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Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis.

Sun, 04/21/2019 - 22:34
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Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis.

Hum Brain Mapp. 2019 Apr 19;:

Authors: James O, Park H, Kim SG

Abstract
A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross-covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.

PMID: 31004386 [PubMed - as supplied by publisher]

Dependence of resting-state fMRI fluctuation amplitudes on cerebral cortical orientation relative to the direction of B0 and anatomical axes.

Sat, 04/20/2019 - 22:33
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Dependence of resting-state fMRI fluctuation amplitudes on cerebral cortical orientation relative to the direction of B0 and anatomical axes.

Neuroimage. 2019 Apr 16;:

Authors: Viessmann O, Scheffler K, Bianciardi M, Wald LL, Polimeni JR

Abstract
Functional magnetic resonance imaging (fMRI) is now capable of sub-millimetre scale measurements over the entire human brain, however with such high resolutions each voxel is influenced by the local fine-scale details of the cerebral cortical vascular anatomy. The cortical vasculature is structured with the pial vessels lying tangentially along the grey matter surface, intracortical diving arterioles and ascending venules running perpendicularly to the surface, and a randomly oriented capillary network within the parenchyma. It is well-known that the amplitude of the blood-oxygenation level dependent (BOLD) signal emanating from a vessel depends on its orientation relative to the B0field. Thus the vascular geometric hierarchy will impart an orientation dependence to the BOLD signal amplitudes and amplitude differences due to orientation differences constitute a bias for interpreting neuronal activity. Here, we demonstrate a clear effect of cortical orientation to B0in the resting-state BOLD-fMRI amplitude (quantified as the coefficient of temporal signal variation) for 1.1 mm, isotropic data at 7T and 2 mm, isotropic at 3T. The maximum bias, i.e. the fluctuation amplitude difference between regions where cortex is perpendicular to vs. parallel to B0, is about +70% at the pial surface at 7T and +11% at 3T. The B0orientation bias declines with cortical depth, becomes progressively smaller closer to the white matter surface, but then increases again to a local maximum within the white matter just beneath the cortical grey matter, suggesting a distinct tangential network of white matter vessels that also generate a BOLD orientation effect. We further found significant (negative) biases with the cortex orientation to the anterior-posterior anatomical axis of the head: a maximum negative bias of about -30% at the pial surface at 7T and about -13% at 3T. The amount of signal variance explained by the low frequency drift, motion and the respiratory cycle also showed a cortical orientation dependence; only the cardiac cycle induced signal variance was independent of cortical orientation, suggesting that the cardiac induced component of the image time-series fluctuations is not related to a significant change in susceptibility. Although these orientation effects represent a signal bias, and are likely to be a nuisance in high-resolution analyses, they may help characterize the vascular influences on candidate fMRI acquisitions and, thereby, may be exploited to improve the neuronal specificity of fMRI.

PMID: 31002965 [PubMed - as supplied by publisher]

Spontaneous variation in electrocorticographic resting state connectivity.

Sat, 04/20/2019 - 22:33
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Spontaneous variation in electrocorticographic resting state connectivity.

Brain Connect. 2019 Apr 19;:

Authors: Casimo K, Madhyastha TM, Ko A, Brown AB, Grassia F, Ojemann J, Weaver K

Abstract
Prior studies using fMRI, electroencephalography, and magnetoencephalography have observed not only structured patterns in resting state functional connectivity, but also spontaneous longitudinal variation in connectivity patterns not specifically linked to a task. In this first study using electrocorticography, we characterized spontaneous, inter-session variation in resting state functional connectivity not linked to a task. We evaluated pairwise connectivity between electrodes using three measures (phase locking value, amplitude correlation, and coherence) for six canonical frequency bands, aimed at capturing different characteristics of time-evolving signals. We grouped electrodes into ten functional brain regions and used intraclass correlation to estimate longitudinal stability across pairwise connections. We found that stronger phase locking (PLV ≥0.4) in theta through gamma bands and strong correlation in all bands (R^2s ≥0.6) is linked to substantial stability (ICC ≥0.6), but that stability is not necessarily linked to strong phase locking or amplitude correlation. There was no notable link between strong connectivity and high ICC in coherence. In individual regions' phase locking, we note that all within-region connections are markedly stable across frequencies. Additionally, we examined broad patterns of interactions across several functional regions: parahippocampal-entorhinal cortex is characterized by stable yet weak functional connectivity except self-connections. Dorsolateral prefrontal cortex connectivity is weak and unstable, also except self-connections. Inferior parietal lobule has little stability within narrow connectivity bounds. We confirm prior studies linking functional connectivity strength and inter-session variability in resting state connectivity, and extend those findings into higher frequencies than other modalities, with greater spatial specificity than scalp electrophysiology. We suggest that further studies quantitatively compare electrocorticography to other modalities and/or use these findings as a baseline to capture functional connectivity and functional dynamics linked to perturbations attributable to a task or disease state.

PMID: 31002014 [PubMed - as supplied by publisher]

Aberrant Brain Function in Active-Stage Ulcerative Colitis Patients: A Resting-State Functional MRI Study.

Sat, 04/20/2019 - 22:33
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Aberrant Brain Function in Active-Stage Ulcerative Colitis Patients: A Resting-State Functional MRI Study.

Front Hum Neurosci. 2019;13:107

Authors: Fan W, Zhang S, Hu J, Liu B, Wen L, Gong M, Wang G, Yang L, Chen Y, Chen H, Guo H, Zhang D

Abstract
Background: Patients with ulcerative colitis (UC) usually display cognitive impairments, such as memory loss, attention deficits, and declining executive functions, particularly during the active stage of the disease. However, the potential neurological mechanisms of these symptoms remain unclear. Method: Forty-one patients with mildly to moderately active UC, as well as 42 matched healthy controls, were recruited for an examination using psychological scales, cognitive function tests and resting-state functional magnetic resonance imaging (rs-fMRI). Seed points were identified via analysis of amplitude of low-frequency fluctuation (ALFF), and functional connectivity (FC) was calculated between these seed regions and other voxels in the whole brain. Correlation analyses were performed among clinical indexes, neuropsychological assessments and neuroimaging data. Result: Compared with the healthy controls, patients with UC exhibited lower ALFF values in the bilateral hippocampal/parahippocampal (HIPP/ParaHIPP) region and higher ALFF values in the left posterior cingulate cortex (PCC.L) and left middle frontal gyrus (MFG.L). With HIPP/ParaHIPP as the seed point, the strengths of the FC in the bilateral middle frontal gyri (MFG), anterior cingulate cortex (ACC), and left caudate nucleus (CAU.L) increased; using the PCC.L as the seed point, the strengths of the FC in the middle cingulate cortex (MCC) and the left angular gyrus (AUG.L) increased. These abnormal brain regions were mainly located in the limbic system. By analyzing the correlations between these brain regions and behavioral data, we observed a close correlation between decreased HIPP/ParaHIPP activity and memory loss; increased PCC activity and strength of FC with the AUG.L were related to dysfunction of executive function and attention network in patients with UC. Conclusion: Based on these results, the limbic lobe might be the core of the brain-gut axis (BGA) and play an important role in cognitive impairments, suggesting potential mechanisms for cognitive impairment in patients with UC in the active stage of the disease.

PMID: 31001097 [PubMed]

Sampling Rate Effects on Resting State fMRI Metrics.

Sat, 04/20/2019 - 22:33
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Sampling Rate Effects on Resting State fMRI Metrics.

Front Neurosci. 2019;13:279

Authors: Huotari N, Raitamaa L, Helakari H, Kananen J, Raatikainen V, Rasila A, Tuovinen T, Kantola J, Borchardt V, Kiviniemi VJ, Korhonen VO

Abstract
Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3-3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01-0.1 Hz), respiratory (0.12-0.35 Hz) and cardiac power (0.9-1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1-2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1-3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1-2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning.

PMID: 31001071 [PubMed]

Patterns of on-task thought in older age are associated with changes in functional connectivity between temporal and prefrontal regions.

Sat, 04/20/2019 - 01:32
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Patterns of on-task thought in older age are associated with changes in functional connectivity between temporal and prefrontal regions.

Brain Cogn. 2019 Apr 15;132:118-128

Authors: Martinon LM, Riby LM, Poerio G, Wang HT, Jefferies E, Smallwood J

Abstract
Humans spend a large proportion of their time engaged in thoughts unrelated to the task being performed, a tendency that declines with age. However, a clear neuro-cognitive account of what underlies this decrease is lacking. This study addresses the possibility that age-related changes in off-task thinking are correlated with changes in the intrinsic organisation of the brain. Laboratory measures of ongoing thought were recorded in young and older individuals, who also participated in a resting state fMRI experiment. Older individuals showed reduced connectivity between the left anterior temporal lobe with prefrontal aspects of the DMN. We found that off-task thinking did not increase when task demands were lower for older adults, which is a pattern repeatedly seen in younger individuals. Finally, we demonstrated that these neural and thought patterns were linked - for younger participants only, reductions in the strength of connectivity were related to a greater shift towards off-task thoughts when task demands decreased. Importantly, in the older individuals, lower connectivity between the same regions was linked to preserved performance on a creativity task. These data suggest that the age-related reduction of off-task thought may be related to reduced communication between temporal and prefrontal DMN regions in ageing.

PMID: 30999087 [PubMed - as supplied by publisher]

Functional Connectivity Pattern in the Core Face Network Reflects Different Mechanisms of Holistic Face Processing Measured by the Whole-Part Effect and Composite-Face Effect.

Sat, 04/20/2019 - 01:32
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Functional Connectivity Pattern in the Core Face Network Reflects Different Mechanisms of Holistic Face Processing Measured by the Whole-Part Effect and Composite-Face Effect.

Neuroscience. 2019 Apr 15;:

Authors: Li J, Song Y, Liu J

Abstract
Holistic face processing is a critical component of face recognition. There are two classical measures of holistic face processing: the whole-part effect (WPE) and composite-face effect (CFE). However, the two effects have demonstrated inconsistent pattern of results in behavioral literature. Here, to address whether the WPE and CFE tap different mechanisms of holistic face processing, we examined the neural basis of the two effects at network level in a large sample of participants. With a voxel-wise global brain connectivity approach based on resting-state fMRI, we calculated the within network connectivity (WNC) of each voxel in the core face network (CFN). We found that a cluster in the right occipital face area (rOFA) showed positive correlation between its WNC and the WPE, while a cluster in the right fusiform face area (rFFA) showed negative correlation between its WNC and the CFE. These results suggested that the WPE was related to integration of the rOFA within the CFN, while the CFE was associated with separation of the rFFA from other CFN regions. Further analyses showed that higher WPE was related to stronger connection between the rOFA and bilateral posterior superior temporal sulcus (pSTS), while larger CFE was associated with weaker connection between the rFFA and bilateral pSTS. In short, our study reveals distinct neural correlates of the two hallmarks of holistic face processing at network level and sheds new light on the different mechanisms of holistic face processing reflected by the two effects.

PMID: 30999034 [PubMed - as supplied by publisher]

Functional connectome from phase synchrony at resting state is a neural fingerprint.

Sat, 04/20/2019 - 01:32
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Functional connectome from phase synchrony at resting state is a neural fingerprint.

Brain Connect. 2019 Apr 18;:

Authors: Zhang R, Kranz G, Lee TMC

Abstract
Coherent oscillatory activity across brain regions provides a variety of individual-specific characteristics, sometimes referred to as a neural fingerprint. This information, however, may not be directly retrieved from raw fMRI time series. In this study, we examined the data of 205 participants who completed two resting-state fMRI scanning sessions, separated by an average of 2.63 years. In the first step, we tested the long-term reliability of functional connectomes derived from amplitude-based functional connectivity (the conventional method) and found that they remained accurate markers (> 85%, p < 0.001, permutation test) for identifying individuals, even after a period longer than 800 days. Using the same data set, we further expanded our exploration of the extent to which two analytic components of oscillatory activity (amplitude envelope and instantaneous phase) may function as reliable fingerprints. Both analytic signals-in particular, the instantaneous phase-were identified as useful indices in shaping functional connectivity fingerprints (86%, p < 0.001, permutation test). Connectivity profiles derived from the ventral attention, frontoparietal, and default mode networks (DMNs) were the largest contributing factors to identification. The current results suggest that neural synchronization tapped by analytical signal from a low-frequency resting-state fMRI BOLD oscillation could be a reliable and useful fingerprint for identifying individuals and might provide an alternative method for characterizing dynamic functional connectivity profiles.

PMID: 30997813 [PubMed - as supplied by publisher]

Relationship between changes in resting-state spontaneous brain activity and cognitive impairment in patients with CADASIL.

Sat, 04/20/2019 - 01:32
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Relationship between changes in resting-state spontaneous brain activity and cognitive impairment in patients with CADASIL.

J Headache Pain. 2019 Apr 17;20(1):36

Authors: Su J, Wang M, Ban S, Wang L, Cheng X, Hua F, Tang Y, Zhou H, Zhai Y, Du X, Liu J

Abstract
BACKGROUND: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) mainly manifests with cognitive impairment. Cognitive deficits in patients with CADASIL are correlated with structural brain changes such as lacunar lesion burden, normalized brain volume, and anterior thalamic radiation lesions, but changes in resting-state functional brain activity in patients with CADASIL have not been reported.
METHODS: This study used resting-state functional magnetic resonance imaging (fMRI) to measure the amplitude of low-frequency fluctuation (ALFF) in 22 patients with CADASIL and 44 healthy matched controls. A seed-based functional connectivity (FC) analysis was used to investigate whether the dysfunctional areas identified by ALFF analysis exhibited abnormal FC with other brain areas. Pearson's correlation analysis was used to detect correlations between the ALFF z-score of abnormal brain areas and clinical scores in patients with CADASIL.
RESULTS: Patients with CADASIL exhibited significantly lower ALFF values in the right precuneus and cuneus (Pcu/CU) and higher ALFF values in the bilateral superior frontal gyrus (SFG) and left cerebellar anterior and posterior lobes compared with controls. Patients with CADASIL showed weaker FC between the areas with abnormal ALFF (using peaks in the left and right SFG and the right Pcu/CU) and other brain areas. Importantly, the ALFF z-scores for the left and right SFG were negatively associated with cognitive performance, including Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment scores (MoCA), respectively, whereas those of the right Pcu/CU were positively correlated with the MMSE score.
CONCLUSIONS: This preliminary study provides evidence for changes in ALFF of the right Pcu/CU, bilateral SFG and left cerebellar anterior and posterior lobes, and associations between ALFF values for abnormal brain areas and cognitive performance in patients with CADASIL. Therefore, spontaneous brain activity may be a novel imaging biomarker of cognitive impairment in this population.

PMID: 30995925 [PubMed - in process]

Seed-based connectivity analysis of resting-state fMRI in patients with brain tumors: a practical approach.

Thu, 04/18/2019 - 18:04

Seed-based connectivity analysis of resting-state fMRI in patients with brain tumors: a practical approach.

World Neurosurg. 2019 Apr 14;:

Authors: Metwali H, Samii A

Abstract
OBJECTIVE: In this study, we are presenting our experience using resting state functional magnetic resonsce imaging (rs-fMRI) in preoperative planning. We performed goup analysis to demonestrate the effects of brain tumor on resting state networks (RSNs) METHODS: Thirty patients with supratentorial gliomas were included in the study. Preoperative rs-fMRI and structural MRI were performed in all cases. The rs-fMRI was preprocessed ( realignment, slice time correction, coregistration to structural images, normalization and smoothing). The structural images were segmented and normalized. Band filtering and denoising were applied to the functional images. Connectivity analysis was performed using seed based connectivity analysis (SCA) at single subject level and group level. Correlation algorism has been used with r>0.5.
RESULTS: RSNs could be detected in all patients. They showed similarity to the results of the task based fMRI, when task based fMRI was feasible. Detection of the networks was also possible in patients with neurological deficits, in whom task based fMRI was not possible. We could use SCA in patients under anesthesia. High level networks ( default mode, salience, and dorsal attention networks) were detectable but showed wide spectrum of spatial alterations and components disconnections.
CONCLUSION: Rs-fMRI is a feasible method for extended brain mapping. Diverse RSNs could be detected in patients with brain tumors and could be applied in preoperative planning. SCA was a robust and direct approach for data analysis and could answer specific clinically relevant questions. However, further studies are needed to validate the technique and its clinical impact.

PMID: 30995557 [PubMed - as supplied by publisher]

ALTERED BRAIN ACTIVITY IN PATIENTS WITH DIABETIC RETINOPATHY USING REGIONAL HOMOGENEITY: A RESTING-STATE fMRI STUDY.

Thu, 04/18/2019 - 18:04

ALTERED BRAIN ACTIVITY IN PATIENTS WITH DIABETIC RETINOPATHY USING REGIONAL HOMOGENEITY: A RESTING-STATE fMRI STUDY.

Endocr Pract. 2019 Apr;25(4):320-327

Authors: Liao XL, Yuan Q, Shi WQ, Li B, Su T, Lin Q, Min YL, Zhu PW, Ye L, Shao Y

Abstract
Objective: Previous neuroimaging studies have shown that diabetic retinopathy (DR) is accompanied by abnormal spontaneous brain activity. The purpose of the current study was to investigate changes in brain neural homogeneity in patients with DR using regional homogeneity (ReHo). Methods: A total of 56 subjects were recruited, including 28 patients with DR (16 female and 12 male patients) and 28 healthy controls (HCs) (16 female and 12 male patients) approximately matched for age and sex. All subjects underwent resting-state functional magnetic resonance imaging scans. The ReHo method was applied to explore neural homogeneity in the brain. The patients with DR were distinguished from HCs following the construction of receiver operating characteristic curves. The ReHo method was applied to assess changes in synchronous neural activity. Results: Compared to HCs, the ReHo values in the left and right posterior lobes of the cerebellum in patients with DR were significantly increased, whereas ReHo values in the right anterior cingulate gyrus, right cuneus, bilateral precuneus, and left-middle frontal gyrus were significantly decreased. In addition, the ReHo value in the right cuneus showed a positive correlation with the best corrected visual acuity in patients with DR. Conclusion: Dysfunctional brain homology may reveal the pathological mechanisms underlying the visual pathways of patients with DR. Abbreviations: AUC = area under the curve; BA = Brodmann area; DR = diabetic retinopathy; fMRI = functional magnetic resonance imaging; HC = healthy control; MRI = magnetic resonance imaging; rs-fMRI = resting-state fMRI; ReHo = regional homogeneity; ROC = receiver operating characteristic.

PMID: 30995427 [PubMed - in process]

Machine-learning identifies parkinson's disease patients based on resting-state between-network functional connectivity.

Thu, 04/18/2019 - 18:04

Machine-learning identifies parkinson's disease patients based on resting-state between-network functional connectivity.

Br J Radiol. 2019 Apr 17;:20180886

Authors: Rubbert C, Mathys C, Jockwitz C, Hartmann CJ, Eickhoff SB, Hoffstaedter F, Caspers S, Eickhoff CR, Sigl B, Teichert NA, Südmeyer M, Turowski B, Schnitzler A, Caspers J

Abstract
OBJECTIVES: Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson's disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI).
METHODS: Whole-brain rs-fMRI (EPI/TR = 2.2  s/TE = 30  ms/flip angle = 90°/resolution = 3.1 × 3.1 × 3.1  mm/acquisition time≈11  min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10- fold 20-repeats CV over the whole dataset to determine feature importance.
RESULTS: Over the outer folds the mean accuracy was found to be 76.2 % (median 77.8%, SD 18.2, IQR 69.4 - 87.1 %). Mean sensitivity was 81 % (median 80%, SD 21.1, IQR 75 - 100 %) and mean specificity was 72.7 % (median 75%, SD 20.4, IQR 66.7 - 80 %). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks.
CONCLUSIONS: A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting.
ADVANCES IN KNOWLEDGE: Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson's disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.

PMID: 30994036 [PubMed - as supplied by publisher]

Intrinsic insular-frontal networks predict future nicotine dependence severity.

Thu, 04/18/2019 - 18:04
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Intrinsic insular-frontal networks predict future nicotine dependence severity.

J Neurosci. 2019 Apr 16;:

Authors: Hsu LM, Keeley RJ, Liang X, Brynildsen JK, Lu H, Yang Y, Stein EA

Abstract
Although 60% of the US population have tried smoking cigarettes, only 16% smoke regularly. Identifying this susceptible subset of the population before the onset of nicotine dependence may encourage targeted early interventions to prevent regular smoking and/or minimize severity. While prospective neuroimaging in human populations can be challenging, preclinical neuroimaging models prior to chronic nicotine administration can help develop translational biomarkers of disease risk. Chronic, intermittent nicotine (0, 1.2 or 4.8 mg/kg/d (N = 10-11/group)) was administered to male Sprague Dawley rats for 14 days; dependence severity was quantified using precipitated withdrawal behaviors collected prior to, during and following forced nicotine abstinence. Resting state fMRI functional connectivity (FC) prior to drug administration was subjected to a graph theory analytical framework to form a predictive model of subsequent individual differences in nicotine dependence. Whole brain modularity analysis identified 5 modules in the rat brain. A metric of inter-module connectivity, participation coefficient (PC), of an identified insular-frontal cortical module predicted subsequent dependence severity, independent of nicotine dose. To better spatially isolate this effect, this module was subjected to a secondary exploratory modularity analysis, which segregated it into three submodules (frontal-motor, insula and sensory). Higher FC between these 3 sub-modules and 3 of the 5 originally identified modules (striatal, frontal-executive and sensory association) also predicted dependence severity. These data suggest that pre-dispositional, intrinsic differences in circuit strength between insular-frontal based brain networks prior to drug exposure may identify those at highest risk to the development of nicotine dependence.Significance statement:Developing biomarkers of individuals at high risk for addiction before the onset of this brain-based disease is essential for prevention, early intervention and/or subsequent treatment decisions. Using a rodent model of nicotine dependence and a novel data-driven, network-based analysis of resting state fMRI data collected prior to drug exposure, functional connections centered on an intrinsic insular-frontal module predicted the severity of nicotine dependence after drug exposure. The predictive capacity of baseline network measures was specific to inter-regional but not within-region connectivity. While insular and frontal regions have consistently been implicated in nicotine dependence, this is the first study to reveal that innate, individual differences in their circuit strength have the predictive capacity to identify those at greatest risk for and resilience to drug dependence.

PMID: 30992371 [PubMed - as supplied by publisher]

Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer's disease.

Thu, 04/18/2019 - 18:04
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Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer's disease.

Neuroimage Clin. 2019 Apr 03;22:101812

Authors: Schumacher J, Peraza LR, Firbank M, Thomas AJ, Kaiser M, Gallagher P, O'Brien JT, Blamire AM, Taylor JP

Abstract
We studied the dynamic functional connectivity profile of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) compared to controls, how it differs between the two dementia subtypes, and a possible relation between dynamic connectivity alterations and temporally transient clinical symptoms in DLB. Resting state fMRI data from 31 DLB, 29 AD, and 31 healthy control participants were analyzed using dual regression to determine between-network functional connectivity. Subsequently, we used a sliding window approach followed by k-means clustering and dynamic network analyses to study dynamic functional connectivity. Dynamic connectivity measures that showed significant group differences were tested for correlations with clinical symptom severity. Our results show that AD and DLB patients spent more time than controls in sparse connectivity configurations with absence of strong positive and negative connections and a relative isolation of motor networks from other networks. Additionally, DLB patients spent less time in a more strongly connected state and the variability of global brain network efficiency was reduced in DLB compared to controls. There were no significant correlations between dynamic connectivity measures and clinical symptom severity. An inability to switch out of states of low inter-network connectivity into more highly and specifically connected network configurations might be related to the presence of dementia in general as it was observed in both AD and DLB. In contrast, the loss of global efficiency variability in DLB might indicate the presence of an abnormally rigid brain network and the lack of economical dynamics, factors which could contribute to cognitive slowing and an inability to respond appropriately to situational demands.

PMID: 30991620 [PubMed - as supplied by publisher]

Intrinsic brain activity changes associated with adjuvant chemotherapy in older women with breast cancer: a pilot longitudinal study.

Wed, 04/17/2019 - 18:02
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Intrinsic brain activity changes associated with adjuvant chemotherapy in older women with breast cancer: a pilot longitudinal study.

Breast Cancer Res Treat. 2019 Apr 13;:

Authors: Chen BT, Jin T, Patel SK, Ye N, Ma H, Wong CW, Rockne RC, Root JC, Saykin AJ, Ahles TA, Holodny AI, Prakash N, Mortimer J, Waisman J, Yuan Y, Li D, Sedrak MS, Vazquez J, Katheria V, Dale W

Abstract
PURPOSE: Older cancer patients are at increased risk of cancer-related cognitive impairment. The purpose of this study was to assess the alterations in intrinsic brain activity associated with adjuvant chemotherapy in older women with breast cancer.
METHODS: Chemotherapy treatment (CT) group included sixteen women aged ≥ 60 years (range 60-82 years) with stage I-III breast cancers, who underwent both resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological testing with NIH Toolbox for Cognition before adjuvant chemotherapy, at time point 1 (TP1), and again within 1 month after completing chemotherapy, at time point 2 (TP2). Fourteen age- and sex-matched healthy controls (HC) underwent the same assessments at matched intervals. Three voxel-wise rs-fMRI parameters: amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity, were computed at each time point. The changes in rs-fMRI parameters from TP1 to TP2 for each group, the group differences in changes (the CT group vs. the HC group), and the group difference in the baseline rs-fMRI parameters were assessed. In addition, correlative analysis between the rs-fMRI parameters and neuropsychological testing scores was also performed.
RESULTS: In the CT group, one brain region, which included parts of the bilateral subcallosal gyri and right anterior cingulate gyrus, displayed increased ALFF from TP1 to TP2 (cluster p-corrected = 0.024); another brain region in the left precuneus displayed decreased fALFF from TP1 to TP2 (cluster level p-corrected = 0.025). No significant changes in the rs-fMRI parameters from TP1 to TP2 were observed in the HC group. Although ALFF and fALFF alterations were observed only in the CT group, none of the between-group differences in rs-fMRI parameter changes reached statistical significance.
CONCLUSIONS: Our study results of ALFF and fALFF alterations in the chemotherapy-treated women suggest that adjuvant chemotherapy may affect intrinsic brain activity in older women with breast cancer.

PMID: 30989462 [PubMed - as supplied by publisher]

Effects of Childhood Maltreatment on Social Cognition and Brain Functional Connectivity in Borderline Personality Disorder Patients.

Wed, 04/17/2019 - 18:02
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Effects of Childhood Maltreatment on Social Cognition and Brain Functional Connectivity in Borderline Personality Disorder Patients.

Front Psychiatry. 2019;10:156

Authors: Duque-Alarcón X, Alcalá-Lozano R, González-Olvera JJ, Garza-Villarreal EA, Pellicer F

Abstract
Borderline personality disorder (BPD) is a chronic condition characterized by high levels of impulsivity, affective instability, and difficulty to establish and manage interpersonal relationships. However, little is known about its etiology and neurobiological substrates. In our study, we wanted to investigate the influence of child abuse in the psychopathology of BPD by means of social cognitive paradigms [the Movie for the Assessment of Social Cognition (MASC) and the reading the mind in the eyes test (RMET)], and resting state functional magnetic resonance imaging (rs-fMRI). For this, we recruited 33 participants, 18 BPD patients, and 15 controls. High levels of self-reported childhood maltreatment were reported by BPD patients. For the sexual abuse subdimension, there were no differences between the BPD and the control groups, but there was a negative correlation between MASC scores and total childhood maltreatment levels, as well as between physical abuse, physical negligence, and MASC. Both groups showed that the higher the level of childhood maltreatment, the lower the performance on the MASC social cognitive test. Further, in the BPD group, there was hypoconnectivity between the structures responsible for emotion regulation and social cognitive responses that have been described as part of the frontolimbic circuitry (i.e., amygdala). Differential levels of connectivity, associated with different types and levels of abuse were also observed.

PMID: 30988667 [PubMed]

Functional connectomics of affective and psychotic pathology.

Wed, 04/17/2019 - 18:02
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Functional connectomics of affective and psychotic pathology.

Proc Natl Acad Sci U S A. 2019 Apr 15;:

Authors: Baker JT, Dillon DG, Patrick LM, Roffman JL, Brady RO, Pizzagalli DA, Öngür D, Holmes AJ

Abstract
Converging evidence indicates that groups of patients with nominally distinct psychiatric diagnoses are not separated by sharp or discontinuous neurobiological boundaries. In healthy populations, individual differences in behavior are reflected in variability across the collective set of functional brain connections (functional connectome). These data suggest that the spectra of transdiagnostic symptom profiles observed in psychiatric patients may map onto detectable patterns of network function. To examine the manner through which neurobiological variation might underlie clinical presentation, we obtained fMRI data from over 1,000 individuals, including 210 diagnosed with a primary psychotic disorder or affective psychosis (bipolar disorder with psychosis and schizophrenia or schizoaffective disorder), 192 presenting with a primary affective disorder without psychosis (unipolar depression, bipolar disorder without psychosis), and 608 demographically matched healthy comparison participants recruited through a large-scale study of brain imaging and genetics. Here, we examine variation in functional connectomes across psychiatric diagnoses, finding striking evidence for disease connectomic "fingerprints" that are commonly disrupted across distinct forms of pathology and appear to scale as a function of illness severity. The presence of affective and psychotic illnesses was associated with graded disruptions in frontoparietal network connectivity (encompassing aspects of dorsolateral prefrontal, dorsomedial prefrontal, lateral parietal, and posterior temporal cortices). Conversely, other properties of network connectivity, including default network integrity, were preferentially disrupted in patients with psychotic illness, but not patients without psychotic symptoms. This work allows us to establish key biological and clinical features of the functional connectomes of severe mental disease.

PMID: 30988201 [PubMed - as supplied by publisher]

Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification.

Mon, 04/15/2019 - 18:00

Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification.

Neuroinformatics. 2019 Apr 13;:

Authors: Li Y, Liu J, Peng Z, Sheng C, Kim M, Yap PT, Wee CY, Shen D

Abstract
Functional connectivity networks, derived from resting-state fMRI data, have been found as effective biomarkers for identifying mild cognitive impairment (MCI) from healthy elderly. However, the traditional functional connectivity network is essentially a low-order network with the assumption that the brain activity is static over the entire scanning period, ignoring temporal variations among the correlations derived from brain region pairs. To overcome this limitation, we proposed a new type of sparse functional connectivity network to precisely describe the relationship of temporal correlations among brain regions. Specifically, instead of using the simple pairwise Pearson's correlation coefficient as connectivity, we first estimate the temporal low-order functional connectivity for each region pair based on an ULS Group constrained-UOLS regression algorithm, where a combination of ultra-least squares (ULS) criterion with a Group constrained topology structure detection algorithm is applied to detect the topology of functional connectivity networks, aided by an Ultra-Orthogonal Least Squares (UOLS) algorithm to estimate connectivity strength. Compared to the classical least squares criterion which only measures the discrepancy between the observed signals and the model prediction function, the ULS criterion takes into consideration the discrepancy between the weak derivatives of the observed signals and the model prediction function and thus avoids the overfitting problem. By using a similar approach, we then estimate the high-order functional connectivity from the low-order connectivity to characterize signal flows among the brain regions. We finally fuse the low-order and the high-order networks using two decision trees for MCI classification. Experimental results demonstrate the effectiveness of the proposed method on MCI classification.

PMID: 30982183 [PubMed - as supplied by publisher]

Functional resting-state brain connectivity is accompanied by dynamic correlations of application-dependent [18F]FDG PET-tracer fluctuations.

Mon, 04/15/2019 - 18:00

Functional resting-state brain connectivity is accompanied by dynamic correlations of application-dependent [18F]FDG PET-tracer fluctuations.

Neuroimage. 2019 Apr 11;:

Authors: Amend M, Ionescu TM, Di X, Pichler BJ, Biswal BB, Wehrl HF

Abstract
Brain function is characterized by a convolution of various biochemical and physiological processes, raising the interest whether resting-state functional connectivity derived from hemodynamic scales shows underlying metabolic synchronies. Increasing evidence suggests that metabolic connectivity based on glucose consumption associated PET recordings may serve as a marker of cognitive functions and neuropathologies. However, to what extent fMRI-derived resting-state brain connectivity can also be characterized based on dynamic fluctuations of glucose metabolism and how metabolic connectivity is influenced by [18F]FDG pharmacokinetics remains unsolved. Simultaneous PET/MRI measurements were performed in a total of 26 healthy male Lewis rats. Simultaneously to resting-state fMRI scans, one cohort (n = 15) received classical bolus [18F]FDG injections and dynamic PET images were recorded. In a second cohort (n = 11) [18F]FDG was constantly infused over the entire functional PET/MRI scans. Resting-state fMRI and [18F]FDG-PET connectivity was evaluated using a graph-theory based correlation approach and compared on whole-brain level and for a default-mode network-like structure. Further, pharmacokinetic and tracer uptake influences on [18F]FDG-PET connectivity results were investigated based on the different PET protocols. By integrating simultaneous resting-state fMRI and dynamic [18F]FDG-PET measurements in the rat brain, we identified homotopic correlations between both modalities, suggesting an underlying synchrony between hemodynamic processes and glucose consumption. Furthermore, the presence of the prominent resting-state default-mode network-like structure was not only depicted on a functional scale but also from dynamic fluctuations of [18F]FDG. In addition, the present findings demonstrated strong pharmacokinetic and tracer uptake dependencies of [18F]FDG-PET connectivity outcomes. This study highlights the application of dynamic [18F]FDG-PET to study cognitive brain functions and to decode underlying brain networks in the resting-state. Thereby, PET-derived connectivity outcomes indicated strong dependencies on tracer application regimens and subsequent time-varying tracer pharmacokinetics.

PMID: 30981858 [PubMed - as supplied by publisher]

Abnormal coupling among spontaneous brain activity metrics and cognitive deficits in major depressive disorder.

Sun, 04/14/2019 - 20:59
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Abnormal coupling among spontaneous brain activity metrics and cognitive deficits in major depressive disorder.

J Affect Disord. 2019 Apr 08;252:74-83

Authors: Zhu J, Zhang Y, Zhang B, Yang Y, Wang Y, Zhang C, Zhao W, Zhu DM, Yu Y

Abstract
BACKGROUND: A variety of functional metrics derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been employed to explore spontaneous brain activity changes in major depressive disorder (MDD) and have enjoyed significant success in unraveling the neurobiological mechanisms underlying this disorder. However, it is unclear whether spatial and temporal coupling relationships among these rs-fMRI metrics are altered in MDD.
METHODS: 50 patients with MDD and 36 well-matched healthy controls underwent rs-fMRI scans. A dynamic analysis was applied to compute multiple frequently used metrics including fractional amplitude of low frequency fluctuations, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity. Kendall's W was used to calculate volume-wise (across voxels) and voxel-wise (across time windows) concordance among these metrics. Inter-group differences in the concordance and their associations with clinical and cognitive variables were tested.
RESULTS: Compared to healthy controls, patients with MDD showed decreased whole gray matter volume-wise concordance. Despite similar spatial distributions, quantitative comparison analysis revealed that MDD patients exhibited reduced voxel-wise concordance in multiple cortical and subcortical regions. Moreover, the lower concordance was associated with worse performances in prospective memory and sustained attention in the MDD group.
LIMITATIONS: The study design of fairly modest sample size did not allow us to perform a full analysis of the potential effects of medication and illness duration.
CONCLUSIONS: Our findings suggest that spatial and temporal decoupling of multiple resting-state brain activity metrics may help elucidate the neural mechanisms of cognitive deficits in depression.

PMID: 30981059 [PubMed - as supplied by publisher]