Forum of resting-state fMRI

Dissociable Functional Networks of the Human Dentate Nucleus.

Cereb Cortex. 2013 Mar 19;

Authors: Bernard JA, Peltier SJ, Benson BL, Wiggins JL, Jaeggi SM, Buschkuehl M, Jonides J, Monk CS, Seidler RD

Abstract
The cerebellar dentate nucleus has been reported to project to motor and prefrontal cortical regions in nonhuman primates from 2 anatomically distinct areas. However, despite a wealth of human neuroimaging data implicating the cerebellum in motor and cognitive behaviors, evidence of dissociable motor and cognitive networks comprising the human dentate is lacking. To investigate the existence of these 2 networks in the human brain, we used resting-state functional connectivity magnetic resonance imaging. The resting-state fMRI signal was extracted from regions of interest in the dorsal and ventral dentate nucleus. We report a "motor" network involving the dorsal dentate, anterior regions of the cerebellum, and the precentral gyrus, and a "cognitive" network involving the ventral dentate, Crus I, and prefrontal cortex. The existence of these 2 distinct networks supports the notion that cerebellar involvement in cognitive tasks is above and beyond that associated with motor response components.

PMID: 23513045 [PubMed - as supplied by publisher]

Neurometrics of intrinsic connectivity networks at rest using fMRI: Retest reliability and cross-validation using a meta-level method.

Neuroimage. 2013 Mar 15;

Authors: Wisner KM, Atluri G, Lim KO, Macdonald AW

Abstract
Functional images of the resting brain can be empirically parsed into intrinsic connectivity networks (ICNs) which closely resemble patterns of evoked task-based brain activity and which have a biological and genetic basis. Recently, ICNs have become popular for investigating brain functioning and brain-behavior relationships. However, the replicability and neurometrics of these networks are only beginning to be reported. Using a meta-level independent component analysis (ICA), we produced ICNs from three data sets collected from two samples of healthy adults. The ICNs from our data sets demonstrated robust and independent replication of 12 intrinsic networks that reflected 17 canonical, task-based, brain networks. We found within-subject reliability of ICNs was modest overall, but ranged from poor to good, and that voxels with the highest measured connectivity rarely had the highest reliability. Networks associated with executive functions, visuospatial reasoning, motor coordination, speech and audition, default mode, vision, and interoception showed moderate to high group-level reproducibility and replicability. However, only the first four of these networks also showed fair or better within-subject reliability over time. Our findings highlight the replicability of ICN's across data sets, the range of within-subject neurometrics across different networks, and the shared characteristics between resting and task-based networks.

PMID: 23507379 [PubMed - as supplied by publisher]

Spontaneous EEG alpha oscillation interacts with positive and negative BOLD responses in the visual-auditory cortices and default-mode network.

Neuroimage. 2013 Mar 15;

Authors: Mayhew SD, Ostwald D, Porcaro C, Bagshaw AP

Abstract
The human brain is continually, dynamically active and spontaneous fluctuations in this activity play a functional role in affecting both behavioural and neuronal responses. However, the mechanisms through which this occurs remain poorly understood. Simultaneous EEG-fMRI is a promising technique to study how spontaneous activity modulates the brain's response to stimulation, as temporal indices of ongoing cortical excitability can be integrated with spatially localised evoked responses. Here we demonstrate an interaction between the ongoing power of the electrophysiological alpha oscillation and the magnitude of both positive (PBR) and negative (NBR) fMRI responses to two contrasts of visual checkerboard reversal. Furthermore, the amplitude of pre-stimulus EEG alpha-power significantly modulated the amplitude and shape of subsequent PBR and NBR to the visual stimulus. A nonlinear reduction of visual PBR and an enhancement of auditory NBR and default-mode network NBR were observed in trials preceded by high alpha-power. These modulated areas formed a functionally connected network during a separate resting-state recording. Our findings suggest that the "baseline" state of the brain exhibits considerable trial-to-trial variability which arises from fluctuations in the balance of cortical inhibition/excitation that are represented by respective increases/decreases in the power of the EEG alpha oscillation. The consequence of this spontaneous electrophysiological variability is modulated amplitudes of both PBR and NBR to stimulation. Fluctuations in alpha-power may subserve a functional relationship in the visual-auditory network, acting as mediator for both short and long-range cortical inhibition, the strength of which is represented in part by NBR.

PMID: 23507378 [PubMed - as supplied by publisher]

Functional network architecture of reading-related regions across development.

Brain Lang. 2013 Mar 15;

Authors: Vogel AC, Church JA, Power JD, Miezin FM, Petersen SE, Schlaggar BL

Abstract
Reading requires coordinated neural processing across a large number of brain regions. Studying relationships between reading-related regions informs the specificity of information processing performed in each region. Here, regions of interest were defined from a meta-analysis of reading studies, including a developmental study. Relationships between regions were defined as temporal correlations in spontaneous fMRI signal; i.e., resting state functional connectivity MRI (RSFC). Graph theory based network analysis defined the community structure of the "reading-related" regions. Regions sorted into previously defined communities, such as the fronto-parietal and cingulo-opercular control networks, and the default mode network. This structure was similar in children, and no apparent "reading" community was defined in any age group. These results argue against regions, or sets of regions, being specific or preferential for reading, instead indicating that regions used in reading are also used in a number of other tasks.

PMID: 23506969 [PubMed - as supplied by publisher]

Inferring functional connectivity in MRI using Bayesian network structure learning with a modified PC algorithm.

Neuroimage. 2013 Mar 7;

Authors: Iyer S, Shafran I, Grayson D, Gates K, Nigg J, Fair D

Abstract
Resting state functional connectivity MRI (rs-fcMRI) is a popular technique used to gauge the functional relatedness between regions in the brain for typical and special populations. Most of the work to date determines this relationship by using Pearson's correlation on BOLD fMRI timeseries. However, it has been recognized that there are at least two key limitations to this method. First, it is not possible to resolve the direct and indirect connections/influences. Second, the direction of information flow between the regions cannot be differentiated. In the current paper, we follow-up on recent work by Smith et al (2011), and apply a Bayesian approach called the PC algorithm to both simulated data and empirical data to determine whether these two factors can be discerned with group average, as opposed to single subject, functional connectivity data. When applied on simulated individual subjects, the algorithm performs well determining indirect and direct connection but fails in determining directionality. However, when applied at group level, PC algorithm gives strong results for both indirect and direct connections and the direction of information flow. Applying the algorithm on empirical data, using a diffusion-weighted imaging (DWI) structural connectivity matrix as the baseline, the PC algorithm outperformed the direct correlations. We conclude that, under certain conditions, the PC algorithm leads to an improved estimate of brain network structure compared to the traditional connectivity analysis based on correlations.

PMID: 23501054 [PubMed - as supplied by publisher]

A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics.

Neuroimage. 2013 Mar 14;

Authors: Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Martino AD, Li Q, Zuo XN, Castellanos FX, Milham MP

Abstract
Functional connectomics is one of the most rapidly expanding areas of neuroimaging research. Yet, concerns remain regarding the use of resting-state fMRI (R-fMRI) to characterize inter-individual variation in the functional connectome. In particular, recent findings that "micro" head movements can introduce artifactual inter-individual and group-related differences in R-fMRI metrics have raised concerns. Here, we first build on prior demonstrations of regional variation in the magnitude of framewise displacements associated with a given head movement, by providing a comprehensive voxel-based examination of the impact of motion on the BOLD signal (i.e., motion-BOLD relationships). Positive motion-BOLD relationships were detected in primary and supplementary motor areas, particularly in low motion datasets. Negative motion-BOLD relationships were most prominent in prefrontal regions, and expanded throughout the brain in high motion datasets (e.g., children). Scrubbing of volumes with FD>0.2 effectively removed negative but not positive correlations; these findings suggest that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact. We also examined the ability of motion correction strategies to eliminate artifactual differences related to motion among individuals and between groups for a broad array of voxel-wise R-fMRI metrics. Residual relationships between motion and the examined R-fMRI metrics remained for all correction approaches, underscoring the need to covary motion effects at the group-level. Notably, global signal regression reduced relationships between motion and inter-individual differences in correlation-based R-fMRI metrics; Z-standardization (mean-centering and variance normalization) of subject-level maps for R-fMRI metrics prior to group-level analyses demonstrated similar advantages. Finally, our test-retest (TRT) analyses revealed significant motion effects on TRT reliability for R-fMRI metrics. Generally, motion compromised reliability of R-fMRI metrics, with the exception of those based on frequency characteristics - particularly, amplitude of low frequency fluctuations (ALFF). The implications of our findings for decision-making regarding the assessment and correction of motion are discussed, as are insights into potential differences among volume-based metrics of motion.

PMID: 23499792 [PubMed - as supplied by publisher]

GABAergic neuroactive steroids and resting-state functional connectivity in postpartum depression: A preliminary study.

J Psychiatr Res. 2013 Mar 14;

Authors: Deligiannidis KM, Sikoglu EM, Shaffer SA, Frederick B, Svenson AE, Kopoyan A, Kosma CA, Rothschild AJ, Moore CM

Abstract
Postpartum depression (PPD) affects up to 1 in 8 women. The early postpartum period is characterized by a downward physiological shift from relatively elevated levels of sex steroids during pregnancy to diminished levels after parturition. Sex steroids influence functional brain connectivity in healthy non-puerperal subjects. This study tests the hypothesis that PPD is associated with attenuation of resting-state functional connectivity (rs-fc) within corticolimbic regions implicated in depression and alterations in neuroactive steroid concentrations as compared to healthy postpartum women. Subjects (n = 32) were prospectively evaluated during pregnancy and in the postpartum with repeated plasma neuroactive steroid measurements and mood and psychosocial assessments. Healthy comparison subjects (HCS) and medication-free subjects with unipolar PPD (PPD) were examined using functional magnetic resonance imaging (fMRI) within 9 weeks of delivery. We performed rs-fc analysis with seeds placed in the anterior cingulate cortex (ACC), and bilateral amygdala (AMYG), hippocampi (HIPP) and dorsolateral prefrontal cortices (DLPFCs). Postpartum rs-fc and perinatal neuroactive steroid plasma concentrations, quantified by liquid chromatography/mass spectrometry, were compared between groups. PPD subjects showed attenuation of connectivity for each of the tested regions (i.e. ACC, AMYG, HIPP and DLPFC) and between corticocortical and corticolimbic regions vs. HCS. Perinatal concentrations of pregnanolone, allopregnanolone and pregnenolone were not different between groups. This is the first report of a disruption in the rs-fc patterns in medication-free subjects with PPD. This disruption may contribute to the development of PPD, at a time of falling neuroactive steroid concentrations.

PMID: 23499388 [PubMed - as supplied by publisher]

Gaussian Mixture Model-based noise reduction in resting state fMRI data.

J Neurosci Methods. 2013 Mar 7;

Authors: Garg G, Prasad G, Coyle D

Abstract
Neuroimaging the default mode network (DMN) in resting state has been of significant interest for investigating pathological conditions as resting state data are less affected by the variability in the subject's performance and movement-related artefacts in the electromagnetic field which are often issues in event-related activation experiments. An issue to be considered with resting state data is the very low amplitude of the activation patterns which are not induced by any stimulation or stimulus paradigm. Though, many studies have suggested that amplitude of low frequency fluctuation (ALFF) analysis is suitable for resting state functional magnetic resonance imaging (fMRI) data analysis, the low signal-to-noise-ratio (SNR) of acquired neuroimaging data poses a significant problem in the accurate analysis of the same. In this work, a Gaussian Mixture Model (GMM) method to suppress the noise during data pre-processing before ALFF is applied (GMM-ALFF) is proposed, where the optimum numbers of Gaussian distributions are fitted to the data using the Bayesian information criterion (BIC). The method has been tested with artificial data as well as real resting state fMRI data collected from Alzheimer's disease patients with different levels of added noise. Improvement of as much as 40% for artificial datasets and at least 3% for real datasets (p<0.05) have been observed when applying the proposed GMM approach prior to the analysis with the existing ALFF approach.

PMID: 23499197 [PubMed - as supplied by publisher]

Frequency shifts in the anterior default mode network and the salience network in chronic pain disorder.

BMC Psychiatry. 2013 Mar 13;13(1):84

Authors: Otti A, Guendel H, Wohlschläger A, Zimmer C, Noll-Hussong M

Abstract
BACKGROUND: Recent functional imaging studies on chronic pain of various organic etiologies have shown significant alterations in both the spatial and the temporal dimensions of the functional connectivity of the human brain in its resting state. However, it remains unclear whether similar changes in intrinsic connectivity networks (ICNs) also occur in patients with chronic pain disorder, defined as persistent, medically unexplained pain. METHODS: We compared 21 patients who suffered from chronic pain disorder with 19 age- and gender-matched controls using 3T-fMRI. All neuroimaging data were analyzed using both independent component analysis (ICA) and power spectra analysis. RESULTS: In patients suffering from chronic pain disorder, the fronto-insular 'salience' network (FIN) and the anterior default mode network (aDMN) predominantly oscillated at higher frequencies (0.20 - 0.24 Hz), whereas no significant differences were observed in the posterior DMN (pDMN) and the sensorimotor network (SMN). CONCLUSIONS: Our results indicate that chronic pain disorder may be a self-sustaining and endogenous mental process that affects temporal organization in terms of a frequency shift in the rhythmical dynamics of cortical networks associated with emotional homeostasis and introspection.

PMID: 23497482 [PubMed - as supplied by publisher]

Resting state functional connectivity of five neural networks in bipolar disorder and schizophrenia.

J Affect Disord. 2013 Mar 9;

Authors: Mamah D, Barch DM, Repovš G

Abstract
BACKGROUND: Bipolar disorder (BPD) and schizophrenia (SCZ) share clinical characteristics and genetic contributions. Functional dysconnectivity across various brain networks has been reported to contribute to the pathophysiology of both SCZ and BPD. However, research examining resting-state neural network dysfunction across multiple networks to understand the relationship between these two disorders is lacking. METHODS: We conducted a resting-state functional connectivity fMRI study of 35 BPD and 25 SCZ patients, and 33 controls. Using previously defined regions-of-interest, we computed the mean connectivity within and between five neural networks: default mode (DM), fronto-parietal (FP), cingulo-opercular (CO), cerebellar (CER), and salience (SAL). Repeated measures ANOVAs were used to compare groups, adjusting false discovery rate to control for multiple comparisons. The relationship of connectivity with the SANS/SAPS, vocabulary and matrix reasoning was investigated using hierarchical linear regression analyses. RESULTS: Decreased within-network connectivity was only found for the CO network in BPD. Across groups, connectivity was decreased between CO-CER (p<0.001), to a larger degree in SCZ than in BPD. In SCZ, there was also decreased connectivity in CO-SAL, FP-CO, and FP-CER, while BPD showed decreased CER-SAL connectivity. Disorganization symptoms were predicted by connectivity between CO-CER and CER-SAL. DISCUSSION: Our findings indicate dysfunction in the connections between networks involved in cognitive and emotional processing in the pathophysiology of BPD and SCZ. Both similarities and differences in connectivity were observed across disorders. Further studies are required to investigate relationships of neural networks to more diverse clinical and cognitive domains underlying psychiatric disorders.

PMID: 23489402 [PubMed - as supplied by publisher]

Intensive reasoning training alters patterns of brain connectivity at rest.

J Neurosci. 2013 Mar 13;33(11):4796-803

Authors: Mackey AP, Miller Singley AT, Bunge SA

Abstract
Patterns of correlated activity among brain regions reflect functionally relevant networks that are widely assumed to be stable over time. We hypothesized that if these correlations reflect the prior history of coactivation of brain regions, then a marked shift in cognition could alter the strength of coupling between these regions. We sought to test whether intensive reasoning training in humans would result in tighter coupling among regions in the lateral frontoparietal network, as measured with resting-state fMRI (rs-fMRI). Rather than designing an artificial training program, we studied individuals who were preparing for a standardized test that places heavy demands on relational reasoning, the Law School Admissions Test (LSAT). LSAT questions require test takers to group or sequence items according to a set of complex rules. We recruited young adults who were enrolled in an LSAT course that offers 70 h of reasoning instruction (n = 25), and age- and IQ-matched controls intending to take the LSAT in the future (n = 24). rs-fMRI data were collected for all subjects during two scanning sessions separated by 90 d. An analysis of pairwise correlations between brain regions implicated in reasoning showed that fronto-parietal connections were strengthened, along with parietal-striatal connections. These findings provide strong evidence for neural plasticity at the level of large-scale networks supporting high-level cognition.

PMID: 23486950 [PubMed - in process]

Intrinsic variability in the human response to pain is assembled from multiple, dynamic brain processes.

Neuroimage. 2013 Feb 24;

Authors: Mayhew SD, Hylands-White N, Porcaro C, Derbyshire SW, Bagshaw AP

Abstract
The stimulus-evoked response is the principle measure used to elucidate the timing and spatial location of human brain activity. Brain and behavioural responses to pain are influenced by multiple intrinsic and extrinsic factors and display considerable, natural trial-by-trial variability. However, because the neuronal sources of this variability are poorly understood the functional information it contains is under-exploited for understanding the relationship between brain function and behaviour. We recorded simultaneous EEG-fMRI during rest and noxious thermal stimulation to characterise the relationship between natural fluctuations in behavioural pain-ratings, the spatiotemporal dynamics of brain network responses and intrinsic connectivity. We demonstrate that fMRI response variability in the pain network is: dependent upon its resting-state functional connectivity; modulated by behaviour; and correlated with EEG evoked-potential amplitude. The pre-stimulus default-mode network (DMN) fMRI signal predicts the subsequent magnitude of pain ratings, evoked-potentials and pain network BOLD responses. Additionally, the power of the ongoing EEG alpha oscillation, an index of cortical excitability, modulates the DMN fMRI response to pain. The complex interaction between alpha-power, DMN activity and both the behavioural report of pain and the brain's response to pain demonstrates the neurobiological significance of trial-by-trial variability. Furthermore, we show that multiple, interconnected factors contribute to both the brain's response to stimulation and the psychophysiological emergence of the subjective experience of pain.

PMID: 23485593 [PubMed - as supplied by publisher]

Altered Fronto-Striatal and Fronto-Cerebellar Circuits in Heroin-Dependent Individuals: A Resting-State fMRI Study.

PLoS One. 2013;8(3):e58098

Authors: Wang Y, Zhu J, Li Q, Li W, Wu N, Zheng Y, Chang H, Chen J, Wang W

Abstract
BACKGROUND: The formation of compulsive pattern of drug use is related to abnormal regional neural activity and functional reorganization in the heroin addicts' brain, but the relationship between heroin-use-induced disrupted local neural activity and its functional organization pattern in resting-state is unknown.
METHODOLOGYPRINCIPAL FINDINGS: With fMRI data acquired during resting state from 17 male heroin dependent individuals (HD) and 15 matched normal controls (NC), we analyzed the changes of amplitude of low frequency fluctuation (ALFF) in brain areas, and its relationship with history of heroin use. Then we investigated the addiction related alteration in functional connectivity of the brain regions with changed ALFF using seed-based correlation analysis. Compared with NC, the ALFF of HD was obviously decreased in the right caudate, right dorsal anterior cingulate cortex (dACC), right superior medial frontal cortex and increased in the bilateral cerebellum, left superior temporal gyrus and left superior occipital gyrus. Of the six regions, only the ALFF value of right caudate had a negative correlation with heroin use. Setting the six regions as "seeds", we found the functional connectivity between the right caudate and dorsolateral prefrontal cortex (dlPFC) was reduced but that between the right caudate and cerebellum was enhanced. Besides, an abnormal lateral PFC-dACC connection was also observed in HD.
CONCLUSIONS: The observations of dysfunction of fronto-striatal and fronto-cerebellar circuit in HD implicate an altered balance between local neuronal assemblies activity and their integrated network organization pattern which may be involved in the process from voluntary to habitual and compulsive drug use.

PMID: 23483978 [PubMed - as supplied by publisher]

Altered Default Mode Network Connectivity in Older Adults with Cognitive Complaints and Amnestic Mild Cognitive Impairment.

J Alzheimers Dis. 2013 Mar 12;

Authors: Wang Y, Risacher SL, West JD, McDonald BC, Magee TR, Farlow MR, Gao S, O'Neill DP, Saykin AJ

Abstract
Default mode network (DMN) disruption has been reported in Alzheimer's disease (AD), yet the specific pattern of altered connectivity over the course of prodromal AD remains to be characterized. The aim of this study was to assess DMN connectivity in older adults with informant-verified cognitive complaints (CC) but normal neuropsychological performance compared to individuals with mild cognitive impairment (MCI) and healthy controls (HC). DMN maps were derived from resting-state fMRI using independent component analysis. Group comparisons of DMN connectivity were performed between older adults with MCI (n = 18), CC (n = 23), and HC (n = 16). Both CC and MCI showed decreased DMN connectivity in the right hippocampus compared to HC, with the CC group showing greater connectivity than MCI. These differences survived atrophy correction and correlated with cognitive performance. DMN connectivity appears sensitive to early prodromal neurodegenerative changes associated with AD, notably including pre-MCI individuals with cognitive complaints.

PMID: 23481685 [PubMed - as supplied by publisher]

Infraslow LFP correlates to resting-state fMRI BOLD signals.

Neuroimage. 2013 Feb 26;

Authors: Pan WJ, Thompson GJ, Magnuson ME, Jaeger D, Keilholz S

Abstract
The slow fluctuations of the blood-oxygenation-level dependent (BOLD) signal in resting-state fMRI are widely utilized as a surrogate marker of ongoing neural activity. Spontaneous neural activity includes a broad range of frequencies, from infraslow (<0.5Hz) fluctuations to fast action potentials. Recent studies have demonstrated a correlative relationship between the BOLD fluctuations and power modulations of the local field potential (LFP), particularly in the gamma band. However, the relationship between the BOLD signal and the infraslow components of the LFP, which are directly comparable in frequency to the BOLD fluctuations, has not been directly investigated. Here we report a first examination of the temporal relation between the resting-state BOLD signal and infraslow LFPs using simultaneous fMRI and full-band LFP recording in rat. The spontaneous BOLD signal at the recording sites exhibited significant localized correlation with the infraslow LFP signals as well as with the slow power modulations of higher-frequency LFPs (1-100Hz) at a delay comparable to the hemodynamic response time under anesthesia. Infraslow electrical activity has been postulated to play a role in attentional processes, and the findings reported here suggest that infraslow LFP coordination may share a mechanism with the large-scale BOLD-based networks previously implicated in task performance, providing new insight into the mechanisms contributing to the resting state fMRI signal.

PMID: 23481462 [PubMed - as supplied by publisher]

Dysfunctional neural networks associated with impaired social interactions in early psychosis: an ICA analysis.

Brain Imaging Behav. 2013 Mar 12;

Authors: Mazza M, Catalucci A, Pino MC, Giusti L, Nigri A, Pollice R, Roncone R, Casacchia M, Gallucci M

Abstract
The "default mode", or baseline of brain function is a topic of great interest in schizophrenia research. Recent neuroimaging studies report that the symptoms of chronic schizophrenia subjects are associated with temporal frequency alterations as well as with the disruption of local spatial patterns in the default mode network (DMN). Previous studies both on chronic and medicated subjects with psychosis suffered from limitations; on this basis, it was hypothesized that the default mode network showed abnormal activation and connectivity in young and neuroleptic-naïve patients with first-episode psychosis. This study investigated emotional responses to pleasant and unpleasant/disgusting visual stimuli by a resting-state analysis of fMRI-data from 12 untreated first-episode psychosis patients with prevalently negative symptomatology versus 12 healthy subjects. We chose this experimental task to explore the functional link between default mode network and hedonic processing which has been proposed as a marker of cerebral dysfunction in psychotic disorder and implicated in its pathophysiology. Independent Component Analysis (ICA) was used to identify the default mode component. Both healthy and first-episode subjects showed significant spatial differences in the default mode network. In first-episode subjects, medial frontal hypoactivity and cerebellar hyperactivity were correlated with the severity of negative symptoms.

PMID: 23479058 [PubMed - as supplied by publisher]

Consumption of Fermented Milk Product with Probiotic Modulates Brain Activity.

Gastroenterology. 2013 Mar 5;

Authors: Tillisch K, Labus J, Kilpatrick L, Jiang Z, Stains J, Ebrat B, Guyonnet D, Legrain-Raspaud S, Trotin B, Naliboff B, Mayer EA

Abstract
BACKGROUND & AIMS: Changes in gut microbiota have been reported to alter signaling mechanisms, emotional behavior, and visceral nociceptive reflexes in rodents. However, alteration of the intestinal microbiota with antibiotics or probiotics has not been shown to produce these changes in humans. We investigated whether consumption of a fermented milk product with probiotic (FMPP) for 4 weeks by healthy women altered brain intrinsic connectivity or responses to emotional attention tasks. METHODS: Healthy women with no gastrointestinal or psychiatric symptoms were randomly assigned to groups given FMPP (n=12), a non-fermented milk product (n=11, controls), or no intervention (n=13) twice daily for 4 weeks. The FMPP contained Bifidobacterium animalis subsp. Lactis, Streptococcus thermophiles, Lactobacillus bulgaricus, and Lactococcus lactis subsp. Lactis. Participants underwent functional magnetic resonance imaging (fMRI) before and after the intervention, to measure brain response to an emotional faces attention task and resting brain activity. Multivariate and region of interest analyses were performed. RESULTS: FMPP intake was associated with reduced task-related response of a distributed functional network (49% crossblock covariance; P =.004) containing affective, viscerosensory, and somatosensory cortices. Alterations in intrinsic activity of resting brain indicated that ingestion of FMPP was associated with changes in midbrain connectivity, which could explain the observed differences in activity during the task. CONCLUSIONS: Four weeks intake of a FMPP by healthy women affected activity of brain regions that control central processing of emotion and sensation.

PMID: 23474283 [PubMed - as supplied by publisher]

Resting-state brain activity in major depressive disorder patients and their siblings.

J Affect Disord. 2013 Mar 6;

Authors: Liu CH, Ma X, Wu X, Fan TT, Zhang Y, Zhou FC, Li LJ, Li F, Tie CL, Li SF, Zhang D, Zhou Z, Dong J, Wang YJ, Yao L, Wang CY

Abstract
BACKGROUND: Major depressive disorder (MDD) is a highly heritable psychiatric disease, and the existing literature is not robust enough to allow us to evaluate whether MDD-associated biomarkers are state-independent heritable endophenotypes or state markers related to depression per se. METHODS: Twenty two patients diagnosed with MDD, 22 siblings, as well as 26 gender-, age-, and education-matched healthy subjects, participated in the resting-state functional magnetic resonance imaging (fMRI) analysis. We compared the differences in the fractional amplitude of low-frequency fluctuation (fALFF) among the three groups and investigated the correlation between clinical measurements and fALFF in the regions displaying significant group differences. RESULTS: Both the MDD and siblings groups showed an increased fALFF in the left middle frontal gyrus (l-MFG, Brodmann Area, BA 10) compared to the healthy controls. The MDD groups demonstrated an increased fALFF in the right dorsal medial frontal gyrus (r-DMFG, BA 9) and a decreased fALFF in the bilateral lingual gyrus relative to siblings and healthy controls. LIMITATIONS: Medication effects, an inability to control subjects' thoughts during imaging. CONCLUSIONS: Our results suggest that the dysfunction in the l-MFG may represent an imaging endophenotype which may indicate a risk for MDD. The r-DMFG may play a critical role in depressive symptomatology and may reveal therapeutic target for MDD.

PMID: 23474094 [PubMed - as supplied by publisher]

[Effects of acupuncture on default mode network images of chronic sciatica patients in the resting network state].

Zhongguo Zhong Xi Yi Jie He Za Zhi. 2012 Dec;32(12):1624-7

Authors: Li J, Dong JC, Yue JJ

Abstract
OBJECTIVE: To observe the functional magnetic resonance imaging (fMRI) data changes of default mode network (DMN) in chronic sciatica patients in the resting network state treated by acupuncture, and to study the correlation between DMN and the consisting effects after acupuncture analgesia.
METHODS: Weizhong (BL40) and Huantiao (GB30) of the patients' lower limbs were selected as the main points to acupuncture for ten times. The whole brain was scanned using fMRI. The independent component analysis (ICA) was adopted to get DMN information. The brain DMN function link was analyzed in the two groups of subjects.
RESULTS: The DMN images were obtained in all subjects after DMN fMRI data processing. The main DMN differences between the sciatica patients group and the healthy control group were demonstrated as decreased activities of DLPFC and anterior cingulate cortex (ACC). After acupuncture, activities of these regions basically recovered to normal. The DMN of healthy volunteers shown by fMRI data in the RNS mainly existed in the precunes, BA7, BA10, and ACC.
CONCLUSION: MRI images of DMN in the RNS could reflect chronic pain, which was suitable for studies on the effects after acupuncture analgesia.

PMID: 23469600 [PubMed - in process]

Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification.

Brain Struct Funct. 2013 Mar 7;

Authors: Wee CY, Yap PT, Zhang D, Wang L, Shen D

Abstract
Emergence of advanced network analysis techniques utilizing resting-state functional magnetic resonance imaging (R-fMRI) has enabled a more comprehensive understanding of neurological disorders at a whole-brain level. However, inferring brain connectivity from R-fMRI is a challenging task, particularly when the ultimate goal is to achieve good control-patient classification performance, owing to perplexing noise effects, curse of dimensionality, and inter-subject variability. Incorporating sparsity into connectivity modeling may be a possible solution to partially remedy this problem since most biological networks are intrinsically sparse. Nevertheless, sparsity constraint, when applied at an individual level, will inevitably cause inter-subject variability and hence degrade classification performance. To this end, we formulate the R-fMRI time series of each region of interest (ROI) as a linear representation of time series of other ROIs to infer sparse connectivity networks that are topologically identical across individuals. This formulation allows simultaneous selection of a common set of ROIs across subjects so that their linear combination is best in estimating the time series of the considered ROI. Specifically, l 1-norm is imposed on each subject to filter out spurious or insignificant connections to produce sparse networks. A group-constraint is hence imposed via multi-task learning using a l 2-norm to encourage consistent non-zero connections across subjects. This group-constraint is crucial since the network topology is identical for all subjects while still preserving individual information via different connectivity values. We validated the proposed modeling in mild cognitive impairment identification and promising results achieved demonstrate its superiority in disease characterization, particularly greater sensitivity to early stage brain pathologies. The inferred group-constrained sparse network is found to be biologically plausible and is highly associated with the disease-associated anatomical anomalies. Furthermore, our proposed approach achieved similar classification performance when finer atlas was used to parcellate the brain space.

PMID: 23468090 [PubMed - as supplied by publisher]