Frontoparietal activation during visual conjunction search: Effects of bottom-up guidance and adult age.
Hum Brain Mapp. 2017 Jan 04;:
Authors: Madden DJ, Parks EL, Tallman CW, Boylan MA, Hoagey DA, Cocjin SB, Johnson MA, Chou YH, Potter GG, Chen NK, Packard LE, Siciliano RE, Monge ZA, Diaz MT
We conducted functional magnetic resonance imaging (fMRI) with a visual search paradigm to test the hypothesis that aging is associated with increased frontoparietal involvement in both target detection and bottom-up attentional guidance (featural salience). Participants were 68 healthy adults, distributed continuously across 19 to 78 years of age. Frontoparietal regions of interest (ROIs) were defined from resting-state scans obtained prior to task-related fMRI. The search target was defined by a conjunction of color and orientation. Each display contained one item that was larger than the others (i.e., a size singleton) but was not informative regarding target identity. Analyses of search reaction time (RT) indicated that bottom-up attentional guidance from the size singleton (when coincident with the target) was relatively constant as a function of age. Frontoparietal fMRI activation related to target detection was constant as a function of age, as was the reduction in activation associated with salient targets. However, for individuals 35 years of age and older, engagement of the left frontal eye field (FEF) in bottom-up guidance was more prominent than for younger individuals. Further, the age-related differences in left FEF activation were a consequence of decreasing resting-state functional connectivity in visual sensory regions. These findings indicate that age-related compensatory effects may be expressed in the relation between activation and behavior, rather than in the magnitude of activation, and that relevant changes in the activation-RT relation may begin at a relatively early point in adulthood. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
PMID: 28052456 [PubMed - as supplied by publisher]
Correspondence between evoked and intrinsic functional brain network configurations.
Hum Brain Mapp. 2017 Jan 04;:
Authors: Bolt T, Nomi JS, Rubinov M, Uddin LQ
Much of the literature exploring differences between intrinsic and task-evoked brain architectures has examined changes in functional connectivity patterns between specific brain regions. While informative, this approach overlooks important overall functional changes in hub organization and network topology that may provide insights about differences in integration between intrinsic and task-evoked states. Examination of changes in overall network organization, such as a change in the concentration of hub nodes or a quantitative change in network organization, is important for understanding the underlying processes that differ between intrinsic and task-evoked brain architectures. The present study used graph-theoretical techniques applied to publicly available neuroimaging data collected from a large sample of individuals (N = 202), and a within-subject design where resting-state and several task scans were collected from each participant as part of the Human Connectome Project. We demonstrate that differences between intrinsic and task-evoked brain networks are characterized by a task-general shift in high-connectivity hubs from primarily sensorimotor/auditory processing areas during the intrinsic state to executive control/salience network areas during task performance. In addition, we demonstrate that differences between intrinsic and task-evoked architectures are associated with changes in overall network organization, such as increases in network clustering, global efficiency and integration between modules. These findings offer a new perspective on the principles guiding functional brain organization by identifying unique and divergent properties of overall network organization between the resting-state and task performance. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
PMID: 28052450 [PubMed - as supplied by publisher]
Distinction between Neural and Vascular BOLD Oscillations and Intertwined Heart Rate Oscillations at 0.1 Hz in the Resting State and during Movement.
PLoS One. 2017;12(1):e0168097
Authors: Pfurtscheller G, Schwerdtfeger A, Brunner C, Aigner C, Fink D, Brito J, Carmo MP, Andrade A
In the resting state, blood oxygen level-dependent (BOLD) oscillations with a frequency of about 0.1 Hz are conspicuous. Whether their origin is neural or vascular is not yet fully understood. Furthermore, it is not clear whether these BOLD oscillations interact with slow oscillations in heart rate (HR). To address these two questions, we estimated phase-locking (PL) values between precentral gyrus (PCG) and insula in 25 scanner-naïve individuals during rest and stimulus-paced finger movements in both hemispheres. PL was quantified in terms of time delay and duration in the frequency band 0.07 to 0.13 Hz. Results revealed both positive and negative time delays. Positive time delays characterize neural BOLD oscillations leading in the PCG, whereas negative time delays represent vascular BOLD oscillations leading in the insula. About 50% of the participants revealed positive time delays distinctive for neural BOLD oscillations, either with short or long unilateral or bilateral phase-locking episodes. An expected preponderance of neural BOLD oscillations was found in the left hemisphere during right-handed movement and unexpectedly in the right hemisphere during rest. Only neural BOLD oscillations were significantly associated with heart rate variability (HRV) in the 0.1-Hz range in the first resting state. It is well known that participating in magnetic resonance imaging (MRI) studies may be frightening and cause anxiety. In this respect it is important to note that the most significant hemispheric asymmetry (p<0.002) with a right-sided dominance of neural BOLD and a left-sided dominance of vascular BOLD oscillations was found in the first resting session in the scanner-naïve individuals. Whether the enhanced left-sided perfusion (dominance of vascular BOLD) or the right-sided dominance of neural BOLD is related to the increased level of anxiety, attention or stress needs further research.
PMID: 28052074 [PubMed - in process]
WE-FG-206-02: Brief Variations of BOLD Signal in Resting State FMRI Leading to Functional Connectivity Pattern Identification.
Med Phys. 2016 Jun;43(6):3831
Authors: Ball N, Chen N
PURPOSE: To assess the role of spontaneous brain activity in identifying functional connectivity patterns in the brain during resting-state using both high and low blood-oxygen level dependent (BOLD) signals from functional magnetic resonance imaging (fMRI).
METHODS: fMRI BOLD signals within seed regions from known functional connectivity networks were extracted to find an average seed-region time profile. A threshold was applied to the BOLD signal and and time points were selected where the BOLD signal satisfied the threshold requirements. The same threshold was also applied to the time profile of each voxel in the brain, i.e., including voxels outside of the seed region. The signal profile of seed region time points above the threshold was compared to the signal profile of whole brain voxel time points over the threshold. Any time point that occurred on both signal profiles was considered a match. We then evaluated whether or not a voxel was part of the functional connectivity network based on the percentage of matching time points.
RESULTS: These findings suggest that functional connectivity patterns can be identified only using a small number of time points. Using only 10% of the time points during a functional MRI functional connectivity patterns can be reproduced with varying degrees of accuracy. Using this same method, with a 33% threshold a histogram of numbers of matches per matches possible within the region where we expect to see the functional connectivity pattern, shows no significant separation between using the top third, the middle third, or the bottom third of data.
CONCLUSION: This work indicates that functional connectivity patterns can be reproduced not only with spontaneous activity of high signals, but also from brief instances of low BOLD signal. As the potential role of resting-state fMRI for clinical diagnosis evolves, understanding the fundamentals of functional connectivity patterns is critical. NIH R01-NS074045.
PMID: 28048928 [PubMed - in process]
SU-F-J-139: Amplitude of Low Frequency Fluctuation(ALFF) and Regional Homogeneity (ReHo) Study of the Respiration Motion Control Byhypnosis.
Med Phys. 2016 Jun;43(6):3439
Authors: Liu Y, Li R, Xie Y
PURPOSE: Respiration control by hypnosis is a method in reducing the detriment to the healthy organs or organizations for patients during radiotherapy, especially for lung and abdomen cancer (Fig.1). It's hypothesized that there exists alterations neurological brain activity during the hypnosis state of respiratory motion control in comparison with resting state.
METHODS: Thirteen healthy volunteers were organized to participate in a hypnosis experiment that consisted of two sectional scans of functional magnetic resonance imaging (fMRI), rest state condition (RSC) scanning and hypnosis state condition (HSC) scanning. In addition, the coronal section of the lung was scanned during both conditions. During the hypnosis scan, the volunteers were under the hypnotists' guidance to keep peace and stable respiration. To evaluate the altered physiological performance of hypnosis in the respiratory control, three conventional indicators ALFF/fALFF (0.01-0.08Hz) and ReHo, were applied to identify the difference.
RESULTS: Compared with RSC, HSC showed significant (p<0.05) higher ReHo in superior temporal gyrus, middle temporal gyrus, frontal lobe, middle occipital gyrus, parietal lobe, cerebellum anterior Lobe and lingual gyrus, and left brainstem (Fig.2). While significant lower ReHo in middle frontal gyrus, superior frontal gyrus, inferior semi-lunar lobule, sub-lobar and limbic lobe (Fig.2). As for the ALFF results, significant higher value of HSC was observed in superior temporal gyrus, middle temporal gyrus, middle occipital gyrus, middle occipital gyrus, cerebellum anterior lobe, lingual gyrus, sub-lobar, limbic lobe, and lower in cerebellum posterior lobe, inferior semi-lunar lobule, inferior parietal lobule right middle frontal gyrus, cerebellar tonsil (Fig.3). The results of fALFF were similar to ALFF (Fig.4). The above results demonstrated that most significant regions of brain were uniform between ReHo and ALFF/fALFF.
CONCLUSION: Hypnosis is a new psychological and helpful technology for respiration control. This study provides new insights of neurological brain activity during hypnosis of respiration control. This work is supported by grants from Guangdong Innovative Research Team Program of China (Grant No. 2011S013), National 863 Programs of China (Grant Nos. 2012AA02A604 and 2015AA043203), the National High-tech R&D Program for Young Scientists by the Ministry of Science and Technology of China (Grant No. 2015AA020917).
PMID: 28048305 [PubMed - in process]
SU-G-IeP1-11: Resting-State Fluctuation of BOLD Signal Amplitude for Mapping Cerebrovascular Reactivity in Presurgical Functional MRI.
Med Phys. 2016 Jun;43(6):3646-3647
Authors: Wang P, Hou P, Kesler S, Colen R, Kumar A, Prabhu S, Liu H
PURPOSE: Cerebrovascular reactivity (CVR) MRI with hypercapnia challenges, such as a breath-hold (BH) task, has been proposed to indicate areas with neurovascular uncoupling potentials for presurgical functional MRI (fMRI). This study aimed to explore the use of resting-state (RS) fMRI, in particular, the RS fluctuation of amplitude (RSFA) for detecting the impaired CVR and compare with BH MRI in patients with gliomas.
METHODS: The BH and RS fMRI from six patients as part of their presurgical fMRI studies were analyzed. Functional images were coregistered to the 3D T1-weighted images and spatially smoothed using a 4-mm Gaussian kernel. A band-pass filter (0.01-0.08 Hz) was then applied to the RS time series. Temporal standard deviations of the BH and the filtered RS signal changes were computed for each voxel to indicate CVR and subsequently transformed to Z-scores. For quantitative comparison, a region of interest (ROI) with significant CVR in the sensorimotor cortex on the contralateral side of the tumor was determined for each patient as Z-scores greater than 50% of the local maxima. In addition, a tumor ROI was determined by referencing the T2 FLAIR and post-contrast T1-weighted images of each patient.
RESULTS: Significant cross-subject correlations between the BH CVR and RSFA were found in both the sensorimotor and tumor ROIs (r=0.59, p<0.05 and r=0.83, p<0.05, respectively). Within individuals, voxel-based analyses showed significant correlations between two methods in both ROIs in all patients. The spatial patterns of BH CVR and RSFA maps appeared similar across the brain with sparse local discrepancies.
CONCLUSION: The RSFA derived from RS-fMRI is a promising method for probing impaired CVR in presurgical fMRI mapping. Unlike BH, RS-fMRI is less dependent on patient performance. In addition, with its proven relation to neural activity, RSFA of certain frequency bands has potentials to better indicate the neurovascular uncoupling.
PMID: 28047432 [PubMed - in process]
Disturbed Brain Activity in Resting-State Networks of Patients with First-Episode Schizophrenia with Auditory Verbal Hallucinations: A Cross-sectional Functional MR Imaging Study.
Radiology. 2016 Jan 03;:160938
Authors: Cui LB, Liu L, Guo F, Chen YC, Chen G, Xi M, Qin W, Sun JB, Li C, Xi YB, Wang HN, Yin H
Purpose To investigate auditory verbal hallucination (AVH)-specific patterns of brain activity within the resting-state networks (RSNs) that have been proposed to underpin the neural mechanisms of schizophrenia (SZ). Materials and Methods This cross-sectional study was approved by the local ethics committee, and written informed consent was obtained from all participants prospectively recruited. Independent component analysis was used to investigate RSNs in 17 patients with first-episode untreated SZ with AVHs, 15 patients with SZ without AVHs, and 19 healthy control subjects who underwent resting-state functional magnetic resonance imaging. Dual regression was implemented to perform between-group analysis. Regional brain function was then explored within RSNs by using the amplitude of low-frequency fluctuation. Two-sample t tests were used to compare regional brain function between the two patient groups, and Pearson correlation analysis was used to characterize the relationship between imaging findings and severity of AVHs. Receiver operating characteristic analysis was used to evaluate the diagnostic performance of these brain function measures. Results Independent component analysis demonstrated symptom-specific abnormal disrupted coactivation within the auditory, default mode, executive, motor, and frontoparietal networks and was pronounced in the auditory cortex, supramarginal gyrus, insula, putamen, dorsolateral prefrontal cortex, angular gyrus, precuneus, and thalamus (P < .05 with false discovery rate correction). Amplitude of low-frequency fluctuation analysis demonstrated similar patterns within these RSNs (P < .05 with false discovery rate correction). Furthermore, a positive correlation between the degree of coactivation within the motor network and the severity of AVHs was observed in patients with SZ with AVHs (r = 0.67, P = .003). The area under the receiver operating characteristic curve was 0.76-0.90 for all RSNs. Conclusion These findings indicate that dysfunctional brain regions are involved in auditory processing, language production and monitoring, and sensory information filtering in patients with SZ with AVHs, which may be helpful in furthering the understanding of pathophysiological correlates of AVHs in SZ. Online supplemental material is available for this article.
PMID: 28045645 [PubMed - as supplied by publisher]
Correspondent functional topography of the human left inferior parietal lobule at rest and under task revealed using resting-state fMRI and coactivation based parcellation.
Hum Brain Mapp. 2017 Jan 03;:
Authors: Wang J, Xie S, Guo X, Becker B, Fox PT, Eickhoff SB, Jiang T
The human left inferior parietal lobule (LIPL) plays a pivotal role in many cognitive functions and is an important node in the default mode network (DMN). Although many previous studies have proposed different parcellation schemes for the LIPL, the detailed functional organization of the LIPL and the exact correspondence between the DMN and LIPL subregions remain unclear. Mounting evidence indicates that spontaneous fluctuations in the brain are strongly associated with cognitive performance at the behavioral level. However, whether a consistent functional topographic organization of the LIPL during rest and under task can be revealed remains unknown. Here, they used resting-state functional connectivity (RSFC) and task-related coactivation patterns separately to parcellate the LIPL and identified seven subregions. Four subregions were located in the supramarginal gyrus (SMG) and three subregions were located in the angular gyrus (AG). The subregion-specific networks and functional characterization revealed that the four anterior subregions were found to be primarily involved in sensorimotor processing, movement imagination and inhibitory control, audition perception and speech processing, and social cognition, whereas the three posterior subregions were mainly involved in episodic memory, semantic processing, and spatial cognition. The results revealed a detailed functional organization of the LIPL and suggested that the LIPL is a functionally heterogeneous area. In addition, the present study demonstrated that the functional architecture of the LIPL during rest corresponds with that found in task processing. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.
PMID: 28045222 [PubMed - as supplied by publisher]
Abnormal Resting-State Neural Activity and Connectivity of Fatigue in Parkinson's Disease.
CNS Neurosci Ther. 2017 Jan 03;:
Authors: Zhang JJ, Ding J, Li JY, Wang M, Yuan YS, Zhang L, Jiang SM, Wang XX, Zhu L, Zhang KZ
AIMS: Fatigue is a common burdensome problem in patients with Parkinson's disease (PD), but its pathophysiological mechanisms are poorly understood. This study aimed at investigating the neural substrates of fatigue in patients with PD.
METHODS: A total of 17 PD patients with fatigue, 32 PD patients without fatigue, and 25 matched healthy controls were recruited. The 9-item fatigue severity scale (FSS) was used for fatigue screening and severity rating. Resting-state functional magnetic resonance imaging (RS-fMRI) data were obtained from all subjects. Amplitude of low-frequency fluctuations (ALFF) was used to measure regional brain activity, and functional connectivity (FC) was applied to investigate functional connectivity at a network level.
RESULTS: PD-related fatigue was associated with ALFF changes in right middle frontal gyrus within the attention network and in left insula as well as right midcingulate cortex within the salience network. FC analysis revealed that above three regions showing ALFF differences had altered functional connectivity mainly in the temporal, parietal, and motor cortices.
CONCLUSION: Our findings do reveal that abnormal regional brain activity within attention and salience network and altered FC of above abnormal regions are involved in neural mechanism of fatigue in patients with PD.
PMID: 28044431 [PubMed - as supplied by publisher]
A Family of Locally Constrained CCA Models for Detecting Activation Patterns in fMRI.
Neuroimage. 2016 Dec 29;:
Authors: Zhuang X, Yang Z, Curran T, Byrd R, Nandy R, Cordes D
Canonical correlation analysis (CCA) has been used in functional Magnetic Resonance Imaging (fMRI) for improved detection of activation by incorporating time series from multiple voxels in a local neighborhood. To improve the specificity of local CCA methods, spatial constraints were previously proposed. In this study, constraints are generalized by introducing a family model of spatial constraints for CCA to further increase both sensitivity and specificity in fMRI activation detection. The proposed locally-constrained CCA (cCCA) model is formulated in terms of a multivariate constrained optimization problem and solved efficiently with numerical optimization techniques. To evaluate the performance of this cCCA model, simulated data are generated with a Signal-To-Noise Ratio of 0.25, which is realistic to the noise level contained in episodic memory fMRI data. Receiver operating characteristic (ROC) methods are used to compare the performance of different models. The cCCA model with optimum parameters (called optimum-cCCA) obtains the largest area under the ROC curve. Furthermore, a novel validation method is proposed to validate the selected optimum-cCCA parameters based on ROC from simulated data and real fMRI data. Results for optimum-cCCA are then compared with conventional fMRI analysis methods using data from an episodic memory task. Wavelet-resampled resting-state data are used to obtain the null distribution of activation. For simulated data, accuracy in detecting activation increases for the optimum-cCCA model by about 43% as compared to the single voxel analysis with comparable Gaussian smoothing. Results from the real fMRI data set indicate a significant increase in activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.
PMID: 28041980 [PubMed - as supplied by publisher]
Functional Anatomy of the Human Thalamus at Rest.
Neuroimage. 2016 Dec 29;:
Authors: Jangir Kumar V, van Oort E, Scheffler K, Beckmann CF, Grodd W
In the present work, we used resting state-fMRI to investigate the functional anatomy of the thalamus at rest by applying an Independent Component Analysis to delineate thalamic substructures into stable and reproducible parcels for the left and right thalamus. We determined 15 functionally distinct thalamic parcels, which differed in laterality and size but exhibited a correspondence with 18 cytoarchitectonally defined nuclei. We characterized their structural connectivity in determining DWI based cortical fiber pathways and found selected projections to different cortical areas. In contrast, the functional connections of these parcels were not confined to certain cortical areas or lobes. We, finally evaluated cortical projections and found particular subcortical and cortical pattern for each parcel, which partly exhibited a correspondence with the thalamo-cortical connectivity maps of the mouse.
PMID: 28041978 [PubMed - as supplied by publisher]
Abnormal spontaneous brain activity in type 2 diabetic retinopathy revealed by amplitude of low-frequency fluctuations: a resting-state fMRI study.
Clin Radiol. 2016 Dec 29;:
Authors: Wang ZL, Zou L, Lu ZW, Xie XQ, Jia ZZ, Pan CJ, Zhang GX, Ge XM
AIM: To explore the altered spontaneous cerebral activity patterns and impaired functional regions in patients with diabetic retinopathy (DR) using resting-state functional magnetic resonance imaging (rs-fMRI) based on the amplitude of low-frequency fluctuations (ALFF) algorithm.
MATERIALS AND METHODS: Twenty-one patients with DR (mean age, 54.9±9.9 years; 11 females) and 17 healthy control subjects (54.8±5.7 years; 9 females) were prospectively studied. The DR patients underwent laboratory tests. All individuals underwent a neuropsychological test. The differences in the ALFF values between the two groups were compared. The relationships between ALFF values and clinical measurements were analysed using a multiple-factor analysis.
RESULTS: Compared to the controls, the DR group showed significantly increased ALFF values in the bilateral occipital gyrus, right lingual gyrus, and precuneus, and decreased values in the right posterior/anterior cerebellar lobe and the parahippocampal, fusiform, superior temporal, inferior parietal, and angular gyrus. Furthermore, the Montreal Cognitive Assessment (MoCA) scores were negatively correlated with decreased ALFF values in the right occipital lobe of the DR group, while increased ALFF values in the right precuneus and lingual gyrus were found to be positively correlated with glycosylated haemoglobin (HbA1c) levels.
CONCLUSIONS: Patients with DR showed spontaneous cerebral activity abnormalities in many cerebral regions that were associated with cognitive impairments. HbA1c levels altered spontaneous cerebral activity in DR patients.
PMID: 28041652 [PubMed - as supplied by publisher]
Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample.
J Neural Transm (Vienna). 2016 Dec 31;:
Authors: Sundermann B, Feder S, Wersching H, Teuber A, Schwindt W, Kugel H, Heindel W, Arolt V, Berger K, Pfleiderer B
In small, selected samples, an approach combining resting-state functional connectivity MRI and multivariate pattern analysis has been able to successfully classify patients diagnosed with unipolar depression. Purposes of this investigation were to assess the generalizability of this approach to a large clinically more realistic sample and secondarily to assess the replicability of previously reported methodological feasibility in a more homogeneous subgroup with pronounced depressive symptoms. Two independent subsets were drawn from the depression and control cohorts of the BiDirect study, each with 180 patients with and 180 controls without depression. Functional connectivity either among regions covering the gray matter or selected regions with known alterations in depression was assessed by resting-state fMRI. Support vector machines with and without automated feature selection were used to train classifiers differentiating between individual patients and controls in the entire first subset as well as in the subgroup. Model parameters were explored systematically. The second independent subset was used for validation of successful models. Classification accuracies in the large, heterogeneous sample ranged from 45.0 to 56.1% (chance level 50.0%). In the subgroup with higher depression severity, three out of 90 models performed significantly above chance (60.8-61.7% at independent validation). In conclusion, common classification methods previously successful in small homogenous depression samples do not immediately translate to a more realistic population. Future research to develop diagnostic classification approaches in depression should focus on more specific clinical questions and consider heterogeneity, including symptom severity as an important factor.
PMID: 28040847 [PubMed - as supplied by publisher]
Functional connectivity decreases in autism in emotion, self, and face circuits identified by Knowledge-based Enrichment Analysis.
Neuroimage. 2016 Dec 28;:
Authors: Cheng W, Rolls ET, Zhang J, Sheng W, Ma L, Wan L, Luo Q, Feng J
A powerful new method is described called Knowledge based functional connectivity Enrichment Analysis (KEA) for interpreting resting state functional connectivity, using circuits that are functionally identified using search terms with the Neurosynth database. The method derives its power by focusing on neural circuits, sets of brain regions that share a common biological function, instead of trying to interpret single functional connectivity links. This provides a novel way of investigating how task- or function-related related networks have resting state functional connectivity differences in different psychiatric states, provides a new way to bridge the gap between task and resting-state functional networks, and potentially helps to identify brain networks that might be treated. The method was applied to interpreting functional connectivity differences in autism. Functional connectivity decreases at the network circuit level in 394 patients with autism compared with 473 controls were found in networks involving the orbitofrontal cortex, anterior cingulate cortex, middle temporal gyrus cortex, and the precuneus, in networks that are implicated in the sense of self, face processing, and theory of mind. The decreases were correlated with symptom severity.
PMID: 28040544 [PubMed - as supplied by publisher]