting state fMRI recorded from thirty PD subjects and twenty-eight age-matched healthy controls (HC). Dynamic functional segmentation of the putamina was obtained by determining the correlation between each voxel in each putamen along a moving window and applying a joint temporal clustering algorithm to establish cluster membership of each voxel at each window. Contiguous voxels that had consistent cluster membership across all windows were then considered to be part of a homogeneous functional subunit. As PD subjects robustly had two homogenous clusters in the putamina, we also segmented the putamina in HC into two dynamic clusters for a fair comparison. We then estimated the dynamic connectivity using sliding windowed correlation between the mean signal from the identified homogenous subunits and 56 other predefined cortical and subcortical ROIs. Specifically, the mean dynamic connectivity strength and connectivity deviation were then compared to evaluate subregional differences. HC subjects had significant differences in mean dynamic connectivity and connectivity deviation between the two putaminal subunits. The posterior subunit connected strongly to sensorimotor areas, the cerebellum, as well as the middle frontal gyrus. The anterior subunit had strong mean dynamic connectivity to the nucleus accumbens, hippocampus, amygdala, caudate and cingulate. In contrast, PD subjects had fewer differences in mean dynamic connectivity between subunits, indicating a degradation of subregional specificity. Overall UPDRS III and MoCA scores could be predicted using mean dynamic connectivity strength and connectivity deviation. Side of onset of the disease was also jointly related with functional connectivity features. Our results suggest a robust loss of specificity of mean dynamic connectivity and connectivity deviation in putaminal subunits in PD that is sensitive to disease severity. In addition, altered mean dynamic connectivity and connectivity deviation features in PD suggest that looking at connectivity dynamics offers an additional dimension for assessment of neurodegenerative disorders.

PMID: 30388599 [PubMed - as supplied by publisher]

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