Cortico-striatal-thalamic network functional connectivity in hemiparkinsonism.
Neurobiol Aging. 2014 Jun 11;
Authors: Agosta F, Caso F, Stankovic I, Inuggi A, Petrovic I, Svetel M, Kostic VS, Filippi M
Cortico-striatal-thalamic network functional connectivity (FC) and its relationship with levodopa (L-dopa) were investigated in 69 patients with hemiparkinsonism (25 drug-naïve [n-PD] and 44 under stable/optimized dopaminergic treatment [t-PD]) and 27 controls. Relative to controls, n-PD patients showed an increased FC between the left and the right basal ganglia, and a decreased connectivity of the affected caudate nucleus and thalamus with the ipsilateral frontal and insular cortices. Compared with both controls and n-PD patients, t-PD patients showed a decreased FC among the striatal and thalamic regions, and an increased FC between the striatum and temporal cortex, and between the thalamus and several sensorimotor, parietal, temporal, and occipital regions. In both n-PD and t-PD, patients with more severe motor disability had an increased striatal and/or thalamic FC with temporal, parietal, occipital, and cerebellar regions. Cortico-striatal-thalamic functional abnormalities occur in patients with hemiparkinsonism, antecede the onset of the motor symptoms on the opposite body side and are modulated by L-dopa. In patients with hemiparkinsonism, L-dopa is likely to facilitate a compensation of functional abnormalities possibly through an increased thalamic FC.
PMID: 25004890 [PubMed - as supplied by publisher]
A review of the use of magnetic resonance imaging in Parkinson's disease.
Ther Adv Neurol Disord. 2014 Jul;7(4):206-20
Authors: Pyatigorskaya N, Gallea C, Garcia-Lorenzo D, Vidailhet M, Lehericy S
To date, the most frequently used Parkinson's disease (PD) biomarkers are the brain imaging measures of dopaminergic dysfunction using positron emission tomography and single photon emission computed tomography. However, major advances have occurred in the development of magnetic resonance imaging (MRI) biomarkers for PD in the past decade. Although conventional structural imaging remains normal in PD, advanced techniques have shown changes in the substantia nigra and the cortex. The most well-developed MRI markers in PD include diffusion imaging and iron load using T2/T2* relaxometry techniques. Other quantitative biomarkers such as susceptibility-weighted imaging for iron load, magnetization transfer and ultra-high-field MRI have shown great potential. More sophisticated techniques such as tractography and resting state functional connectivity give access to anatomical and functional connectivity changes in the brain, respectively. Brain perfusion can be assessed using non-contrast-agent techniques such as arterial spin labelling and spectroscopy gives access to metabolites concentrations. However, to date these techniques are not yet fully validated and standardized quantitative metrics for PD are still lacking. This review presents an overview of new structural, perfusion, metabolic and anatomo-functional connectivity biomarkers, their use in PD and their potential applications to improve the clinical diagnosis of Parkinsonian syndromes and the quality of clinical trials.
PMID: 25002908 [PubMed]
Structural and functional brain connectivity in presymptomatic familial frontotemporal dementia.
Neurology. 2014 Jul 8;83(2):e19-26
Authors: Dopper EG, Rombouts SA, Jiskoot LC, den Heijer T, de Graaf JR, de Koning I, Hammerschlag AR, Seelaar H, Seeley WW, Veer IM, van Buchem MA, Rizzu P, van Swieten JC
OBJECTIVE: We aimed to investigate whether cognitive deficits and structural and functional connectivity changes can be detected before symptom onset in a large cohort of carriers of MAPT (microtubule-associated protein tau) or GRN (progranulin) mutations.
METHODS: In this case-control study, 75 healthy individuals (aged 20-70 years) with 50% risk of frontotemporal dementia (FTD) underwent DNA screening, neuropsychological assessment, structural MRI, and fMRI. We used voxel-based morphometry and tract-based spatial statistics for voxel-wise analyses of gray matter volume and diffusion tensor imaging measures. Using resting-state fMRI scans, we assessed whole-brain functional connectivity to frontoinsular, anterior midcingulate, and posterior cingulate cortices.
RESULTS: Carriers (n = 39) and noncarriers (n = 36) had similar neuropsychological performance, except for lower Letter Digit Substitution Test scores in carriers. Worse performance on Stroop III, Rivermead Behavioral Memory Test, and Happé Cartoons correlated with higher age in carriers, but not controls. Reduced fractional anisotropy in the right uncinate fasciculus was found in carriers compared with controls. Reductions in functional connectivity between anterior midcingulate cortex and frontoinsula and several other brain regions were found in carriers compared with controls and correlated with higher age in carriers, but not controls. We found no significant differences or age correlations in posterior cingulate cortex connectivity. No differences in regional gray matter volume were found, except for a small cluster of higher volume in the precentral gyrus in carriers.
CONCLUSIONS: This study demonstrates that alterations in structural and functional connectivity develop before the first symptoms of FTD arise. These findings suggest that diffusion tensor imaging and resting-state fMRI may have the potential to become sensitive biomarkers for early FTD in future clinical trials.
PMID: 25002573 [PubMed - in process]
Abstinence from Cocaine and Sucrose Self-administration Reveals Altered Mesocorticolimbic Circuit Connectivity.
Brain Connect. 2014 Jul 7;
Authors: Lu H, Zou Q, Chefer S, Ross TJ, Vaupel DB, Guillem K, Rea W, Peoples LL, Stein EA
Previous preclinical studies have emphasized that drugs of abuse, via actions within and between mesocorticolimbic (MCL) regions, usurp learning and memory processes normally involved in the pursuit of naturally rewarding stimuli. To distinguish MCL circuit pathobiological neuroadaptations that accompany addiction from general learning processes associated with natural reward goal-directed behaviour, we trained two groups of rats to self-administer either cocaine (IV) or sucrose (orally) followed by an identically enforced 30 day abstinence period previously shown to induce behavioral self administration (SA) plasticity. A third group of sedentary animals served as a negative control group for general handling effects. We examined low frequency spontaneous fluctuations in the fMRI signal, known as resting-state functional connectivity (rsFC), as a measure of intrinsic neurobiological interactions between brain regions. Decreased rsFC was seen in the cocaine-SA compared with both sucrose-SA and housing control groups between prelimbic cortex (PrL) and entopeduncular nucleus and between nucleus accumbens core (AcbC) and dorsomedial prefrontal cortex (dmPFC). Moreover, individual differences in cocaine SA escalation predicted connectivity strength only in the Acb-dmPFC circuit. These data provide evidence of fronto-striatal plasticity across the addiction trajectory, are consistent with Acb-PFC hypoactivity seen in abstinent human drug addicts and suggest potential circuit level biomarkers that may inform therapeutic interventions. They further suggest that available data from cross sectional human studies may reflect the consequence of rather than a predispositional predecessor to their dependence.
PMID: 24999822 [PubMed - as supplied by publisher]
Altered Inter-Subregion Connectivity of the Default Mode Network in Relapsing Remitting Multiple Sclerosis: A Functional and Structural Connectivity Study.
PLoS One. 2014;9(7):e101198
Authors: Zhou F, Zhuang Y, Gong H, Wang B, Wang X, Chen Q, Wu L, Wan H
BACKGROUND AND PURPOSE: Little is known about the interactions between the default mode network (DMN) subregions in relapsing-remitting multiple sclerosis (RRMS). This study used diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI) to examine alterations of long white matter tracts in paired DMN subregions and their functional connectivity in RRMS patients.
METHODS: Twenty-four RRMS patients and 24 healthy subjects participated in this study. The fiber connections derived from DTI tractography and the temporal correlation coefficient derived from rs-fMRI were combined to examine the inter-subregion structural-functional connectivity (SC-FC) within the DMN and its correlations with clinical markers.
RESULTS: Compared with healthy subjects, the RRMS patients showed the following: 1) significantly decreased SC and increased FC in the pair-wise subregions; 2) two significant correlations in SC-FC coupling patterns, including the positive correlation between slightly increased FC value and long white matter tract damage in the PCC/PCUN-MPFC connection, and the negative correlations between significantly increased FC values and long white matter tract damage in the PCC/PCUN-bilateral mTL connections; 3) SC alterations [log(N track) of the PCC/PCUN-left IPL, RD value of the MPFC-left IPL, FA value of the PCC/PCUN-left mTL connections] correlated with EDSS, increases in the RD value of MPFC-left IPL connection was positively correlated to the MFIS; and decreases in the FA value of PCC/PCUN-right IPL connection was negatively correlated with the PASAT; 4) decreased SC (FA value of the MPFC-left IPL, track volume of the PCC/PCUN-MPFC, and log(N track) of PCC/PCUN-left mTL connections) was positively correlated with brain atrophy.
CONCLUSIONS: In the connections of paired DMN subregions, we observed decreased SC and increased FC in RRMS patients. The relationship between MS-related structural abnormalities and clinical markers suggests that the disruption of this long-distance "inter-subregion" connectivity (white matter) may significantly impact the integrity of the network's function.
PMID: 24999807 [PubMed - as supplied by publisher]
The spatial structure of resting state connectivity stability on the scale of minutes.
Front Neurosci. 2014;8:138
Authors: Gonzalez-Castillo J, Handwerker DA, Robinson ME, Hoy CW, Buchanan LC, Saad ZS, Bandettini PA
Resting state functional MRI (rsfMRI) connectivity patterns are not temporally stable, but fluctuate in time at scales shorter than most common rest scan durations (5-10 min). Consequently, connectivity patterns for two different portions of the same scan can differ drastically. To better characterize this temporal variability and understand how it is spatially distributed across the brain, we scanned subjects continuously for 60 min, at a temporal resolution of 1 s, while they rested inside the scanner. We then computed connectivity matrices between functionally-defined regions of interest for non-overlapping 1 min windows, and classified connections according to their strength, polarity, and variability. We found that the most stable connections correspond primarily to inter-hemispheric connections between left/right homologous ROIs. However, only 32% of all within-network connections were classified as most stable. This shows that resting state networks have some long-term stability, but confirms the flexible configuration of these networks, particularly those related to higher order cognitive functions. The most variable connections correspond primarily to inter-hemispheric, across-network connections between non-homologous regions in occipital and frontal cortex. Finally we found a series of connections with negative average correlation, but further analyses revealed that such average negative correlations may be related to the removal of CSF signals during pre-processing. Using the same dataset, we also evaluated how similarity of within-subject whole-brain connectivity matrices changes as a function of window duration (used here as a proxy for scan duration). Our results suggest scanning for a minimum of 10 min to optimize within-subject reproducibility of connectivity patterns across the entire brain, rather than a few predefined networks.
PMID: 24999315 [PubMed]
Does motion-related brain functional connectivity reflect both artifacts and genuine neural activity?
Neuroimage. 2014 Jul 3;
Authors: Pujol J, Macià D, Blanco-Hinojo L, Martínez-Vilavella G, Sunyer J, de la Torre R, Caixàs A, Martín-Santos R, Deus J, Harrison BJ
Imaging research on functional connectivity is uniquely contributing to characterize the functional organization of the human brain. Functional connectivity measurements, however, may be significantly influenced by head motion that occurs during image acquisition. The identification of how motion influences such measurements is therefore highly relevant to the interpretation of a study's results. We have mapped the effect of head motion on functional connectivity in six different populations representing a wide range of potential influences of motion on functional connectivity. Group-level voxel-wise maps of the correlation between a summary head motion measurement and functional connectivity degree were estimated in 80 young adults, 71 children, 53 older adults, 20 patients with Down syndrome, 24 with Prader-Willi syndrome and 20 with Williams syndrome. In highly compliant young adults, motion correlated with functional connectivity measurements showing a system-specific anatomy involving the sensorimotor cortex, visual areas and default mode network. Further characterization was strongly indicative of these changes expressing genuine neural activity related to motion, as opposed to pure motion artifact. In the populations with larger head motion, results were more indicative of widespread artifacts, but showing notably distinct spatial distribution patterns. Group-level regression of motion effects was efficient in removing both generalized changes and changes putatively related to neural activity. Overall, this study endorses a relatively simple approach for mapping distinct effects of head motion on functional connectivity. Importantly, our findings support the intriguing hypothesis that a component of motion-related changes may reflect system-specific neural activity.
PMID: 24999036 [PubMed - as supplied by publisher]
Fibromyalgia is associated with decreased connectivity between pain- and sensorimotor brain areas.
Brain Connect. 2014 Jul 6;
Authors: Flodin PD, Martinsen S, Löfgren M, Bileviciute-Ljungar I, Kosek E, Fransson P
Fibromyalgia (FM) is a syndrome characterized by chronic pain without known peripheral causes. Previously we have reported dysfunctional pain inhibitory mechanisms for FM patients during pain administration. In the current study we employed a seed correlation analysis (SCA), independent component analysis (ICA), and an analysis of fractional amplitude of low frequency fluctuations (fALFF) to study differences between a cohort of female fibromyalgia patients and an age- and sex matched healthy control group during a resting state condition. FM patients showed decreased connectivity between thalamus and premotor areas, between the right insula and primary sensorimotor areas, as well as between supramarginal and prefrontal areas. Individual sensitivity to painful pressure was associated with increased connectivity between pain related regions (e.g. insula and thalamus) and midline regions of the default mode network (including posterior cingulate cortex and medial prefrontal cortex) among patients and controls. However, neither ICA nor fALFF revealed any group differences. Our findings suggest that abnormal connectivity patterns between pain related regions and the remaining brain during rest reflect an impaired central mechanism of pain modulation in FM. Weaker coupling between pain regions and prefrontal- and sensorimotor areas might indicate a less efficient system level control of pain circuits. Moreover, our results show that multiple, complementary analytical approaches are valuable for obtaining a more comprehensive characterization of deviant resting state activity. In conclusion, our findings show that FM primarily is associated with decreased connectivity, e.g. between several pain related areas and sensorimotor regions, which could reflect a deficiency in pain regulation.
PMID: 24998297 [PubMed - as supplied by publisher]
A case for human systems neuroscience.
Neuroscience. 2014 Jul 2;
Authors: Gardner JL
Can the human brain itself serve as a model for a systems neuroscience approach to understanding the human brain? After all, how the brain is able to create the richness and complexity of human behavior is still largely mysterious. What better choice to study that complexity than to study it in humans? However, measurements of brain activity typically need to be made non-invasively which puts severe constraints on what can be learned about the internal workings of the brain. Our approach has been to use a combination of psychophysics in which we can use human behavioral flexibility to make quantitative measurements of behavior and link those through computational models to measurements of cortical activity through magnetic resonance imaging. In particular, we have tested various computational hypotheses about what neural mechanisms could account for behavioral enhancement with spatial attention (Pestilli et al., 2011). Resting both on quantitative measurements and considerations of what is known through animal models, we concluded that weighting of sensory signals by the magnitude of their response is a neural mechanism for efficient selection of sensory signals and consequent improvements in behavioral performance with attention. While animal models have many technical advantages over studying the brain in humans, we believe that human systems neuroscience should endeavor to validate, replicate and extend basic knowledge learned from animal model systems and thus form a bridge to understanding how the brain creates the complex and rich cognitive capacities of humans.
PMID: 24997268 [PubMed - as supplied by publisher]
Disrupted Intrinsic Networks Link Amyloid-β Pathology and Impaired Cognition in Prodromal Alzheimer's Disease.
Cereb Cortex. 2014 Jul 4;
Authors: Koch K, Myers NE, Göttler J, Pasquini L, Grimmer T, Förster S, Manoliu A, Neitzel J, Kurz A, Förstl H, Riedl V, Wohlschläger AM, Drzezga A, Sorg C
Amyloid-β pathology (Aβ) and impaired cognition characterize Alzheimer's disease (AD); however, neural mechanisms that link Aβ-pathology with impaired cognition are incompletely understood. Large-scale intrinsic connectivity networks (ICNs) are potential candidates for this link: Aβ-pathology affects specific networks in early AD, these networks show disrupted connectivity, and they process specific cognitive functions impaired in AD, like memory or attention. We hypothesized that, in AD, regional changes of ICNs, which persist across rest- and cognitive task-states, might link Aβ-pathology with impaired cognition via impaired intrinsic connectivity. Pittsburgh compound B (PiB)-positron emission tomography reflecting in vivo Aβ-pathology, resting-state fMRI, task-fMRI, and cognitive testing were used in patients with prodromal AD and healthy controls. In patients, default mode network's (DMN) functional connectivity (FC) was reduced in the medial parietal cortex during rest relative to healthy controls, relatively increased in the same region during an attention-demanding task, and associated with patients' cognitive impairment. Local PiB-uptake correlated negatively with DMN connectivity. Importantly, corresponding results were found for the right lateral parietal region of an attentional network. Finally, structural equation modeling confirmed a direct influence of DMN resting-state FC on the association between Aβ-pathology and cognitive impairment. Data provide evidence that disrupted intrinsic network connectivity links Aβ-pathology with cognitive impairment in early AD.
PMID: 24996404 [PubMed - as supplied by publisher]
Evaluating Dynamic Bivariate Correlations in Resting-state fMRI: A comparison study and a new approach.
Neuroimage. 2014 Jun 30;
Authors: Lindquist MA, Xu Y, Nebel MB, Caffo BS
To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-windows technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utilityfor studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data.
PMID: 24993894 [PubMed - as supplied by publisher]
Perfusion MRI Indexes Variability in the Functional Brain Effects of Theta-Burst Transcranial Magnetic Stimulation.
PLoS One. 2014;9(7):e101430
Authors: Gratton C, Lee TG, Nomura EM, D'Esposito M
Transcranial Magnetic Stimulation (TMS) is an important tool for testing causal relationships in cognitive neuroscience research. However, the efficacy of TMS can be variable across individuals and difficult to measure. This variability is especially a challenge when TMS is applied to regions without well-characterized behavioral effects, such as in studies using TMS on multi-modal areas in intrinsic networks. Here, we examined whether perfusion fMRI recordings of Cerebral Blood Flow (CBF), a quantitative measure sensitive to slow functional changes, reliably index variability in the effects of stimulation. Twenty-seven participants each completed four combined TMS-fMRI sessions during which both resting state Blood Oxygen Level Dependent (BOLD) and perfusion Arterial Spin Labeling (ASL) scans were recorded. In each session after the first baseline day, continuous theta-burst TMS (TBS) was applied to one of three locations: left dorsolateral prefrontal cortex (L dlPFC), left anterior insula/frontal operculum (L aI/fO), or left primary somatosensory cortex (L S1). The two frontal targets are components of intrinsic networks and L S1 was used as an experimental control. CBF changes were measured both before and after TMS on each day from a series of interleaved resting state and perfusion scans. Although TBS led to weak selective increases under the coil in CBF measurements across the group, individual subjects showed wide variability in their responses. TBS-induced changes in rCBF were related to TBS-induced changes in functional connectivity of the relevant intrinsic networks measured during separate resting-state BOLD scans. This relationship was selective: CBF and functional connectivity of these networks were not related before TBS or after TBS to the experimental control region (S1). Furthermore, subject groups with different directions of CBF change after TBS showed distinct modulations in the functional interactions of targeted networks. These results suggest that CBF is a marker of individual differences in the effects of TBS.
PMID: 24992641 [PubMed - as supplied by publisher]
Development of Thalamocortical Connectivity during Infancy and Its Cognitive Correlations.
J Neurosci. 2014 Jul 2;34(27):9067-75
Authors: Alcauter S, Lin W, Smith JK, Short SJ, Goldman BD, Reznick JS, Gilmore JH, Gao W
Although commonly viewed as a sensory information relay center, the thalamus has been increasingly recognized as an essential node in various higher-order cognitive circuits, and the underlying thalamocortical interaction mechanism has attracted increasing scientific interest. However, the development of thalamocortical connections and how such development relates to cognitive processes during the earliest stages of life remain largely unknown. Leveraging a large human pediatric sample (N = 143) with longitudinal resting-state fMRI scans and cognitive data collected during the first 2 years of life, we aimed to characterize the age-dependent development of thalamocortical connectivity patterns by examining the functional relationship between the thalamus and nine cortical functional networks and determine the correlation between thalamocortical connectivity and cognitive performance at ages 1 and 2 years. Our results revealed that the thalamus-sensorimotor and thalamus-salience connectivity networks were already present in neonates, whereas the thalamus-medial visual and thalamus-default mode network connectivity emerged later, at 1 year of age. More importantly, brain-behavior analyses based on the Mullen Early Learning Composite Score and visual-spatial working memory performance measured at 1 and 2 years of age highlighted significant correlations with the thalamus-salience network connectivity. These results provide new insights into the understudied early functional brain development process and shed light on the behavioral importance of the emerging thalamocortical connectivity during infancy.
PMID: 24990927 [PubMed - in process]
A study of structural and functional connectivity in early Alzheimer's disease using rest fMRI and diffusion tensor imaging.
Int J Geriatr Psychiatry. 2014 Jul 3;
Authors: Balachandar R, John JP, Saini J, Kumar KJ, Joshi H, Sadanand S, Aiyappan S, Sivakumar PT, Loganathan S, Varghese M, Bharath S
BACKGROUND/OBJECTIVES: Alzheimer's disease (AD) is a progressive neurodegenerative condition where in early diagnosis and interventions are key policy priorities in dementia services and research. We studied the functional and structural connectivity in mild AD to determine the nature of connectivity changes that coexist with neurocognitive deficits in the early stages of AD.
METHODS: Fifteen mild AD subjects and 15 cognitively healthy controls (CHc) matched for age and gender, underwent detailed neurocognitive assessment and magnetic resonance imaging (MRI) of resting state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI). Rest fMRI was analyzed using dual regression approach and DTI by voxel wise statistics.
RESULTS: Patients with mild AD had significantly lower functional connectivity (FC) within the default mode network and increased FC within the executive network. The mild AD group scored significantly lower in all domains of cognition compared with CHc. But fractional anisotropy did not significantly (p < 0.05) differ between the groups.
CONCLUSION: Resting state functional connectivity alterations are noted during initial stages of cognitive decline in AD, even when there are no significant white matter microstructural changes. Copyright © 2014 John Wiley & Sons, Ltd.
PMID: 24990445 [PubMed - as supplied by publisher]
Recognition memory is associated with altered resting-state functional connectivity in people at genetic risk for Alzheimer's disease.
Eur J Neurosci. 2014 Jul 3;
Authors: Matura S, Prvulovic D, Butz M, Hartmann D, Sepanski B, Linnemann K, Oertel-Knöchel V, Karakaya T, Fußer F, Pantel J, van de Ven V
The apolipoprotein E ε4 (ApoE ε4) allele not only represents the strongest single genetic risk factor for sporadic Alzheimer's disease, but also imposes independent effects on brain function in healthy individuals where it has been shown to promote subtle memory deficits and altered intrinsic functional brain network connectivity. Based on previous work showing a potential relevance of the default mode network (DMN) functional connectivity for episodic memory function, we hypothesized that the ApoE ε4 genotype would affect memory performance via modulation of the DMN. We assessed 63 healthy individuals (50-80 years old), of which 20 carried the ε4 allele. All participants underwent resting-state functional magnetic resonance imaging (fMRI), high-resolution 3D anatomical MRI imaging and neuropsychological assessment. Functional connectivity analysis of resting-state activity was performed with a predefined seed region located in the left posterior cingulate cortex (PCC), a core region of the DMN. ApoE ε4 carriers performed significantly poorer than non-carriers in wordlist recognition and cued recall. Furthermore, ε4 carriers showed increased connectivity relative to ε4 non-carriers between the PCC seed region and left-hemispheric middle temporal gyrus (MTG). There was a positive correlation between recognition memory scores and resting-state connectivity in the left MTG in ε4 carriers. These results can be interpreted as compensatory mechanisms strengthening the cross-links between DMN core areas and cortical areas involved in memory processing.
PMID: 24989884 [PubMed - as supplied by publisher]
Enhanced repertoire of brain dynamical states during the psychedelic experience.
Hum Brain Mapp. 2014 Jul 3;
Authors: Tagliazucchi E, Carhart-Harris R, Leech R, Nutt D, Chialvo DR
The study of rapid changes in brain dynamics and functional connectivity (FC) is of increasing interest in neuroimaging. Brain states departing from normal waking consciousness are expected to be accompanied by alterations in the aforementioned dynamics. In particular, the psychedelic experience produced by psilocybin (a substance found in "magic mushrooms") is characterized by unconstrained cognition and profound alterations in the perception of time, space and selfhood. Considering the spontaneous and subjective manifestation of these effects, we hypothesize that neural correlates of the psychedelic experience can be found in the dynamics and variability of spontaneous brain activity fluctuations and connectivity, measurable with functional Magnetic Resonance Imaging (fMRI). Fifteen healthy subjects were scanned before, during and after intravenous infusion of psilocybin and an inert placebo. Blood-Oxygen Level Dependent (BOLD) temporal variability was assessed computing the variance and total spectral power, resulting in increased signal variability bilaterally in the hippocampi and anterior cingulate cortex. Changes in BOLD signal spectral behavior (including spectral scaling exponents) affected exclusively higher brain systems such as the default mode, executive control, and dorsal attention networks. A novel framework enabled us to track different connectivity states explored by the brain during rest. This approach revealed a wider repertoire of connectivity states post-psilocybin than during control conditions. Together, the present results provide a comprehensive account of the effects of psilocybin on dynamical behavior in the human brain at a macroscopic level and may have implications for our understanding of the unconstrained, hyper-associative quality of consciousness in the psychedelic state. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
PMID: 24989126 [PubMed - as supplied by publisher]
Graphs of brain networks.
Alcohol Clin Exp Res. 2013 Nov;37(11):1813-5
Authors: Zahr NM
BACKGROUND: This commentary discusses the study by Telesford and colleagues in which they use network science to analyze resting state functional magnetic resonance imaging (rsfMRI) data collected in nonhuman primates.
METHODS: Their findings using a network science approach in nonhuman primates are considered in the context of results from human studies.
RESULTS: The network science approach to analyzing rsfMRI data from nonhuman primates yields results that are, for the most part, similar to results using alternative analyses methods in human studies.
CONCLUSIONS: Network science to analyze rsfMRI may promote a better understanding of the brain as a complex system.
PMID: 24164166 [PubMed - indexed for MEDLINE]
Dissociation of Regional Activity in Default Mode Network in Medication-Naive, First-Episode Somatization Disorder.
PLoS One. 2014;9(7):e99273
Authors: Su Q, Yao D, Jiang M, Liu F, Jiang J, Xu C, Dai Y, Yu M, Long L, Li H, Liu J, Zhang Z, Zhang J, Xiao C, Guo W
BACKGROUND: Patients with somatization disorder (SD) have altered neural activity in the brain regions of the default mode network (DMN). However, the regional alteration of the DMN in SD remains unknown. The present study was designed to investigate the regional alterations of the DMN in patients with SD at rest.
METHODS: Twenty-five first-episode, medication-naive patients with SD and 28 age-, sex-, education- matched healthy controls underwent a resting-state functional magnetic resonance imaging (fMRI) scan. The fractional amplitude of low-frequency fluctuations (fALFF) was applied to analyze the data.
RESULTS: Patients with SD showed a dissociation pattern of resting-state fALFF in the DMN, with increased fALFF in the bilateral superior medial prefrontal cortex (MPFC, BA8, 9) and decreased fALFF in the left precuneus (PCu, BA7). Furthermore, significantly positive correlation was observed between the z values of the voxels within the bilateral superior MPFC and somatization subscale scores of the Symptom Check List (SCL-90) in patients with SD.
CONCLUSIONS: Our findings indicate that there is a dissociation pattern of the anterior and posterior DMN in first-episode, treatment-naive patients with SD. The results provide new insight for the importance of the DMN in the pathophysiology of SD.
PMID: 24983962 [PubMed - as supplied by publisher]
Machine learning classification of resting state functional connectivity predicts smoking status.
Front Hum Neurosci. 2014;8:425
Authors: Pariyadath V, Stein EA, Ross TJ
Machine learning-based approaches are now able to examine functional magnetic resonance imaging data in a multivariate manner and extract features predictive of group membership. We applied support vector machine (SVM)-based classification to resting state functional connectivity (rsFC) data from nicotine-dependent smokers and healthy controls to identify brain-based features predictive of nicotine dependence. By employing a network-centered approach, we observed that within-network functional connectivity measures offered maximal information for predicting smoking status, as opposed to between-network connectivity, or the representativeness of each individual node with respect to its parent network. Further, our analysis suggests that connectivity measures within the executive control and frontoparietal networks are particularly informative in predicting smoking status. Our findings suggest that machine learning-based approaches to classifying rsFC data offer a valuable alternative technique to understanding large-scale differences in addiction-related neurobiology.
PMID: 24982629 [PubMed]
Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.
Front Neural Circuits. 2014;8:64
Authors: Dey S, Rao AR, Shah M
Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.
PMID: 24982615 [PubMed - in process]