New resting-state fMRI related studies at PubMed

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Altered Brain Functional Connectivity in Small-Cell Lung Cancer Patients after Chemotherapy Treatment: A Resting-State fMRI Study.

Sat, 08/12/2017 - 10:40
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Altered Brain Functional Connectivity in Small-Cell Lung Cancer Patients after Chemotherapy Treatment: A Resting-State fMRI Study.

Comput Math Methods Med. 2017;2017:1403940

Authors: Bromis K, Gkiatis K, Karanasiou I, Matsopoulos G, Karavasilis E, Papathanasiou M, Efstathopoulos E, Kelekis N, Kouloulias V

Abstract
Previous studies in small-cell lung cancer (SCLC) patients have mainly focused on exploring neurocognitive deficits associated with prophylactic cranial irradiation (PCI). Little is known about functional brain alterations that might occur due to chemotherapy treatment in this population before PCI is administered. For this reason, we used resting-state functional Magnetic Resonance Imaging (fMRI) to examine potential functional connectivity disruptions in brain networks, including the Default Mode Network (DMN), the Sensorimotor Network, and the Task-Positive Network (TPN). Nineteen SCLC patients after platinum-based chemotherapy treatment and thirteen controls were recruited in the current study. ROI-to-ROI and Seed-to-Voxel analyses were carried out and revealed functional connectivity deficits in patients within all the networks investigated demonstrating the possible negative effect of chemotherapy in cognitive functions in SCLC populations.

PMID: 28798808 [PubMed - in process]

Decomposing Multifractal Crossovers.

Sat, 08/12/2017 - 10:40
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Decomposing Multifractal Crossovers.

Front Physiol. 2017;8:533

Authors: Nagy Z, Mukli P, Herman P, Eke A

Abstract
Physiological processes-such as, the brain's resting-state electrical activity or hemodynamic fluctuations-exhibit scale-free temporal structuring. However, impacts common in biological systems such as, noise, multiple signal generators, or filtering by transport function, result in multimodal scaling that cannot be reliably assessed by standard analytical tools that assume unimodal scaling. Here, we present two methods to identify breakpoints or crossovers in multimodal multifractal scaling functions. These methods incorporate the robust iterative fitting approach of the focus-based multifractal formalism (FMF). The first approach (moment-wise scaling range adaptivity) allows for a breakpoint-based adaptive treatment that analyzes segregated scale-invariant ranges. The second method (scaling function decomposition method, SFD) is a crossover-based design aimed at decomposing signal constituents from multimodal scaling functions resulting from signal addition or co-sampling, such as, contamination by uncorrelated fractals. We demonstrated that these methods could handle multimodal, mono- or multifractal, and exact or empirical signals alike. Their precision was numerically characterized on ideal signals, and a robust performance was demonstrated on exemplary empirical signals capturing resting-state brain dynamics by near infrared spectroscopy (NIRS), electroencephalography (EEG), and blood oxygen level-dependent functional magnetic resonance imaging (fMRI-BOLD). The NIRS and fMRI-BOLD low-frequency fluctuations were dominated by a multifractal component over an underlying biologically relevant random noise, thus forming a bimodal signal. The crossover between the EEG signal components was found at the boundary between the δ and θ bands, suggesting an independent generator for the multifractal δ rhythm. The robust implementation of the SFD method should be regarded as essential in the seamless processing of large volumes of bimodal fMRI-BOLD imaging data for the topology of multifractal metrics free of the masking effect of the underlying random noise.

PMID: 28798694 [PubMed]

Dual Temporal and Spatial Sparse Representation for Inferring Group-wise Brain Networks from Resting-state fMRI Dataset.

Fri, 08/11/2017 - 15:40

Dual Temporal and Spatial Sparse Representation for Inferring Group-wise Brain Networks from Resting-state fMRI Dataset.

IEEE Trans Biomed Eng. 2017 Aug 09;:

Authors: Gong J, Liu X, Liu T, Zhou J, Sun G, Tian J

Abstract
Recently, sparse representation has been successfully used to identify brain networks from task-based fMRI dataset. However, when using the strategy to analyze resting-state fMRI dataset, it is still a challenge to automatically infer the group-wise brain networks under consideration of group commonalities and subject-specific characteristics. In the paper, a novel method based on dual temporal and spatial sparse representation (DTSSR) is proposed to meet this challenge. Firstly, the brain functional networks with subject-specific characteristics are obtained via sparse representation with online dictionary learning for the fMRI time series (temporal domain) of each subject. Next, based on the current brain science knowledge, a simple mathematical model is proposed to describe the complex nonlinear dynamic coupling mechanism of the brain networks, with which the group-wise intrinsic connectivity networks (ICNs) can be inferred by sparse representation for these brain functional networks (spatial domain) of all subjects. Experiments on Leiden_2180 dataset show that most group-wise ICNs obtained by the proposed DTSSR are interpretable by current brain science knowledge and are consistent with previous literature reports. The robustness of DTSSR and the reproducibility of the results are demonstrated by experiments on three different datasets (Leiden_2180, Leiden_2200 and our own dataset). Results of the present work shed new light on exploring the coupling mechanism of BFNs from perspective of information science.

PMID: 28796604 [PubMed - as supplied by publisher]

Connectivity of precuneus to the default mode and dorsal attention networks: A possible invariant marker of long-term tinnitus.

Fri, 08/11/2017 - 15:40
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Connectivity of precuneus to the default mode and dorsal attention networks: A possible invariant marker of long-term tinnitus.

Neuroimage Clin. 2017;16:196-204

Authors: Schmidt SA, Carpenter-Thompson J, Husain FT

Abstract
Resting state functional connectivity studies of tinnitus have provided inconsistent evidence concerning its neural bases. This may be due to differences in the methodology used, but it is also likely related to the heterogeneity of the tinnitus population. In this study, our goal was to identify resting state functional connectivity alterations that consistently appear across tinnitus subgroups. We examined two sources of variability in the subgroups: tinnitus severity and the length of time a person has had chronic tinnitus (referred to as tinnitus duration). Data for the current large-scale analysis of variance originated partly from our earlier investigations (Schmidt et al., 2013; Carpenter-Thompson et al., 2015) and partly from previously unpublished studies. Decreased correlations between seed regions in the default mode network and the precuneus were consistent across individuals with long-term tinnitus (who have had tinnitus for greater than one year), with more bothersome tinnitus demonstrating stronger decreases. In the dorsal attention network, patients with moderately severe tinnitus showed increased correlations between seeds in the network and the precuneus, with this effect also present in only some patients with mild tinnitus. The same effects were not seen in patients with mild tinnitus and tinnitus duration between 6 and 12 months. Our results are promising initial steps towards identifying invariant neural correlates of tinnitus and indexing differences between subgroups.

PMID: 28794980 [PubMed - in process]

Resting-state functional connectivity changes due to acute and short-term valproic acid administration in the baboon model of GGE.

Fri, 08/11/2017 - 15:40
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Resting-state functional connectivity changes due to acute and short-term valproic acid administration in the baboon model of GGE.

Neuroimage Clin. 2017;16:132-141

Authors: Salinas FS, Szabó CÁ

Abstract
Resting-state functional connectivity (FC) is altered in baboons with genetic generalized epilepsy (GGE) compared to healthy controls (CTL). We compared FC changes between GGE and CTL groups after intravenous injection of valproic acid (VPA) and following one-week of orally administered VPA. Seven epileptic (2 females) and six CTL (3 females) baboons underwent resting-state fMRI (rs-fMRI) at 1) baseline, 2) after intravenous acute VPA administration (20 mg/kg), and 3) following seven-day oral, subacute VPA therapy (20-80 mg/kg/day). FC was evaluated using a data-driven approach, while regressing out the group-wise effects of age, gender and VPA levels. Sixteen networks were identified by independent component analysis (ICA). Each network mask was thresholded (z > 4.00; p < 0.001), and used to compare group-wise FC differences between baseline, intravenous and oral VPA treatment states between GGE and CTL groups. At baseline, FC was increased in most cortical networks of the GGE group but decreased in the thalamic network. After intravenous acute VPA, FC increased in the basal ganglia network and decreased in the parietal network of epileptic baboons to presumed nodes associated with the epileptic network. After oral VPA therapy, FC was decreased in GGE baboons only the orbitofrontal networks connections to the primary somatosensory cortices, reflecting a reversal from baseline comparisons. VPA therapy affects FC in the baboon model of GGE after a single intravenous dose-possibly by facilitating subcortical modulation of the epileptic network and suppressing seizure generation-and after short-term oral VPA treatment, reversing the abnormal baseline increases in FC in the orbitofrontal network. While there is a need to correlate these FC changes with simultaneous EEG recording and seizure outcomes, this study demonstrates the feasibility of evaluating rs-fMRI effects of antiepileptic medications even after short-term exposure.

PMID: 28794974 [PubMed - in process]

Fractional amplitude of low-frequency fluctuations (fALFF) in post-stroke depression.

Fri, 08/11/2017 - 15:40
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Fractional amplitude of low-frequency fluctuations (fALFF) in post-stroke depression.

Neuroimage Clin. 2017;16:116-124

Authors: Egorova N, Veldsman M, Cumming T, Brodtmann A

Abstract
Depression is a common outcome following stroke, associated with reduced quality of life and poorer recovery. Despite attempts to associate depression symptoms with specific lesion sites, the neural basis of post-stroke depression remains poorly understood. Resting state fMRI has provided new insights into the neural underpinnings of post-stroke depression, but has been limited to connectivity analyses exploring interregional correlations in the time-course of activity. Other aspects of resting state BOLD signal remain unexamined. Measuring the amplitude of low frequency fluctuations allows the detection of spontaneous neural activity across the whole brain. It provides complementary information about frequency-specific local neural activity. We calculated the fractional amplitude of low frequency fluctuations (fALFF) in a group of 64 participants scanned 3 months post-stroke. Twenty showed depression symptoms when assessed with the Patient Health Questionnaire (PHQ-9). We performed analyses in both the typical 0.01-0.08 Hz range, as well as separately in the slow-5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz) ranges. We found significantly higher fALFF in the depressed compared to non-depressed participants in the left dorsolateral prefrontal cortex (DLPFC) and the right precentral gyrus, and a significant association between higher depression scores and higher fALFF in the left insula. The group differences were detected in the slow-5 fluctuations, while the association with depression severity was observed in the slow-4 range. We conclude that post-stroke depression can be characterised by aberrant spontaneous local neural activity, which in small samples could be a more sensitive measure than lesion volume and location.

PMID: 28794972 [PubMed - in process]

Decreased Functional Connectivity of Insular Cortex in Drug Naïve First Episode Schizophrenia: In Relation to Symptom Severity.

Fri, 08/11/2017 - 15:40
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Decreased Functional Connectivity of Insular Cortex in Drug Naïve First Episode Schizophrenia: In Relation to Symptom Severity.

PLoS One. 2017;12(1):e0167242

Authors: Pang L, Kennedy D, Wei Q, Lv L, Gao J, Li H, Quan M, Li X, Yang Y, Fan X, Song X

Abstract
BACKGROUND: This study was to examine the insular cortical functional connectivity in drug naïve patients with first episode schizophrenia and to explore the relationship between the connectivity and the severity of clinical symptoms.
METHODS: Thirty-seven drug naïve patients with schizophrenia and 25 healthy controls were enrolled in this study. A seed-based approach was used to analyze the resting-state functional imaging data. Insular cortical connectivity maps were bilaterally extracted for group comparison and validated by voxel-based morphometry (VBM) analysis. Clinical symptoms were measured using the Positive and Negative Syndrome Scale (PANSS).
RESULTS: There were significant reductions in the right insular cortical connectivity with the Heschl's gyrus, anterior cingulate cortex (ACC), and caudate (p's<0.001) in the patient group compared with the healthy control (HC) group. Reduced right insular cortical connectivity with the Heschl's gyrus was further confirmed in the VBM analysis (FDR corrected p<0.05). Within the patient group, there was a significant positive relationship between the right insula-Heschl's connectivity and PANSS general psychopathology scores (r = 0.384, p = 0.019).
CONCLUSION: Reduced insula-Heschl's functional connectivity is present in drug naïve patients with first episode schizophrenia, which might be related to the manifestation of clinical symptoms.

PMID: 28107346 [PubMed - indexed for MEDLINE]

Frequency-specific alteration of functional connectivity density in antipsychotic-naive adolescents with early-onset schizophrenia.

Thu, 08/10/2017 - 14:20
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Frequency-specific alteration of functional connectivity density in antipsychotic-naive adolescents with early-onset schizophrenia.

J Psychiatr Res. 2017 Jul 19;95:68-75

Authors: Wang X, Zhang Y, Long Z, Zheng J, Zhang Y, Han S, Wang Y, Duan X, Yang M, Zhao J, Chen H

Abstract
Early-onset schizophrenia (EOS) is a severe mental illness associated with dysconnectivity that widespread in the brain. However, the functional dysconnectivity in EOS are still mixed. Recently, studies have identified that functional connectivity (FC) arises from a band-limited slow rhythmic mechanism and suggested that the dysconnectivity at specific frequency bands may provide more robust biomarkers for schizophrenia. The frequency-specific changes of FC pattern in EOS remain unclear. To address this issue, resting-state functional magnetic resonance imaging data scans from 39 EOS patients (drug-naive) and 31 healthy controls (HCs) were used to assess the FC density (FCD) across slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz). Results revealed that a remarkable difference between the FCD of the two bands existed mainly in the default mode network (DMN) and subcortical areas. Compared with the HCs, EOS patients showed significantly altered FCD involved in audiovisual information processing, sensorimotor system, and social cognition. Importantly, a significant frequency-by-group interaction was observed in the left precuneus with significantly lower FCD in the slow-4 frequency band, but no significant effect in the slow-5 frequency band. In addition, decreased FC was found between the precuneus and other DMN regions in the slow-4 band. Furthermore, the change in FCD in precuneus was inversely proportional to the clinical symptom in slow-4 band, indicating the key role of precuneus in schizophrenia progress. Our findings demonstrated that the dysconnectivity pattern in EOS could be frequency-dependent.

PMID: 28793242 [PubMed - as supplied by publisher]

Altered Functional Connectivity of the Default Mode Network in Low-Empathy Subjects.

Thu, 08/10/2017 - 14:20
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Altered Functional Connectivity of the Default Mode Network in Low-Empathy Subjects.

Yonsei Med J. 2017 Sep;58(5):1061-1065

Authors: Kim SJ, Kim SE, Kim HE, Han K, Jeong B, Kim JJ, Namkoong K, Kim JW

Abstract
Empathy is the ability to identify with or make a vicariously experience of another person's feelings or thoughts based on memory and/or self-referential mental simulation. The default mode network in particular is related to self-referential empathy. In order to elucidate the possible neural mechanisms underlying empathy, we investigated the functional connectivity of the default mode network in subjects from a general population. Resting state functional magnetic resonance imaging data were acquired from 19 low-empathy subjects and 18 medium-empathy subjects. An independent component analysis was used to identify the default mode network, and differences in functional connectivity strength were compared between the two groups. The low-empathy group showed lower functional connectivity of the medial prefrontal cortex and anterior cingulate cortex (Brodmann areas 9 and 32) within the default mode network, compared to the medium-empathy group. The results of the present study suggest that empathy is related to functional connectivity of the medial prefrontal cortex/anterior cingulate cortex within the default mode network. Functional decreases in connectivity among low-empathy subjects may reflect an impairment of self-referential mental simulation.

PMID: 28792155 [PubMed - in process]

Spontaneous alterations of regional brain activity in patients with adult generalized anxiety disorder.

Thu, 08/10/2017 - 14:20
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Spontaneous alterations of regional brain activity in patients with adult generalized anxiety disorder.

Neuropsychiatr Dis Treat. 2017;13:1957-1965

Authors: Xia L, Li S, Wang T, Guo Y, Meng L, Feng Y, Cui Y, Wang F, Ma J, Jiang G

Abstract
OBJECTIVE: We aimed to examine how spontaneous brain activity might be related to the pathophysiology of generalized anxiety disorder (GAD).
PATIENTS AND METHODS: Using resting-state functional MRI, we examined spontaneous regional brain activity in 31 GAD patients (mean age, 36.87±9.16 years) and 36 healthy control participants (mean age, 39.53±8.83 years) matched for age, education, and sex from December 2014 to October 2015. We performed a two-sample t-test on the voxel-based analysis of the regional homogeneity (ReHo) maps. We used Pearson correlation analysis to compare scores from the Hamilton Anxiety Rating Scale, Hamilton Depression Rating Scale, State-Trait Anxiety Scale-Trait Scale, and mean ReHo values.
RESULTS: We found abnormal spontaneous activity in multiple regions of brain in GAD patients, especially in the sensorimotor cortex and emotional regions. GAD patients showed decreased ReHo values in the right orbital middle frontal gyrus, left anterior cingulate cortex, right middle frontal gyrus, and bilateral supplementary motor areas, with increased ReHo values in the left middle temporal gyrus, left superior temporal gyrus, and right superior occipital gyrus. The ReHo value of the left middle temporal gyrus correlated positively with the Hamilton Anxiety Rating Scale scores.
CONCLUSION: These results suggest that altered local synchronization of spontaneous brain activity may be related to the pathophysiology of GAD.

PMID: 28790831 [PubMed]

The regional neuronal activity in left posterior middle temporal gyrus is correlated with the severity of chronic aphasia.

Thu, 08/10/2017 - 14:20
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The regional neuronal activity in left posterior middle temporal gyrus is correlated with the severity of chronic aphasia.

Neuropsychiatr Dis Treat. 2017;13:1937-1945

Authors: Li J, Du D, Gao W, Sun X, Xie H, Zhang G, Li J, Li H, Li K

Abstract
BACKGROUND: Aphasia is one of the most disabling cognitive deficits affecting >2 million people in the USA. The neuroimaging characteristics of chronic aphasic patients (>6 months post onset) remain largely unknown.
OBJECTIVE: The objective of this study was to investigate the regional signal changes of spontaneous neuronal activity of brain and the inter-regional connectivity in chronic aphasia.
MATERIALS AND METHODS: Resting-state blood oxygenation level-dependent functional magnetic resonance imaging (fMRI) was used to obtain fMRI data from 17 chronic aphasic patients and 20 healthy control subjects in a Siemens Verio 3.0T MR Scanner. The amplitude of low-frequency fluctuation (ALFF) was determined, which directly reflects the regional neuronal activity. The functional connectivity (FC) of fMRI was assessed using a seed voxel linear correlation approach. The severity of aphasia was evaluated by aphasia quotient (AQ) scores obtained from Western Aphasia Battery test.
RESULTS: Compared with normal subjects, aphasic patients showed decreased ALFF values in the regions of left posterior middle temporal gyrus (PMTG), left medial prefrontal gyrus, and right cerebellum. The ALFF values in left PMTG showed strong positive correlation with the AQ score (coefficient r=0.79, P<0.05). There was a positive FC in chronic aphasia between left PMTG and left inferior temporal gyrus (BA20), fusiform gyrus (BA37), and inferior frontal gyrus (BA47\45\44).
CONCLUSION: Left PMTG might play an important role in language dysfunction of chronic aphasia, and ALFF value might be a promising indicator to evaluate the severity of aphasia.

PMID: 28790829 [PubMed]

A Neural Marker of Obsessive-Compulsive Disorder from Whole-Brain Functional Connectivity.

Thu, 08/10/2017 - 14:20
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A Neural Marker of Obsessive-Compulsive Disorder from Whole-Brain Functional Connectivity.

Sci Rep. 2017 Aug 08;7(1):7538

Authors: Takagi Y, Sakai Y, Lisi G, Yahata N, Abe Y, Nishida S, Nakamae T, Morimoto J, Kawato M, Narumoto J, Tanaka SC

Abstract
Obsessive-compulsive disorder (OCD) is a common psychiatric disorder with a lifetime prevalence of 2-3%. Recently, brain activity in the resting state is gathering attention for exploring altered functional connectivity in psychiatric disorders. Although previous resting-state functional magnetic resonance imaging studies investigated the neurobiological abnormalities of patients with OCD, there are concerns that should be addressed. One concern is the validity of the hypothesis employed. Most studies used seed-based analysis of the fronto-striatal circuit, despite the potential for abnormalities in other regions. A hypothesis-free study is a promising approach in such a case, while it requires researchers to handle a dataset with large dimensions. Another concern is the reliability of biomarkers derived from a single dataset, which may be influenced by cohort-specific features. Here, our machine learning algorithm identified an OCD biomarker that achieves high accuracy for an internal dataset (AUC = 0.81; N = 108) and demonstrates generalizability to an external dataset (AUC = 0.70; N = 28). Our biomarker was unaffected by medication status, and the functional networks contributing to the biomarker were distributed widely, including the frontoparietal and default mode networks. Our biomarker has the potential to deepen our understanding of OCD and to be applied clinically.

PMID: 28790433 [PubMed - in process]

Impaired Brain Network Architecture in Newly Diagnosed Parkinson's Disease Based on Graph Theoretical Analysis.

Thu, 08/10/2017 - 14:20
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Impaired Brain Network Architecture in Newly Diagnosed Parkinson's Disease Based on Graph Theoretical Analysis.

Neurosci Lett. 2017 Aug 05;:

Authors: Fang J, Chen H, Cao Z, Jiang Y, Ma L, Ma H, Feng T

Abstract
BACKGROUND: Resting state functional magnetic resonance imaging (rs-fMRI) has been applied to investigate topographic structure in Parkinson's disease (PD). Alteration of topographic architecture has been inconsistent in PD AIM: To investigate the network profile of PD using graph theoretical analysis.
METHOD: Twenty six newly diagnosed PD and 19 age- and gender- matched healthy controls (HC) were included in our analysis. Small-world profile and topographic profiles (nodal degree, global efficiency, local efficiency, cluster coefficient, shortest path length, betweenness centrality) were measured and compared between groups, with age and gender as covariates. We also performed correlation analysis between topographic features with motor severity measured by UPDRS III.
RESULTS: Small-world property was present in PD. Nodal degree, global efficiency, local efficiency and characteristic path length consistently revealed disruptive sensorimotor network, and visual network to a less degree in PD. By contrast, default mode network (DMN) and cerebellum in PD showed higher nodal degree, global efficiency and local efficiency, and lower characteristic path length. Global and local efficiency in the midbrain was higher in PD excluding substantia nigra. PD group also exhibited lower cluster coefficient in the subcortical motor network (thalamus and caudate nucleus). No significant correlation was found between topographic properties and motor severity.
CONCLUSION: PD exhibited disruptive sensorimotor and visual networks in early disease stage. DMN, a certain areas in the cerebellum and midbrain may compensate for disruptive sensorimotor and visual network in PD. Disruptive network architecture may be an early alteration of PD pathophysiology but may not serve as a valid biomarker yet.

PMID: 28789983 [PubMed - as supplied by publisher]

Ultra-fast magnetic resonance encephalography of physiological brain activity - Glymphatic pulsation mechanisms?

Thu, 08/10/2017 - 14:20
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Ultra-fast magnetic resonance encephalography of physiological brain activity - Glymphatic pulsation mechanisms?

J Cereb Blood Flow Metab. 2016 Jun;36(6):1033-45

Authors: Kiviniemi V, Wang X, Korhonen V, Keinänen T, Tuovinen T, Autio J, LeVan P, Keilholz S, Zang YF, Hennig J, Nedergaard M

Abstract
The theory on the glymphatic convection mechanism of cerebrospinal fluid holds that cardiac pulsations in part pump cerebrospinal fluid from the peri-arterial spaces through the extracellular tissue into the peri-venous spaces facilitated by aquaporin water channels. Since cardiac pulses cannot be the sole mechanism of glymphatic propulsion, we searched for additional cerebrospinal fluid pulsations in the human brain with ultra-fast magnetic resonance encephalography. We detected three types of physiological mechanisms affecting cerebral cerebrospinal fluid pulsations: cardiac, respiratory, and very low frequency pulsations. The cardiac pulsations induce a negative magnetic resonance encephalography signal change in peri-arterial regions that extends centrifugally and covers the brain in ≈1 Hz cycles. The respiratory ≈0.3 Hz pulsations are centripetal periodical pulses that occur dominantly in peri-venous areas. The third type of pulsation was very low frequency (VLF 0.001-0.023 Hz) and low frequency (LF 0.023-0.73 Hz) waves that both propagate with unique spatiotemporal patterns. Our findings using critically sampled magnetic resonance encephalography open a new view into cerebral fluid dynamics. Since glymphatic system failure may precede protein accumulations in diseases such as Alzheimer's dementia, this methodological advance offers a novel approach to image brain fluid dynamics that potentially can enable early detection and intervention in neurodegenerative diseases.

PMID: 26690495 [PubMed - indexed for MEDLINE]

Shannon entropy of brain functional complex networks under the influence of the psychedelic Ayahuasca.

Wed, 08/09/2017 - 13:20
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Shannon entropy of brain functional complex networks under the influence of the psychedelic Ayahuasca.

Sci Rep. 2017 Aug 07;7(1):7388

Authors: Viol A, Palhano-Fontes F, Onias H, de Araujo DB, Viswanathan GM

Abstract
The entropic brain hypothesis holds that the key facts concerning psychedelics are partially explained in terms of increased entropy of the brain's functional connectivity. Ayahuasca is a psychedelic beverage of Amazonian indigenous origin with legal status in Brazil in religious and scientific settings. In this context, we use tools and concepts from the theory of complex networks to analyze resting state fMRI data of the brains of human subjects under two distinct conditions: (i) under ordinary waking state and (ii) in an altered state of consciousness induced by ingestion of Ayahuasca. We report an increase in the Shannon entropy of the degree distribution of the networks subsequent to Ayahuasca ingestion. We also find increased local and decreased global network integration. Our results are broadly consistent with the entropic brain hypothesis. Finally, we discuss our findings in the context of descriptions of "mind-expansion" frequently seen in self-reports of users of psychedelic drugs.

PMID: 28785066 [PubMed - in process]

Fractal Analysis of Brain Blood Oxygenation Level Dependent (BOLD) Signals from Children with Mild Traumatic Brain Injury (mTBI).

Wed, 08/09/2017 - 13:20
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Fractal Analysis of Brain Blood Oxygenation Level Dependent (BOLD) Signals from Children with Mild Traumatic Brain Injury (mTBI).

PLoS One. 2017;12(1):e0169647

Authors: Dona O, Noseworthy MD, DeMatteo C, Connolly JF

Abstract
BACKGROUND: Conventional imaging techniques are unable to detect abnormalities in the brain following mild traumatic brain injury (mTBI). Yet patients with mTBI typically show delayed response on neuropsychological evaluation. Because fractal geometry represents complexity, we explored its utility in measuring temporal fluctuations of brain resting state blood oxygen level dependent (rs-BOLD) signal. We hypothesized that there could be a detectable difference in rs-BOLD signal complexity between healthy subjects and mTBI patients based on previous studies that associated reduction in signal complexity with disease.
METHODS: Fifteen subjects (13.4 ± 2.3 y/o) and 56 age-matched (13.5 ± 2.34 y/o) healthy controls were scanned using a GE Discovery MR750 3T MRI and 32-channel RF-coil. Axial FSPGR-3D images were used to prescribe rs-BOLD (TE/TR = 35/2000ms), acquired over 6 minutes. Motion correction was performed and anatomical and functional images were aligned and spatially warped to the N27 standard atlas. Fractal analysis, performed on grey matter, was done by estimating the Hurst exponent using de-trended fluctuation analysis and signal summation conversion methods.
RESULTS AND CONCLUSIONS: Voxel-wise fractal dimension (FD) was calculated for every subject in the control group to generate mean and standard deviation maps for regional Z-score analysis. Voxel-wise validation of FD normality across controls was confirmed, and non-Gaussian voxels (3.05% over the brain) were eliminated from subsequent analysis. For each mTBI patient, regions where Z-score values were at least 2 standard deviations away from the mean (i.e. where |Z| > 2.0) were identified. In individual patients the frequently affected regions were amygdala (p = 0.02), vermis(p = 0.03), caudate head (p = 0.04), hippocampus(p = 0.03), and hypothalamus(p = 0.04), all previously reported as dysfunctional after mTBI, but based on group analysis. It is well known that the brain is best modeled as a complex system. Therefore a measure of complexity using rs-BOLD signal FD could provide an additional method to grade and monitor mTBI. Furthermore, this approach can be personalized thus providing unique patient specific assessment.

PMID: 28072842 [PubMed - indexed for MEDLINE]

Influence of Vascular Variant of the Posterior Cerebral Artery (PCA) on Cerebral Blood Flow, Vascular Response to CO2 and Static Functional Connectivity.

Wed, 08/09/2017 - 13:20
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Influence of Vascular Variant of the Posterior Cerebral Artery (PCA) on Cerebral Blood Flow, Vascular Response to CO2 and Static Functional Connectivity.

PLoS One. 2016;11(8):e0161121

Authors: Emmert K, Zöller D, Preti MG, Van De Ville D, Giannakopoulos P, Haller S

Abstract
INTRODUCTION: The fetal origin of the posterior cerebral artery (fPCA) is a frequent vascular variant in 11-29% of the population. For the fPCA, blood flow in the PCA originates from the anterior instead of the posterior circulation. We tested whether this blood supply variant impacts the cerebral blood flow assessed by arterial spin labeling (ASL), cerebrovascular reserve as well as resting-state static functional connectivity (sFC) in the sense of a systematic confound.
METHODS: The study included 385 healthy, elderly subjects (mean age: 74.18 years [range: 68.9-90.4]; 243 female). Participants were classified into normal vascular supply (n = 296, 76.88%), right fetal origin (n = 23, 5.97%), left fetal origin (n = 16, 4.16%), bilateral fetal origin (n = 4, 1.04%), and intermediate (n = 46, 11.95%, excluded from further analysis) groups. ASL-derived relative cerebral blood flow (relCBF) maps and cerebrovascular reserve (CVR) maps derived from a CO2 challenge with blocks of 7% CO2 were compared. Additionally, sFC between 90 regions of interest (ROIs) was compared between the groups.
RESULTS: CVR was significantly reduced in subjects with ipsilateral fPCA, most prominently in the temporal lobe. ASL yielded a non-significant trend towards reduced relCBF in bilateral posterior watershed areas. In contrast, conventional atlas-based sFC did not differ between groups.
CONCLUSIONS: In conclusion, fPCA presence may bias the assessment of cerebrovascular reserve by reducing the response to CO2. In contrast, its effect on ASL-assessed baseline perfusion was marginal. Moreover, fPCA presence did not systematically impact resting-state sFC. Taken together, this data implies that perfusion variables should take into account the vascularization patterns.

PMID: 27532633 [PubMed - indexed for MEDLINE]

Mapping altered brain connectivity and its clinical associations in adult moyamoya disease: A resting-state functional MRI study.

Tue, 08/08/2017 - 12:00

Mapping altered brain connectivity and its clinical associations in adult moyamoya disease: A resting-state functional MRI study.

PLoS One. 2017;12(8):e0182759

Authors: Kazumata K, Tha KK, Uchino H, Ito M, Nakayama N, Abumiya T

Abstract
Detection of subtle ischemic injuries in moyamoya disease may enable optimization of timing of revascularization surgery, and could potentially improve functional outcomes. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study functional organization of the brain, but it remains unclear whether rs-fMRI could elucidate distinct characteristics in moyamoya disease. Here, we aimed to determine changes in a conventional rs-fMRI measure and analyze any associations with clinical symptoms and cerebral hemodynamics. Thirty-one adults with moyamoya disease and 25 adult controls underwent rs-fMRI, in which we measured brain connectivity via temporal correlations of low-frequency BOLD signals. We identified the extent of between-group differences with multivoxel pattern analysis. Seed-based analysis was performed to determine associations with vascular lesions, symptoms, and regional cerebral blood flow (rCBF). There was significantly altered connectivity in the precentral gyrus, operculo-insular region, precuneus, cingulate cortex, and middle frontal gyrus in moyamoya disease. There was reduced connectivity in the left insula, left precuneus, right precentral, and right middle frontal regions, which form part of the salience, default mode, motor, and central executive networks, respectively. Patients with ischemic motor-related symptoms showed significantly decreased connectivity in precentral homotopic regions compared with those without, while there were no differences in vascular lesions or rCBF. Connectivity between the right occipital and left hippocampus was significantly associated with cognitive performance and posterior cerebral artery involvement. Our results demonstrate distinct alterations in the temporal correlations of low-frequency BOLD signals, predominantly in resting-state networks in moyamoya disease. Additionally, rs-fMRI measures were associated with ischemic motor-related symptoms and cognitive performance in the patients. Thus, rs-fMRI may offer a useful non-invasive method of acquiring additional information beyond cerebral perfusion as part of clinical investigations in patients with moyamoya disease.

PMID: 28783763 [PubMed - in process]

Combining task-related activation and connectivity analysis of fMRI data reveals complex modulation of brain networks.

Tue, 08/08/2017 - 12:00

Combining task-related activation and connectivity analysis of fMRI data reveals complex modulation of brain networks.

Hum Brain Mapp. 2017 Aug 07;:

Authors: Gerchen MF, Kirsch P

Abstract
Task-related effects in functional magnetic resonance imaging (fMRI) data are usually analyzed with local activation approaches or integrative connectivity approaches, for example, by psychophysiological interaction (PPI) analysis. While both approaches are often applied to the same data set, a systematic combination of the results with a whole-brain (WB) perspective is rarely conducted and the relationship between task-dependent activation and connectivity effects is relatively unexplored. Here, we combined brain activation and graph theoretical analysis of WB-PPI results in an exemplary episodic memory data set of N = 136 healthy human participants and found regions with congruent as well as incongruent activation and connectivity changes between task and control conditions. A comparison with large-scale resting state networks showed that in congruent as well as incongruent regions task-positively modulated connections were mainly between-network connections, especially with the default mode network, while task-negatively modulated connections were mainly found within resting state networks. Over all regions, the strength of absolute activation effects was associated with the tendency to exhibit task-positive connectivity changes, mainly driven by a strong relationship in negatively activated regions. These results demonstrate that task demands lead to a complex modulation of brain networks and provide evidence that task-evoked activation and connectivity effects reflect separable and complementary information on the macroscale brain level assessed by fMRI. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

PMID: 28782871 [PubMed - as supplied by publisher]

A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.

Tue, 08/08/2017 - 12:00

A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.

Neuroimage. 2017 Aug 04;:

Authors: Huertas I, Oldehinkel M, van Oort ESB, Garcia-Solis D, Mir P, Beckmann CF, Marquand AF

Abstract
The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data is characterized as a linearly weighted combination of multiscale basis functions which map onto underlying brain nuclei or networks or nuclei. In this model, the elementary building blocks are derived to reflect the functional anatomy of the brain during the resting state. This model is estimated using a Bayesian framework which accurately quantifies uncertainty and automatically finds the most accurate and parsimonious combination of basis functions describing the data. We demonstrate the utility of this framework by predicting quantitative SPECT images of striatal dopamine function and we compare a variety of basis sets including generic isotropic functions, anatomical representations of the striatum derived from structural MRI, and two different soft functional parcellations of the striatum derived from resting-state fMRI (rfMRI). We found that a combination of ∼50 multiscale functional basis functions accurately represented the striatal dopamine activity, and that functional basis functions derived from an advanced parcellation technique known as Instantaneous Connectivity Parcellation (ICP) provided the most parsimonious models of dopamine function. Importantly, functional basis functions derived from resting fMRI were more accurate than both structural and generic basis sets in representing dopamine function in the striatum for a fixed model order. We demonstrate the translational validity of our framework by constructing classification models for discriminating parkinsonian disorders and their subtypes. Here, we show that ICP approach is the only basis set that performs well across all comparisons and performs better overall than the classical voxel-based approach. This spatial model constitutes an elegant alternative to voxel-based approaches in neuroimaging studies; not only are their atoms biologically informed, they are also adaptive to high resolutions, represent high dimensions efficiently, and capture long-range spatial dependencies, which are important and challenging objectives for neuroimaging data.

PMID: 28782681 [PubMed - as supplied by publisher]

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