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1.  Neuroimaging for the Evaluation of Chronic Headaches 
Executive Summary
Objective
The objectives of this evidence based review are:
i) To determine the effectiveness of computed tomography (CT) and magnetic resonance imaging (MRI) scans in the evaluation of persons with a chronic headache and a normal neurological examination.
ii) To determine the comparative effectiveness of CT and MRI scans for detecting significant intracranial abnormalities in persons with chronic headache and a normal neurological exam.
iii) To determine the budget impact of CT and MRI scans for persons with a chronic headache and a normal neurological exam.
Clinical Need: Condition and Target Population
Headaches disorders are generally classified as either primary or secondary with further sub-classifications into specific headache types. Primary headaches are those not caused by a disease or medical condition and include i) tension-type headache, ii) migraine, iii) cluster headache and, iv) other primary headaches, such as hemicrania continua and new daily persistent headache. Secondary headaches include those headaches caused by an underlying medical condition. While primary headaches disorders are far more frequent than secondary headache disorders, there is an urge to carry out neuroimaging studies (CT and/or MRI scans) out of fear of missing uncommon secondary causes and often to relieve patient anxiety.
Tension type headaches are the most common primary headache disorder and migraines are the most common severe primary headache disorder. Cluster headaches are a type of trigeminal autonomic cephalalgia and are less common than migraines and tension type headaches. Chronic headaches are defined as headaches present for at least 3 months and lasting greater than or equal to 15 days per month. The International Classification of Headache Disorders states that for most secondary headaches the characteristics of the headache are poorly described in the literature and for those headache disorders where it is well described there are few diagnostically important features.
The global prevalence of headache in general in the adult population is estimated at 46%, for tension-type headache it is 42% and 11% for migraine headache. The estimated prevalence of cluster headaches is 0.1% or 1 in 1000 persons. The prevalence of chronic daily headache is estimated at 3%.
Neuroimaging
Computed Tomography
Computed tomography (CT) is a medical imaging technique used to aid diagnosis and to guide interventional and therapeutic procedures. It allows rapid acquisition of high-resolution three-dimensional images, providing radiologists and other physicians with cross-sectional views of a person’s anatomy. CT scanning poses risk of radiation exposure. The radiation exposure from a conventional CT scanner may emit effective doses of 2-4mSv for a typical head CT.
Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is a medical imaging technique used to aid diagnosis but unlike CT it does not use ionizing radiation. Instead, it uses a strong magnetic field to image a person’s anatomy. Compared to CT, MRI can provide increased contrast between the soft tissues of the body. Because of the persistent magnetic field, extra care is required in the magnetic resonance environment to ensure that injury or harm does not come to any personnel while in the environment.
Research Questions
What is the effectiveness of CT and MRI scanning in the evaluation of persons with a chronic headache and a normal neurological examination?
What is the comparative effectiveness of CT and MRI scanning for detecting significant intracranial abnormality in persons with chronic headache and a normal neurological exam?
What is the budget impact of CT and MRI scans for persons with a chronic headache and a normal neurological exam.
Research Methods
Literature Search
Search Strategy
A literature search was performed on February 18, 2010 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published from January, 2005 to February, 2010. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria full-text articles were obtained. Reference lists were also examined for any additional relevant studies not identified through the search. Articles with an unknown eligibility were reviewed with a second clinical epidemiologist and then a group of epidemiologists until consensus was established.
Inclusion Criteria
Systematic reviews, randomized controlled trials, observational studies
Outpatient adult population with chronic headache and normal neurological exam
Studies reporting likelihood ratio of clinical variables for a significant intracranial abnormality
English language studies
2005-present
Exclusion Criteria
Studies which report outcomes for persons with seizures, focal symptoms, recent/new onset headache, change in presentation, thunderclap headache, and headache due to trauma
Persons with abnormal neurological examination
Case reports
Outcomes of Interest
Primary Outcome
Probability for intracranial abnormality
Secondary Outcome
Patient relief from anxiety
System service use
System costs
Detection rates for significant abnormalities in MRI and CT scans
Summary of Findings
Effectiveness
One systematic review, 1 small RCT, and 1 observational study met the inclusion and exclusion criteria. The systematic review completed by Detsky, et al. reported the likelihood ratios of specific clinical variables to predict significant intracranial abnormalities. The RCT completed by Howard et al., evaluated whether neuroimaging persons with chronic headache increased or reduced patient anxiety. The prospective observational study by Sempere et al., provided evidence for the pre-test probability of intracranial abnormalities in persons with chronic headache as well as minimal data on the comparative effectiveness of CT and MRI to detect intracranial abnormalities.
Outcome 1: Pre-test Probability.
The pre-test probability is usually related to the prevalence of the disease and can be adjusted depending on the characteristics of the population. The study by Sempere et al. determined the pre-test probability (prevalence) of significant intracranial abnormalities in persons with chronic headaches defined as headache experienced for at least a 4 week duration with a normal neurological exam. There is a pre-test probability of 0.9% (95% CI 0.5, 1.4) in persons with chronic headache and normal neurological exam. The highest pre-test probability of 5 found in persons with cluster headaches. The second highest, that of 3.7, was reported in persons with indeterminate type headache. There was a 0.75% rate of incidental findings.
Likelihood ratios for detecting a significant abnormality
Clinical findings from the history and physical may be used as screening test to predict abnormalities on neuroimaging. The extent to which the clinical variable may be a good predictive variable can be captured by reporting its likelihood ratio. The likelihood ratio provides an estimate of how much a test result will change the odds of having a disease or condition. The positive likelihood ratio (LR+) tells you how much the odds of having the disease increases when a test is positive. The negative likelihood ratio (LR-) tells you how much the odds of having the disease decreases when the test is negative.
Detsky et al., determined the likelihood ratio for specific clinical variable from 11 studies. There were 4 clinical variables with both statistically significant positive and negative likelihood ratios. These included: abnormal neurological exam (LR+ 5.3, LR- 0.72), undefined headache (LR+ 3.8, LR- 0.66), headache aggravated by exertion or valsalva (LR+ 2.3, LR- 0.70), and headache with vomiting (LR+ 1.8, and LR- 0.47). There were two clinical variables with a statistically significant positive likelihood ratio and non significant negative likelihood ratio. These included: cluster-type headache (LR+ 11, LR- 0.95), and headache with aura (LR+ 12.9, LR- 0.52). Finally, there were 8 clinical variables with both statistically non significant positive and negative likelihood ratios. These included: headache with focal symptoms, new onset headache, quick onset headache, worsening headache, male gender, headache with nausea, increased headache severity, and migraine type headache.
Outcome 2: Relief from Anxiety
Howard et al. completed an RCT of 150 persons to determine if neuroimaging for headaches was anxiolytic or anxiogenic. Persons were randomized to receiving either an MRI scan or no scan for investigation of their headache. The study population was stratified into those persons with a Hospital Anxiety and Depression scale (HADS) > 11 (the high anxiety and depression group) and those < 11 (the low anxiety and depression) so that there were 4 groups:
Group 1: High anxiety and depression, no scan group
Group 2: High anxiety and depression, scan group
Group 3: Low anxiety and depression, no scan group
Group 4: Low anxiety and depression, scan group
Anxiety
There was no evidence for any overall reduction in anxiety at 1 year as measured by a visual analogue scale of ‘level of worry’ when analysed by whether the person received a scan or not. Similarly, there was no interaction between anxiety and depression status and whether a scan was offered or not on patient anxiety. Anxiety did not decrease at 1 year to any statistically significant degree in the high anxiety and depression group (HADS positive) compared with the low anxiety and depression group (HADS negative).
There are serious methodological limitations in this study design which may have contributed to these negative results. First, when considering the comparison of ‘scan’ vs. ‘no scan’ groups, 12 people (16%) in the ‘no scan group’ actually received a scan within the follow up year. If indeed scanning does reduce anxiety then this contamination of the ‘no scan’ group may have reduced the effect between the groups results resulting in a non significant difference in anxiety scores between the ‘scanned’ and the ‘no scan’ group. Second, there was an inadequate sample size at 1 year follow up in each of the 4 groups which may have contributed to a Type II statistical error (missing a difference when one may exist) when comparing scan vs. no scan by anxiety and depression status. Therefore, based on the results and study limitations it is inconclusive as to whether scanning reduces anxiety.
Outcome 3: System Services
Howard et al., considered services used and system costs a secondary outcome. These were determined by examining primary care case notes at 1 year for consultation rates, symptoms, further investigations, and contact with secondary and tertiary care.
System Services
The authors report that the use of neurologist and psychiatrist services was significantly higher for those persons not offered as scan, regardless of their anxiety and depression status (P<0.001 for neurologist, and P=0.033 for psychiatrist)
Outcome 4: System Costs
System Costs
There was evidence of statistically significantly lower system costs if persons with high levels of anxiety and depression (Hospital Anxiety and Depression Scale score >11) were provided with a scan (P=0.03 including inpatient costs, and 0.047 excluding inpatient costs).
Comparative Effectiveness of CT and MRI Scans
One study reported the detection rate for significant intracranial abnormalities using CT and MRI. In a cohort of 1876 persons with a non acute headache defined as any type of headache that had begun at least 4 weeks before enrolment Sempere et al. reported that the detection rate was 19/1432 (1.3%) using CT and 4/444 (0.9%) using MRI. Of 119 normal CT scans 2 (1.7%) had significant intracranial abnormality on MRI. The 2 cases were a small meningioma, and an acoustic neurinoma.
Summary
The evidence presented can be summarized as follows:
Pre-test Probability
Based on the results by Sempere et al., there is a low pre-test probability for intracranial abnormalities in persons with chronic headaches and a normal neurological exam (defined as headaches experiences for a minimum of 4 weeks). The Grade quality of evidence supporting this outcome is very low.
Likelihood Ratios
Based on the systematic review by Detsky et al., there is a statistically significant positive and negative likelihood ratio for the following clinical variables: abnormal neurological exam, undefined headache, headache aggravated by exertion or valsalva, headache with vomiting. Grade quality of evidence supporting this outcome is very low.
Based on the systematic review by Detsky et al. there is a statistically significant positive likelihood ratio but non statistically significant negative likelihood ratio for the following clinical variables: cluster headache and headache with aura. The Grade quality of evidence supporting this outcome is very low.
Based on the systematic review by Detsky et al., there is a non significant positive and negative likelihood ratio for the following clinical variables: headache with focal symptoms, new onset headache, quick onset headache, worsening headache, male gender, headache with nausea, increased headache severity, migraine type headache. The Grade quality of evidence supporting this outcome is very low.
Relief from Anxiety
Based on the RCT by Howard et al., it is inconclusive whether neuroimaging scans in persons with a chronic headache are anxiolytic. The Grade quality of evidence supporting this outcome is low.
System Services
Based on the RCT by Howard et al. scanning persons with chronic headache regardless of their anxiety and/or depression level reduces service use. The Grade quality of evidence is low.
System Costs
Based on the RCT by Howard et al., scanning persons with a score greater than 11 on the High Anxiety and Depression Scale reduces system costs. The Grade quality of evidence is moderate.
Comparative Effectiveness of CT and MRI Scans
There is sparse evidence to determine the relative effectiveness of CT compared with MRI scanning for the detection of intracranial abnormalities. The Grade quality of evidence supporting this is very low.
Economic Analysis
Ontario Perspective
Volumes for neuroimaging of the head i.e. CT and MRI scans, from the Ontario Health Insurance Plan (OHIP) data set were used to investigate trends in the province for Fiscal Years (FY) 2004-2009.
Assumptions were made in order to investigate neuroimaging of the head for the indication of headache. From the literature, 27% of all CT and 13% of all MRI scans for the head were assumed to include an indication of headache. From that same retrospective chart review and personal communication with the author 16% of CT scans and 4% of MRI scans for the head were for the sole indication of headache. From the Ministry of Health and Long-Term Care (MOHLTC) wait times data, 73% of all CT and 93% of all MRI scans in the province, irrespective of indication were outpatient procedures.
The expenditure for each FY reflects the volume for that year and since volumes have increased in the past 6 FYs, the expenditure has also increased with a pay-out reaching 3.0M and 2.8M for CT and MRI services of the head respectively for the indication of headache and a pay-out reaching 1.8M and 0.9M for CT and MRI services of the head respectively for the indication of headache only in FY 08/09.
Cost per Abnormal Finding
The yield of abnormal finding for a CT and MRI scan of the head for the indication of headache only is 2% and 5% respectively. Based on these yield a high-level estimate of the cost per abnormal finding with neuroimaging of the head for headache only can be calculated for each FY. In FY 08/09 there were 37,434 CT and 16,197 MRI scans of the head for headache only. These volumes would generate a yield of abnormal finding of 749 and 910 with a CT scan and MRI scan respectively. The expenditure for FY 08/09 was 1.8M and 0.9M for CT and MRI services respectively. Therefore the cost per abnormal finding would be $2,409 for CT and $957 for MRI. These cost per abnormal finding estimates were limited because they did not factor in comparators or the consequences associated with an abnormal reading or FNs. The estimates only consider the cost of the neuroimaging procedure and the yield of abnormal finding with the respective procedure.
PMCID: PMC3377587  PMID: 23074404
2.  The power of using functional fMRI on small rodents to study brain pharmacology and disease 
Functional magnetic resonance imaging (fMRI) is an excellent tool to study the effect of pharmacological modulations on brain function in a non-invasive and longitudinal manner. We introduce several blood oxygenation level dependent (BOLD) fMRI techniques, including resting state (rsfMRI), stimulus-evoked (st-fMRI), and pharmacological MRI (phMRI). Respectively, these techniques permit the assessment of functional connectivity during rest as well as brain activation triggered by sensory stimulation and/or a pharmacological challenge. The first part of this review describes the physiological basis of BOLD fMRI and the hemodynamic response on which the MRI contrast is based. Specific emphasis goes to possible effects of anesthesia and the animal’s physiological conditions on neural activity and the hemodynamic response. The second part of this review describes applications of the aforementioned techniques in pharmacologically induced, as well as in traumatic and transgenic disease models and illustrates how multiple fMRI methods can be applied successfully to evaluate different aspects of a specific disorder. For example, fMRI techniques can be used to pinpoint the neural substrate of a disease beyond previously defined hypothesis-driven regions-of-interest. In addition, fMRI techniques allow one to dissect how specific modifications (e.g., treatment, lesion etc.) modulate the functioning of specific brain areas (st-fMRI, phMRI) and how functional connectivity (rsfMRI) between several brain regions is affected, both in acute and extended time frames. Furthermore, fMRI techniques can be used to assess/explore the efficacy of novel treatments in depth, both in fundamental research as well as in preclinical settings. In conclusion, by describing several exemplary studies, we aim to highlight the advantages of functional MRI in exploring the acute and long-term effects of pharmacological substances and/or pathology on brain functioning along with several methodological considerations.
doi:10.3389/fphar.2015.00231
PMCID: PMC4612660  PMID: 26539115
fMRI; rsfMRI; phMRI; BOLD; rodents
3.  Resting-state fMRI as a biomarker for Alzheimer's disease? 
Previous work indicates that resting-state functional magnetic resonance imaging (fMRI) is sensitive to functional brain changes related to Alzheimer's disease (AD) pathology across the clinical spectrum. Cross-sectional studies have found functional connectivity differences in the brain's default mode network in aging, mild cognitive impairment, and AD. In addition, two recent longitudinal studies have shown that functional connectivity changes track AD progression. This earlier work suggests that resting-state fMRI may be a promising biomarker for AD. However, some key issues still need to be addressed before resting-state fMRI can be successfully applied clinically. In a previous issue of Alzheimer's Research & Therapy, Vemuri and colleagues discuss the use of resting-state fMRI in the study of AD. In this commentary, I will highlight and expand upon some of their main conclusions.
doi:10.1186/alzrt106
PMCID: PMC3334541  PMID: 22423634
4.  Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures 
Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient's self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of “synchronicity” of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI). Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG), which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15 and 12.45%, respectively), while combined fMRI/MEG methods using dynamic functional network connectivity analyses improved classification up to 5.12% relative to use of fMRI alone and up to 17.21% relative to use of MEG alone.
doi:10.3389/fnins.2016.00466
PMCID: PMC5070283  PMID: 27807403
connectivity; fMRI; MEG; Schizophrenia; static and dynamic functional connectivity; classification
5.  Fast transient networks in spontaneous human brain activity 
eLife  2014;3:e01867.
To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states.
DOI: http://dx.doi.org/10.7554/eLife.01867.001
eLife digest
When subjects lie motionless inside scanners without any particular task to perform, their brains show stereotyped patterns of activity across regions known as resting state networks. Each network consists of areas with a common function, such as the ‘motor’ network or the ‘visual’ network. The role of resting state networks is unclear, but these spontaneous activity patterns are altered in disorders including autism, schizophrenia, and Alzheimer’s disease.
One puzzling feature of resting state networks is that they seem to last for relatively long times. However, the majority of studies into resting state networks have used fMRI brain scans, in which changes in the level of oxygen in the blood are used as a proxy for the activity of a given brain region. Since changes in blood oxygen occur relatively slowly, the ability of fMRI to detect rapid changes in activity is limited: it is thus possible that the long-lived nature of resting state networks is an artefact of the use of fMRI.
Now, Baker et al. have used a different type of brain scan known as an MEG scan to show that the activity of resting state networks is shorter lived than previously thought. MEG scanners measure changes in the magnetic fields generated by electrical currents in the brain, which means that they can detect alterations in brain activity much more rapidly than fMRI.
MEG recordings from the brains of nine healthy subjects revealed that individual resting state networks were typically stable for only 100 ms to 200 ms. Moreover, transitions between different networks did not occur randomly; instead, certain networks were much more likely to become active after others. The work of Baker et al. suggests that the resting brain is constantly changing between different patterns of activity, which enables it to respond quickly to any given situation.
DOI: http://dx.doi.org/10.7554/eLife.01867.002
doi:10.7554/eLife.01867
PMCID: PMC3965210  PMID: 24668169
magnetoencephalography; resting state; connectivity; non-stationary; hidden Markov model; microstates; human
6.  Vascular autorescaling of fMRI (VasA fMRI) improves sensitivity of population studies: A pilot study 
Neuroimage  2016;124(Pt A):794-805.
The blood oxygenation level-dependent (BOLD) signal is widely used for functional magnetic resonance imaging (fMRI) of brain function in health and disease. The statistical power of fMRI group studies is significantly hampered by high inter-subject variance due to differences in baseline vascular physiology. Several methods have been proposed to account for physiological vascularization differences between subjects and hence improve the sensitivity in group studies. However, these methods require the acquisition of additional reference scans (such as a full resting-state fMRI session or ASL-based calibrated BOLD). We present a vascular autorescaling (VasA) method, which does not require any additional reference scans. VasA is based on the observation that slow oscillations (< 0.1 Hz) in arterial blood CO2 levels occur naturally due to changes in respiration patterns. These oscillations yield fMRI signal changes whose amplitudes reflect the blood oxygenation levels and underlying local vascularization and vascular responsivity. VasA estimates proxies of the amplitude of these CO2-driven oscillations directly from the residuals of task-related fMRI data without the need for reference scans. The estimates are used to scale the amplitude of task-related fMRI responses, to account for vascular differences. The VasA maps compared well to cerebrovascular reactivity (CVR) maps and cerebral blood volume maps based on vascular space occupancy (VASO) measurements in four volunteers, speaking to the physiological vascular basis of VasA. VasA was validated in a wide variety of tasks in 138 volunteers. VasA increased t-scores by up to 30% in specific brain areas such as the visual cortex. The number of activated voxels was increased by up to 200% in brain areas such as the orbital frontal cortex while still controlling the nominal false-positive rate. VasA fMRI outperformed previously proposed rescaling approaches based on resting-state fMRI data and can be readily applied to any task-related fMRI data set, even retrospectively.
Highlights
•We present a vascular autocalibration method (VasA) for fMRI studies.•VasA fMRI accounts for physiological vascularization differences between subjects.•VasA fMRI significantly increases functional sensitivity in group analyses.•VasA does not require additional reference scans or other complicated procedures.•VasA can be readily applied to any task-related fMRI data set, even retrospectively.
doi:10.1016/j.neuroimage.2015.09.033
PMCID: PMC4655941  PMID: 26416648
BOLD fMRI; Group analysis; Vascularization differences; Autorescaling; ALFF
7.  Resting state functional connectivity in the human spinal cord 
eLife  2014;3:e02812.
Functional magnetic resonance imaging using blood oxygenation level dependent (BOLD) contrast is well established as one of the most powerful methods for mapping human brain function. Numerous studies have measured how low-frequency BOLD signal fluctuations from the brain are correlated between voxels in a resting state, and have exploited these signals to infer functional connectivity within specific neural circuits. However, to date there have been no previous substantiated reports of resting state correlations in the spinal cord. In a cohort of healthy volunteers, we observed robust functional connectivity between left and right ventral (motor) horns, and between left and right dorsal (sensory) horns. Our results demonstrate that low-frequency BOLD fluctuations are inherent in the spinal cord as well as the brain, and by analogy to cortical circuits, we hypothesize that these correlations may offer insight into the execution and maintenance of sensory and motor functions both locally and within the cerebrum.
DOI: http://dx.doi.org/10.7554/eLife.02812.001
eLife digest
Brain imaging methods such as functional magnetic resonance imaging (fMRI) can provide us with a picture of what the brain is doing when a person is carrying out a specific task. For example, an fMRI scan recorded whilst someone is reading is likely to show activity in regions in the left hemisphere of the brain that are known to be involved in language comprehension. fMRI can also be used to measure patterns of neuronal activity when someone is awake but not engaged in a specific task. This approach, known as resting state fMRI, can be used to examine which regions of the resting brain are active at the same time. Researchers are interested in these patterns of brain activity because they reflect neural circuits that work together to produce different functions and behaviors.
Over 4000 papers have used resting state fMRI to study the human brain. However, to date there has been no conclusive investigation of resting state activity in the spinal cord. This is largely because the spinal cord is much smaller than the brain, and most fMRI scanners are not sensitive enough to study it in detail. Consequently, little is known about intrinsic neural circuits in the resting spinal cord.
Now Barry et al. have used advances in fMRI technology to show that resting state functional connectivity does indeed exist in the spinal cord. Correlations were found in the resting levels of activity between spatially distinct areas of the cord, specifically between the ventral horns and between the dorsal horns. The ventral horns relay motor signals to the body, whilst the dorsal horns receive sensory signals from the body.
These findings also have clinical applications. Some patients with incomplete spinal cord injuries can recover near normal function, but the mechanisms responsible for this recovery are unclear because clinicians have not been able to probe neuronal connections in the spinal cord in a non-invasive manner. The work of Barry et al. should help with efforts to understand the neuronal changes that support recovery from spinal cord injury.
DOI: http://dx.doi.org/10.7554/eLife.02812.002
doi:10.7554/eLife.02812
PMCID: PMC4120419  PMID: 25097248
fMRI; spinal cord; 7 Tesla; resting state; functional connectivity; human
8.  Neuroimaging basis in the conversion of aMCI patients with APOE-ε4 to AD: study protocol of a prospective diagnostic trial 
BMC Neurology  2016;16:64.
Background
The ε4 allele of the Apolipoprotein E gene (APOE-ε4) is a potent genetic risk factor for sporadic Alzheimer’s disease (AD). Amnestic mild cognitive impairment (aMCI) is an intermediate state between normal cognitive aging and dementia, which is easy to convert to AD dementia. It is an urgent problem in the field of cognitive neuroscience to reveal the conversion of aMCI-ε4 to AD. Based on our preliminary work, we will study the neuroimaging features in the special group of aMCI-ε4 with multi-modality magnetic resonance imaging (structural MRI, resting state-fMRI and diffusion tensor imaging) longitudinally.
Methods/Design
In this study, 200 right-handed subjects who are diagnosed as aMCI with APOE-ε4 will be recruited at the memory clinic of the Neurology Department, XuanWu Hospital, Capital Medical University, Beijing, China. All subjects will undergo the neuroimaging and neuropsychological evaluation at a 1 year-interval for 3 years. The primary outcome measures are 1) Microstructural alterations revealed with multimodal MRI scans including structure MRI (sMRI), resting state functional MRI (rs-fMRI), diffusion tensor imaging (DTI); 2) neuropsychological evaluation, including the World Health Organization-University of California-LosAngeles Auditory Verbal Learning Test (WHO-UCLA AVLT), Addenbrook’s cognitive examination-revised (ACE-R), mini-mental state examination (MMSE), Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating scale (CDR).
Discussion
This study is to find out the neuroimaging biomarker and the changing laws of the marker during the progress of aMCI-ε4 to AD, and the final purpose is to provide scientific evidence for new prevention, diagnosis and treatment of AD.
Trial Registration
This study has been registered to ClinicalTrials.gov (NCT02225964, https://www.clinicaltrials.gov/) in August 24, 2014.
doi:10.1186/s12883-016-0587-2
PMCID: PMC4866435  PMID: 27176479
APOE4 allele; Alzheimer’s disease; Amnestic mild cognitive impairment; Neuroimaging techniques; Multimodality; Longitudinal study
9.  Network analysis of EEG related functional MRI changes due to medication withdrawal in focal epilepsy 
NeuroImage : Clinical  2015;8:560-571.
Anti-epileptic drugs (AEDs) have a global effect on the neurophysiology of the brain which is most likely reflected in functional brain activity recorded with EEG and fMRI. These effects may cause substantial inter-subject variability in studies where EEG correlated functional MRI (EEG–fMRI) is used to determine the epileptogenic zone in patients who are candidate for epilepsy surgery. In the present study the effects on resting state fMRI are quantified in conditions with AED administration and after withdrawal of AEDs.
EEG–fMRI data were obtained from 10 patients in the condition that the patient was on the steady-state maintenance doses of AEDs as prescribed (condition A) and after withdrawal of AEDs (condition B), at the end of a clinically standard pre-surgical long term video-EEG monitoring session. Resting state networks (RSN) were extracted from fMRI. The epileptic component (ICE) was identified by selecting the RSN component with the largest overlap with the EEG–fMRI correlation pattern. Changes in RSN functional connectivity between conditions A and B were quantified.
EEG–fMRI correlation analysis was successful in 30% and 100% of the cases in conditions A and B, respectively. Spatial patterns of ICEs are comparable in conditions A and B, except for one patient for whom it was not possible to identify the ICE in condition A. However, the resting state functional connectivity is significantly increased in the condition after withdrawal of AEDs (condition B), which makes resting state fMRI potentially a new tool to study AED effects. The difference in sensitivity of EEG–fMRI in conditions A and B, which is not related to the number of epileptic EEG events occurring during scanning, could be related to the increased functional connectivity in condition B.
Highlights
•EEG–fMRI measured before and after withdrawal of AEDs in focal epilepsy.•Epilepsy-related fMRI patterns are comparable before and after withdrawal of AEDs.•Increased functional connectivity and EEG–fMRI yield after withdrawal of AEDs.
doi:10.1016/j.nicl.2015.06.002
PMCID: PMC4484549  PMID: 26137444
Epilepsy; EEG–fMRI; Anti-epileptic drugs; Functional connectivity; Resting-state fMRI
10.  Prediction of Task-Related BOLD fMRI with Amplitude Signatures of Resting-State fMRI 
Blood oxygen contrast-functional magnetic resonance imaging (fMRI) signals are a convolution of neural and vascular components. Several studies indicate that task-related (T-fMRI) or resting-state (R-fMRI) responses linearly relate to hypercapnic task responses. Based on the linearity of R-fMRI and T-fMRI with hypercapnia demonstrated by different groups using different study designs, we hypothesized that R-fMRI and T-fMRI signals are governed by a common physiological mechanism and that resting-state fluctuation of amplitude (RSFA) should be linearly related to T-fMRI responses. We tested this prediction in a group of healthy younger humans where R-fMRI, T-fMRI, and hypercapnic (breath hold, BH) task measures were obtained form the same scan session during resting state and during performance of motor and BH tasks. Within individual subjects, significant linear correlations were observed between motor and BH task responses across voxels. When averaged over the whole brain, the subject-wise correlation between the motor and BH tasks showed a similar linear relationship within the group. Likewise, a significant linear correlation was observed between motor-task activity and RSFA across voxels and subjects. The linear rest–task (R–T) relationship between motor activity and RSFA suggested that R-fMRI and T-fMRI responses are governed by similar physiological mechanisms. A practical use of the R–T relationship is its potential to estimate T-fMRI responses in special populations unable to perform tasks during fMRI scanning. Using the R–T relationship determined from the first group of 12 healthy subjects, we predicted the T-fMRI responses in a second group of 7 healthy subjects. RSFA in both the lower and higher frequency ranges robustly predicted the magnitude of T-fMRI responses at the subject and voxel levels. We propose that T-fMRI responses are reliably predictable to the voxel level in situations where only R-fMRI measures are possible, and may be useful for assessing neural activity in task non-compliant clinical populations.
doi:10.3389/fnsys.2012.00007
PMCID: PMC3294272  PMID: 22408609
breath hold; resting-state fluctuations; BOLD; fMRI; hypercapnia; motor cortex; prediction; vascular
11.  Basal ganglia dysfunction in idiopathic REM sleep behaviour disorder parallels that in early Parkinson’s disease 
Brain  2016;139(8):2224-2234.
See Postuma (doi:10.1093/aww131) for a scientific commentary on this article.
Idiopathic REM sleep behaviour disorder (RBD) is associated with frequent conversion to Parkinson’s disease. Rolinski et al. show that resting-state fMRI differentiates cases of RBD and Parkinson’s disease from controls with high sensitivity (96%) and specificity (74–78%). Basal ganglia network connectivity may reveal future Parkinson’s disease before motor symptom onset.
See Postuma (doi:10.1093/aww131) for a scientific commentary on this article.Idiopathic REM sleep behaviour disorder (RBD) is associated with frequent conversion to Parkinson’s disease. Rolinski et al. show that resting-state fMRI differentiates cases of RBD and Parkinson’s disease from controls with high sensitivity (96%) and specificity (74–78%). Basal ganglia network connectivity may reveal future Parkinson’s disease before motor symptom onset.
See Postuma (doi:10.1093/aww131) for a scientific commentary on this article.
Resting state functional magnetic resonance imaging dysfunction within the basal ganglia network is a feature of early Parkinson’s disease and may be a diagnostic biomarker of basal ganglia dysfunction. Currently, it is unclear whether these changes are present in so-called idiopathic rapid eye movement sleep behaviour disorder, a condition associated with a high rate of future conversion to Parkinson’s disease. In this study, we explore the utility of resting state functional magnetic resonance imaging to detect basal ganglia network dysfunction in rapid eye movement sleep behaviour disorder. We compare these data to a set of healthy control subjects, and to a set of patients with established early Parkinson’s disease. Furthermore, we explore the relationship between resting state functional magnetic resonance imaging basal ganglia network dysfunction and loss of dopaminergic neurons assessed with dopamine transporter single photon emission computerized tomography, and perform morphometric analyses to assess grey matter loss. Twenty-six patients with polysomnographically-established rapid eye movement sleep behaviour disorder, 48 patients with Parkinson’s disease and 23 healthy control subjects were included in this study. Resting state networks were isolated from task-free functional magnetic resonance imaging data using dual regression with a template derived from a separate cohort of 80 elderly healthy control participants. Resting state functional magnetic resonance imaging parameter estimates were extracted from the study subjects in the basal ganglia network. In addition, eight patients with rapid eye movement sleep behaviour disorder, 10 with Parkinson’s disease and 10 control subjects received 123I-ioflupane single photon emission computerized tomography. We tested for reduction of basal ganglia network connectivity, and for loss of tracer uptake in rapid eye movement sleep behaviour disorder and Parkinson’s disease relative to each other and to controls. Connectivity measures of basal ganglia network dysfunction differentiated both rapid eye movement sleep behaviour disorder and Parkinson’s disease from controls with high sensitivity (96%) and specificity (74% for rapid eye movement sleep behaviour disorder, 78% for Parkinson’s disease), indicating its potential as an indicator of early basal ganglia dysfunction. Rapid eye movement sleep behaviour disorder was indistinguishable from Parkinson’s disease on resting state functional magnetic resonance imaging despite obvious differences on dopamine transported single photon emission computerized tomography. Basal ganglia connectivity is a promising biomarker for the detection of early basal ganglia network dysfunction, and may help to identify patients at risk of developing Parkinson’s disease in the future. Future risk stratification using a polymodal approach could combine basal ganglia network connectivity with clinical and other imaging measures, with important implications for future neuroprotective trials in rapid eye movement sleep behaviour disorder.
doi:10.1093/brain/aww124
PMCID: PMC4958897  PMID: 27297241
Parkinson’s disease; imaging; rapid eye movement sleep behaviour disorder
12.  Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis 
Neuroinformatics  2015;13(3):277-295.
Research on an early detection of Mild Cognitive Impairment (MCI), a prodromal stage of Alzheimer’s Disease (AD), with resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been of great interest for the last decade. Witnessed by recent studies, functional connectivity is a useful concept in extracting brain network features and finding biomarkers for brain disease diagnosis. However, it still remains challenging for the estimation of functional connectivity from rs-fMRI due to the inevitable high dimensional problem. In order to tackle this problem, we utilize a group sparse representation along with a structural equation model. Unlike the conventional group sparse representation method that does not explicitly consider class-label information, which can help enhance the diagnostic performance, in this paper, we propose a novel supervised discriminative group sparse representation method by penalizing a large within-class variance and a small between-class variance of connectivity coefficients. Thanks to the newly devised penalization terms, we can learn connectivity coefficients that are similar within the same class and distinct between classes, thus helping enhance the diagnostic accuracy. The proposed method also allows the learned common network structure to preserve the network specific and label-related characteristics. In our experiments on the rs-fMRI data of 37 subjects (12 MCI; 25 healthy normal control) with a cross-validation technique, we demonstrated the validity and effectiveness of the proposed method, showing the diagnostic accuracy of 89.19% and the sensitivity of 0.9167.
doi:10.1007/s12021-014-9241-6
PMCID: PMC4469635  PMID: 25501275
Alzheimer’s Disease (AD); Mild Cognitive Impairment (MCI); resting-state fMRI; functional connectivity; sparse regression learning
13.  Advances in the application of MRI to amyotrophic lateral sclerosis 
Importance of the field
With the emergence of therapeutic candidates for the incurable and rapidly progressive neurodegenerative condition of amyotrophic lateral sclerosis (ALS), it will be essential to develop easily obtainable biomarkers for diagnosis, as well as monitoring, in a disease where clinical examination remains the predominant diagnostic tool. Magnetic resonance imaging (MRI) has greatly developed over the past thirty years since its initial introduction to neuroscience. With multi-modal applications, MRI is now offering exciting opportunities to develop practical biomarkers in ALS.
Areas covered in this review
The historical application of MRI to the field of ALS, its state-of-the-art and future aspirations will be reviewed. Specifically, the significance and limitations of structural MRI to detect gross morphological tissue changes in relation to clinical presentation will be discussed. The more recent application of diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), functional and resting-state MRI (fMRI & R-fMRI) will be contrasted in relation to these more conventional MRI assessments. Finally, future aspirations will be sketched out in providing a more disease mechanism-based molecular MRI.
What the reader will gain
This review will equip the reader with an overview of the application of MRI to ALS and illustrate its potential to develop biomarkers. This discussion is exemplified by key studies, demonstrating the strengths and limitations of each modality. The reader will gain an expert opinion on both the current and future developments of MR imaging in ALS.
Take home message
MR imaging generates potential diagnostic, prognostic and therapeutic monitoring biomarkers of ALS. The emerging fusion of structural, functional and potentially molecular imaging will improve our understanding of wider cerebral connectivity and holds the promise of biomarkers sensitive to the earliest changes.
doi:10.1517/17530059.2010.536836
PMCID: PMC3080036  PMID: 21516259
14.  25th Annual Computational Neuroscience Meeting: CNS-2016 
Sharpee, Tatyana O. | Destexhe, Alain | Kawato, Mitsuo | Sekulić, Vladislav | Skinner, Frances K. | Wójcik, Daniel K. | Chintaluri, Chaitanya | Cserpán, Dorottya | Somogyvári, Zoltán | Kim, Jae Kyoung | Kilpatrick, Zachary P. | Bennett, Matthew R. | Josić, Kresimir | Elices, Irene | Arroyo, David | Levi, Rafael | Rodriguez, Francisco B. | Varona, Pablo | Hwang, Eunjin | Kim, Bowon | Han, Hio-Been | Kim, Tae | McKenna, James T. | Brown, Ritchie E. | McCarley, Robert W. | Choi, Jee Hyun | Rankin, James | Popp, Pamela Osborn | Rinzel, John | Tabas, Alejandro | Rupp, André | Balaguer-Ballester, Emili | Maturana, Matias I. | Grayden, David B. | Cloherty, Shaun L. | Kameneva, Tatiana | Ibbotson, Michael R. | Meffin, Hamish | Koren, Veronika | Lochmann, Timm | Dragoi, Valentin | Obermayer, Klaus | Psarrou, Maria | Schilstra, Maria | Davey, Neil | Torben-Nielsen, Benjamin | Steuber, Volker | Ju, Huiwen | Yu, Jiao | Hines, Michael L. | Chen, Liang | Yu, Yuguo | Kim, Jimin | Leahy, Will | Shlizerman, Eli | Birgiolas, Justas | Gerkin, Richard C. | Crook, Sharon M. | Viriyopase, Atthaphon | Memmesheimer, Raoul-Martin | Gielen, Stan | Dabaghian, Yuri | DeVito, Justin | Perotti, Luca | Kim, Anmo J. | Fenk, Lisa M. | Cheng, Cheng | Maimon, Gaby | Zhao, Chang | Widmer, Yves | Sprecher, Simon | Senn, Walter | Halnes, Geir | Mäki-Marttunen, Tuomo | Keller, Daniel | Pettersen, Klas H. | Andreassen, Ole A. | Einevoll, Gaute T. | Yamada, Yasunori | Steyn-Ross, Moira L. | Alistair Steyn-Ross, D. | Mejias, Jorge F. | Murray, John D. | Kennedy, Henry | Wang, Xiao-Jing | Kruscha, Alexandra | Grewe, Jan | Benda, Jan | Lindner, Benjamin | Badel, Laurent | Ohta, Kazumi | Tsuchimoto, Yoshiko | Kazama, Hokto | Kahng, B. | Tam, Nicoladie D. | Pollonini, Luca | Zouridakis, George | Soh, Jaehyun | Kim, DaeEun | Yoo, Minsu | Palmer, S. E. | Culmone, Viviana | Bojak, Ingo | Ferrario, Andrea | Merrison-Hort, Robert | Borisyuk, Roman | Kim, Chang Sub | Tezuka, Taro | Joo, Pangyu | Rho, Young-Ah | Burton, Shawn D. | Bard Ermentrout, G. | Jeong, Jaeseung | Urban, Nathaniel N. | Marsalek, Petr | Kim, Hoon-Hee | Moon, Seok-hyun | Lee, Do-won | Lee, Sung-beom | Lee, Ji-yong | Molkov, Yaroslav I. | Hamade, Khaldoun | Teka, Wondimu | Barnett, William H. | Kim, Taegyo | Markin, Sergey | Rybak, Ilya A. | Forro, Csaba | Dermutz, Harald | Demkó, László | Vörös, János | Babichev, Andrey | Huang, Haiping | Verduzco-Flores, Sergio | Dos Santos, Filipa | Andras, Peter | Metzner, Christoph | Schweikard, Achim | Zurowski, Bartosz | Roach, James P. | Sander, Leonard M. | Zochowski, Michal R. | Skilling, Quinton M. | Ognjanovski, Nicolette | Aton, Sara J. | Zochowski, Michal | Wang, Sheng-Jun | Ouyang, Guang | Guang, Jing | Zhang, Mingsha | Michael Wong, K. Y. | Zhou, Changsong | Robinson, Peter A. | Sanz-Leon, Paula | Drysdale, Peter M. | Fung, Felix | Abeysuriya, Romesh G. | Rennie, Chris J. | Zhao, Xuelong | Choe, Yoonsuck | Yang, Huei-Fang | Mi, Yuanyuan | Lin, Xiaohan | Wu, Si | Liedtke, Joscha | Schottdorf, Manuel | Wolf, Fred | Yamamura, Yoriko | Wickens, Jeffery R. | Rumbell, Timothy | Ramsey, Julia | Reyes, Amy | Draguljić, Danel | Hof, Patrick R. | Luebke, Jennifer | Weaver, Christina M. | He, Hu | Yang, Xu | Ma, Hailin | Xu, Zhiheng | Wang, Yuzhe | Baek, Kwangyeol | Morris, Laurel S. | Kundu, Prantik | Voon, Valerie | Agnes, Everton J. | Vogels, Tim P. | Podlaski, William F. | Giese, Martin | Kuravi, Pradeep | Vogels, Rufin | Seeholzer, Alexander | Podlaski, William | Ranjan, Rajnish | Vogels, Tim | Torres, Joaquin J. | Baroni, Fabiano | Latorre, Roberto | Gips, Bart | Lowet, Eric | Roberts, Mark J. | de Weerd, Peter | Jensen, Ole | van der Eerden, Jan | Goodarzinick, Abdorreza | Niry, Mohammad D. | Valizadeh, Alireza | Pariz, Aref | Parsi, Shervin S. | Warburton, Julia M. | Marucci, Lucia | Tamagnini, Francesco | Brown, Jon | Tsaneva-Atanasova, Krasimira | Kleberg, Florence I. | Triesch, Jochen | Moezzi, Bahar | Iannella, Nicolangelo | Schaworonkow, Natalie | Plogmacher, Lukas | Goldsworthy, Mitchell R. | Hordacre, Brenton | McDonnell, Mark D. | Ridding, Michael C. | Zapotocky, Martin | Smit, Daniel | Fouquet, Coralie | Trembleau, Alain | Dasgupta, Sakyasingha | Nishikawa, Isao | Aihara, Kazuyuki | Toyoizumi, Taro | Robb, Daniel T. | Mellen, Nick | Toporikova, Natalia | Tang, Rongxiang | Tang, Yi-Yuan | Liang, Guangsheng | Kiser, Seth A. | Howard, James H. | Goncharenko, Julia | Voronenko, Sergej O. | Ahamed, Tosif | Stephens, Greg | Yger, Pierre | Lefebvre, Baptiste | Spampinato, Giulia Lia Beatrice | Esposito, Elric | et Olivier Marre, Marcel Stimberg | Choi, Hansol | Song, Min-Ho | Chung, SueYeon | Lee, Dan D. | Sompolinsky, Haim | Phillips, Ryan S. | Smith, Jeffrey | Chatzikalymniou, Alexandra Pierri | Ferguson, Katie | Alex Cayco Gajic, N. | Clopath, Claudia | Angus Silver, R. | Gleeson, Padraig | Marin, Boris | Sadeh, Sadra | Quintana, Adrian | Cantarelli, Matteo | Dura-Bernal, Salvador | Lytton, William W. | Davison, Andrew | Li, Luozheng | Zhang, Wenhao | Wang, Dahui | Song, Youngjo | Park, Sol | Choi, Ilhwan | Shin, Hee-sup | Choi, Hannah | Pasupathy, Anitha | Shea-Brown, Eric | Huh, Dongsung | Sejnowski, Terrence J. | Vogt, Simon M. | Kumar, Arvind | Schmidt, Robert | Van Wert, Stephen | Schiff, Steven J. | Veale, Richard | Scheutz, Matthias | Lee, Sang Wan | Gallinaro, Júlia | Rotter, Stefan | Rubchinsky, Leonid L. | Cheung, Chung Ching | Ratnadurai-Giridharan, Shivakeshavan | Shomali, Safura Rashid | Ahmadabadi, Majid Nili | Shimazaki, Hideaki | Nader Rasuli, S. | Zhao, Xiaochen | Rasch, Malte J. | Wilting, Jens | Priesemann, Viola | Levina, Anna | Rudelt, Lucas | Lizier, Joseph T. | Spinney, Richard E. | Rubinov, Mikail | Wibral, Michael | Bak, Ji Hyun | Pillow, Jonathan | Zaho, Yuan | Park, Il Memming | Kang, Jiyoung | Park, Hae-Jeong | Jang, Jaeson | Paik, Se-Bum | Choi, Woochul | Lee, Changju | Song, Min | Lee, Hyeonsu | Park, Youngjin | Yilmaz, Ergin | Baysal, Veli | Ozer, Mahmut | Saska, Daniel | Nowotny, Thomas | Chan, Ho Ka | Diamond, Alan | Herrmann, Christoph S. | Murray, Micah M. | Ionta, Silvio | Hutt, Axel | Lefebvre, Jérémie | Weidel, Philipp | Duarte, Renato | Morrison, Abigail | Lee, Jung H. | Iyer, Ramakrishnan | Mihalas, Stefan | Koch, Christof | Petrovici, Mihai A. | Leng, Luziwei | Breitwieser, Oliver | Stöckel, David | Bytschok, Ilja | Martel, Roman | Bill, Johannes | Schemmel, Johannes | Meier, Karlheinz | Esler, Timothy B. | Burkitt, Anthony N. | Kerr, Robert R. | Tahayori, Bahman | Nolte, Max | Reimann, Michael W. | Muller, Eilif | Markram, Henry | Parziale, Antonio | Senatore, Rosa | Marcelli, Angelo | Skiker, K. | Maouene, M. | Neymotin, Samuel A. | Seidenstein, Alexandra | Lakatos, Peter | Sanger, Terence D. | Menzies, Rosemary J. | McLauchlan, Campbell | van Albada, Sacha J. | Kedziora, David J. | Neymotin, Samuel | Kerr, Cliff C. | Suter, Benjamin A. | Shepherd, Gordon M. G. | Ryu, Juhyoung | Lee, Sang-Hun | Lee, Joonwon | Lee, Hyang Jung | Lim, Daeseob | Wang, Jisung | Lee, Heonsoo | Jung, Nam | Anh Quang, Le | Maeng, Seung Eun | Lee, Tae Ho | Lee, Jae Woo | Park, Chang-hyun | Ahn, Sora | Moon, Jangsup | Choi, Yun Seo | Kim, Juhee | Jun, Sang Beom | Lee, Seungjun | Lee, Hyang Woon | Jo, Sumin | Jun, Eunji | Yu, Suin | Goetze, Felix | Lai, Pik-Yin | Kim, Seonghyun | Kwag, Jeehyun | Jang, Hyun Jae | Filipović, Marko | Reig, Ramon | Aertsen, Ad | Silberberg, Gilad | Bachmann, Claudia | Buttler, Simone | Jacobs, Heidi | Dillen, Kim | Fink, Gereon R. | Kukolja, Juraj | Kepple, Daniel | Giaffar, Hamza | Rinberg, Dima | Shea, Steven | Koulakov, Alex | Bahuguna, Jyotika | Tetzlaff, Tom | Kotaleski, Jeanette Hellgren | Kunze, Tim | Peterson, Andre | Knösche, Thomas | Kim, Minjung | Kim, Hojeong | Park, Ji Sung | Yeon, Ji Won | Kim, Sung-Phil | Kang, Jae-Hwan | Lee, Chungho | Spiegler, Andreas | Petkoski, Spase | Palva, Matias J. | Jirsa, Viktor K. | Saggio, Maria L. | Siep, Silvan F. | Stacey, William C. | Bernar, Christophe | Choung, Oh-hyeon | Jeong, Yong | Lee, Yong-il | Kim, Su Hyun | Jeong, Mir | Lee, Jeungmin | Kwon, Jaehyung | Kralik, Jerald D. | Jahng, Jaehwan | Hwang, Dong-Uk | Kwon, Jae-Hyung | Park, Sang-Min | Kim, Seongkyun | Kim, Hyoungkyu | Kim, Pyeong Soo | Yoon, Sangsup | Lim, Sewoong | Park, Choongseok | Miller, Thomas | Clements, Katie | Ahn, Sungwoo | Ji, Eoon Hye | Issa, Fadi A. | Baek, JeongHun | Oba, Shigeyuki | Yoshimoto, Junichiro | Doya, Kenji | Ishii, Shin | Mosqueiro, Thiago S. | Strube-Bloss, Martin F. | Smith, Brian | Huerta, Ramon | Hadrava, Michal | Hlinka, Jaroslav | Bos, Hannah | Helias, Moritz | Welzig, Charles M. | Harper, Zachary J. | Kim, Won Sup | Shin, In-Seob | Baek, Hyeon-Man | Han, Seung Kee | Richter, René | Vitay, Julien | Beuth, Frederick | Hamker, Fred H. | Toppin, Kelly | Guo, Yixin | Graham, Bruce P. | Kale, Penelope J. | Gollo, Leonardo L. | Stern, Merav | Abbott, L. F. | Fedorov, Leonid A. | Giese, Martin A. | Ardestani, Mohammad Hovaidi | Faraji, Mohammad Javad | Preuschoff, Kerstin | Gerstner, Wulfram | van Gendt, Margriet J. | Briaire, Jeroen J. | Kalkman, Randy K. | Frijns, Johan H. M. | Lee, Won Hee | Frangou, Sophia | Fulcher, Ben D. | Tran, Patricia H. P. | Fornito, Alex | Gliske, Stephen V. | Lim, Eugene | Holman, Katherine A. | Fink, Christian G. | Kim, Jinseop S. | Mu, Shang | Briggman, Kevin L. | Sebastian Seung, H. | Wegener, Detlef | Bohnenkamp, Lisa | Ernst, Udo A. | Devor, Anna | Dale, Anders M. | Lines, Glenn T. | Edwards, Andy | Tveito, Aslak | Hagen, Espen | Senk, Johanna | Diesmann, Markus | Schmidt, Maximilian | Bakker, Rembrandt | Shen, Kelly | Bezgin, Gleb | Hilgetag, Claus-Christian | van Albada, Sacha Jennifer | Sun, Haoqi | Sourina, Olga | Huang, Guang-Bin | Klanner, Felix | Denk, Cornelia | Glomb, Katharina | Ponce-Alvarez, Adrián | Gilson, Matthieu | Ritter, Petra | Deco, Gustavo | Witek, Maria A. G. | Clarke, Eric F. | Hansen, Mads | Wallentin, Mikkel | Kringelbach, Morten L. | Vuust, Peter | Klingbeil, Guido | De Schutter, Erik | Chen, Weiliang | Zang, Yunliang | Hong, Sungho | Takashima, Akira | Zamora, Criseida | Gallimore, Andrew R. | Goldschmidt, Dennis | Manoonpong, Poramate | Karoly, Philippa J. | Freestone, Dean R. | Soundry, Daniel | Kuhlmann, Levin | Paninski, Liam | Cook, Mark | Lee, Jaejin | Fishman, Yonatan I. | Cohen, Yale E. | Roberts, James A. | Cocchi, Luca | Sweeney, Yann | Lee, Soohyun | Jung, Woo-Sung | Kim, Youngsoo | Jung, Younginha | Song, Yoon-Kyu | Chavane, Frédéric | Soman, Karthik | Muralidharan, Vignesh | Srinivasa Chakravarthy, V. | Shivkumar, Sabyasachi | Mandali, Alekhya | Pragathi Priyadharsini, B. | Mehta, Hima | Davey, Catherine E. | Brinkman, Braden A. W. | Kekona, Tyler | Rieke, Fred | Buice, Michael | De Pittà, Maurizio | Berry, Hugues | Brunel, Nicolas | Breakspear, Michael | Marsat, Gary | Drew, Jordan | Chapman, Phillip D. | Daly, Kevin C. | Bradle, Samual P. | Seo, Sat Byul | Su, Jianzhong | Kavalali, Ege T. | Blackwell, Justin | Shiau, LieJune | Buhry, Laure | Basnayake, Kanishka | Lee, Sue-Hyun | Levy, Brandon A. | Baker, Chris I. | Leleu, Timothée | Philips, Ryan T. | Chhabria, Karishma
BMC Neuroscience  2016;17(Suppl 1):54.
Table of contents
A1 Functional advantages of cell-type heterogeneity in neural circuits
Tatyana O. Sharpee
A2 Mesoscopic modeling of propagating waves in visual cortex
Alain Destexhe
A3 Dynamics and biomarkers of mental disorders
Mitsuo Kawato
F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons
Vladislav Sekulić, Frances K. Skinner
F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains
Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári
F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks.
Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić
O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators
Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona
O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain
Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi
O3 Modeling auditory stream segregation, build-up and bistability
James Rankin, Pamela Osborn Popp, John Rinzel
O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields
Alejandro Tabas, André Rupp, Emili Balaguer-Ballester
O5 A simple model of retinal response to multi-electrode stimulation
Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin
O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task
Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer
O7 Input-location dependent gain modulation in cerebellar nucleus neurons
Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber
O8 Analytic solution of cable energy function for cortical axons and dendrites
Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu
O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network
Jimin Kim, Will Leahy, Eli Shlizerman
O10 Is the model any good? Objective criteria for computational neuroscience model selection
Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook
O11 Cooperation and competition of gamma oscillation mechanisms
Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen
O12 A discrete structure of the brain waves
Yuri Dabaghian, Justin DeVito, Luca Perotti
O13 Direction-specific silencing of the Drosophila gaze stabilization system
Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon
O14 What does the fruit fly think about values? A model of olfactory associative learning
Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn
O15 Effects of ionic diffusion on power spectra of local field potentials (LFP)
Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll
O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits
Yasunori Yamada
O17 Spatial coarse-graining the brain: origin of minicolumns
Moira L. Steyn-Ross, D. Alistair Steyn-Ross
O18 Modeling large-scale cortical networks with laminar structure
Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang
O19 Information filtering by partial synchronous spikes in a neural population
Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner
O20 Decoding context-dependent olfactory valence in Drosophila
Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama
P1 Neural network as a scale-free network: the role of a hub
B. Kahng
P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging
Nicoladie D. Tam
P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique
Nicoladie D.Tam, Luca Pollonini, George Zouridakis
P4 Modeling jamming avoidance of weakly electric fish
Jaehyun Soh, DaeEun Kim
P5 Synergy and redundancy of retinal ganglion cells in prediction
Minsu Yoo, S. E. Palmer
P6 A neural field model with a third dimension representing cortical depth
Viviana Culmone, Ingo Bojak
P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord
Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk
P8 The recognition dynamics in the brain
Chang Sub Kim
P9 Multivariate spike train analysis using a positive definite kernel
Taro Tezuka
P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia
Pangyu Joo
P11 The ionic basis of heterogeneity affects stochastic synchrony
Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban
P12 Circular statistics of noise in spike trains with a periodic component
Petr Marsalek
P14 Representations of directions in EEG-BCI using Gaussian readouts
Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong
P15 Action selection and reinforcement learning in basal ganglia during reaching movements
Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak
P17 Axon guidance: modeling axonal growth in T-Junction assay
Csaba Forro, Harald Dermutz, László Demkó, János Vörös
P19 Transient cell assembly networks encode persistent spatial memories
Yuri Dabaghian, Andrey Babichev
P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons
Haiping Huang
P21 Design of biologically-realistic simulations for motor control
Sergio Verduzco-Flores
P22 Towards understanding the functional impact of the behavioural variability of neurons
Filipa Dos Santos, Peter Andras
P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia
Christoph Metzner, Achim Schweikard, Bartosz Zurowski
P24 Memory recall and spike frequency adaptation
James P. Roach, Leonard M. Sander, Michal R. Zochowski
P25 Stability of neural networks and memory consolidation preferentially occur near criticality
Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski
P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems
Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou
P27 Neurofield: a C++ library for fast simulation of 2D neural field models
Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao
P28 Action-based grounding: Beyond encoding/decoding in neural code
Yoonsuck Choe, Huei-Fang Yang
P29 Neural computation in a dynamical system with multiple time scales
Yuanyuan Mi, Xiaohan Lin, Si Wu
P30 Maximum entropy models for 3D layouts of orientation selectivity
Joscha Liedtke, Manuel Schottdorf, Fred Wolf
P31 A behavioral assay for probing computations underlying curiosity in rodents
Yoriko Yamamura, Jeffery R. Wickens
P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models
Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver
P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm
Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang
P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis
Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon
P35 Dynamics of cooperative excitatory and inhibitory plasticity
Everton J. Agnes, Tim P. Vogels
P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons
William F. Podlaski, Tim P. Vogels
P37 Phenomenological neural model for adaptation of neurons in area IT
Martin Giese, Pradeep Kuravi, Rufin Vogels
P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment
Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels
P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations
Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona
P40 Different roles for transient and sustained activity during active visual processing
Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden
P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications
Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh
P42 High frequency neuron can facilitate propagation of signal in neural networks
Aref Pariz, Shervin S. Parsi, Alireza Valizadeh
P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus
Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova
P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP
Florence I. Kleberg, Jochen Triesch
P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex
Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. Ridding, Jochen Triesch
P46 Structure and dynamics of axon network formed in primary cell culture
Martin Zapotocky, Daniel Smit, Coralie Fouquet, Alain Trembleau
P47 Efficient signal processing and sampling in random networks that generate variability
Sakyasingha Dasgupta, Isao Nishikawa, Kazuyuki Aihara, Taro Toyoizumi
P48 Modeling the effect of riluzole on bursting in respiratory neural networks
Daniel T. Robb, Nick Mellen, Natalia Toporikova
P49 Mapping relaxation training using effective connectivity analysis
Rongxiang Tang, Yi-Yuan Tang
P50 Modeling neuron oscillation of implicit sequence learning
Guangsheng Liang, Seth A. Kiser, James H. Howard, Jr., Yi-Yuan Tang
P51 The role of cerebellar short-term synaptic plasticity in the pathology and medication of downbeat nystagmus
Julia Goncharenko, Neil Davey, Maria Schilstra, Volker Steuber
P52 Nonlinear response of noisy neurons
Sergej O. Voronenko, Benjamin Lindner
P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion
Tosif Ahamed, Greg Stephens
P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings
Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre
P55 Sufficient sampling rates for fast hand motion tracking
Hansol Choi, Min-Ho Song
P56 Linear readout of object manifolds
SueYeon Chung, Dan D. Lee, Haim Sompolinsky
P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves
Ryan S. Phillips, Jeffrey Smith
P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus
Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner
P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning
N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver
P60 A set of curated cortical models at multiple scales on Open Source Brain
Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver
P61 A synaptic story of dynamical information encoding in neural adaptation
Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu
P62 Physical modeling of rule-observant rodent behavior
Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin
P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes
Hannah Choi, Anitha Pasupathy, Eric Shea-Brown
P65 Stability of FORCE learning on spiking and rate-based networks
Dongsung Huh, Terrence J. Sejnowski
P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments
Simon M. Vogt, Arvind Kumar, Robert Schmidt
P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation
Stephen Van Wert, Steven J. Schiff
P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo
Richard Veale, Matthias Scheutz
P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions
Sang Wan Lee
P70 Maturation of sensory networks through homeostatic structural plasticity
Júlia Gallinaro, Stefan Rotter
P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings
Paula Sanz-Leon, Peter A. Robinson
P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study
Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan
P73 Exact spike-timing distribution reveals higher-order interactions of neurons
Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli
P74 Neural mechanism of visual perceptual learning using a multi-layered neural network
Xiaochen Zhao, Malte J. Rasch
P75 Inferring collective spiking dynamics from mostly unobserved systems
Jens Wilting, Viola Priesemann
P76 How to infer distributions in the brain from subsampled observations
Anna Levina, Viola Priesemann
P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons
Lucas Rudelt, Joseph T. Lizier, Viola Priesemann
P78 A nearest-neighbours based estimator for transfer entropy between spike trains
Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann
P79 Active learning of psychometric functions with multinomial logistic models
Ji Hyun Bak, Jonathan Pillow
P81 Inferring low-dimensional network dynamics with variational latent Gaussian process
Yuan Zaho, Il Memming Park
P82 Computational investigation of energy landscapes in the resting state subcortical brain network
Jiyoung Kang, Hae-Jeong Park
P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map
Jaeson Jang, Se-Bum Paik
P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition
Woochul Choi, Se-Bum Paik
P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map
Changju Lee, Jaeson Jang, Se-Bum Paik
P86 Computational method classifying neural network activity patterns for imaging data
Min Song, Hyeonsu Lee, Se-Bum Paik
P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory
Youngjin Park, Woochul Choi, Se-Bum Paik
P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons
Ergin Yilmaz, Veli Baysal, Mahmut Ozer
P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance
Veronika Koren, Klaus Obermayer
P90 Methods for building accurate models of individual neurons
Daniel Saska, Thomas Nowotny
P91 A full size mathematical model of the early olfactory system of honeybees
Ho Ka Chan, Alan Diamond, Thomas Nowotny
P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks
Christoph S. Herrmann, Micah M. Murray, Silvio Ionta, Axel Hutt, Jérémie Lefebvre
P93 Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input
Philipp Weidel, Renato Duarte, Abigail Morrison
P94 Modulation of tuning induced by abrupt reduction of SST cell activity
Jung H. Lee, Ramakrishnan Iyer, Stefan Mihalas
P95 The functional role of VIP cell activation during locomotion
Jung H. Lee, Ramakrishnan Iyer, Christof Koch, Stefan Mihalas
P96 Stochastic inference with spiking neural networks
Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier
P97 Modeling orientation-selective electrical stimulation with retinal prostheses
Timothy B. Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin
P98 Ion channel noise can explain firing correlation in auditory nerves
Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell
P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit
Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram
P100 On the representation of arm reaching movements: a computational model
Antonio Parziale, Rosa Senatore, Angelo Marcelli
P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior
Rosa Senatore, Antonio Parziale, Angelo Marcelli
P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge
K. Skiker, M. Maouene
P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia
Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton
P104 Effect of network size on computational capacity
Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr
P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks
Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton
P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas
Juhyoung Ryu, Sang-Hun Lee
P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception
Joonwon Lee, Sang-Hun Lee
P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making
Hyang Jung Lee, Sang-Hun Lee
P110 A Bayesian algorithm for phoneme Perception and its neural implementation
Daeseob Lim, Sang-Hun Lee
P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol
Jisung Wang, Heonsoo Lee
P112 Self-organized criticality of neural avalanche in a neural model on complex networks
Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee
P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model
Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee
P114 Computational model to replicate seizure suppression effect by electrical stimulation
Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee
P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy
Felix Goetze, Pik-Yin Lai
P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell
Seonghyun Kim, Jeehyun Kwag
P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model
Hyun Jae Jang, Jeehyun Kwag
P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum
Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar
P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease
Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison
P120 Learning sparse representations in the olfactory bulb
Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov
P121 Functional classification of homologous basal-ganglia networks
Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski
P122 Short term memory based on multistability
Tim Kunze, Andre Peterson, Thomas Knösche
P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units
Minjung Kim, Hojeong Kim
P125 Decoding laser-induced somatosensory information from EEG
Ji Sung Park, Ji Won Yeon, Sung-Phil Kim
P126 Phase synchronization of alpha activity for EEG-based personal authentication
Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim
P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model
Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa
P130 Epileptic seizures in the unfolding of a codimension-3 singularity
Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa
P131 Incremental dimensional exploratory reasoning under multi-dimensional environment
Oh-hyeon Choung, Yong Jeong
P132 A low-cost model of eye movements and memory in personal visual cognition
Yong-il Lee, Jaeseung Jeong
P133 Complex network analysis of structural connectome of autism spectrum disorder patients
Su Hyun Kim, Mir Jeong, Jaeseung Jeong
P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip
Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong
P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making
Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong
P136 Detecting purchase decision based on hyperfrontality of the EEG
Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong
P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome
Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong
P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans
Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong
P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it?
Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong
P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish
Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa
P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data
JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii
P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems
Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta
P146 Swinging networks
Michal Hadrava, Jaroslav Hlinka
P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions
Hannah Bos, Moritz Helias
P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning
Charles M. Welzig, Zachary J. Harper
P149 Multiscale complexity analysis for the segmentation of MRI images
Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han
P150 A neuro-computational model of emotional attention
René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker
P151 Multi-site delayed feedback stimulation in parkinsonian networks
Kelly Toppin, Yixin Guo
P152 Bistability in Hodgkin–Huxley-type equations
Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden
P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency
Mark D. McDonnell, Bruce P. Graham
P154 Quantifying resilience patterns in brain networks: the importance of directionality
Penelope J. Kale, Leonardo L. Gollo
P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations
Merav Stern, L. F. Abbott
P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues
Leonid A. Fedorov, Martin A. Giese
P157 Spiking model for the interaction between action recognition and action execution
Mohammad Hovaidi Ardestani, Martin Giese
P158 Surprise-modulated belief update: how to learn within changing environments?
Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner
P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation
Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns
P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks
Won Hee Lee, Sophia Frangou
P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis
Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito
P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations
Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink
P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse
Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers
P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes
Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst
P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology
Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll
P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach
Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. Andreassen
P167 Local field potentials in a 4 × 4 mm2 multi-layered network model
Espen Hagen, Johanna Senk, Sacha J. van Albada, Markus Diesmann
P168 A spiking network model explains multi-scale properties of cortical dynamics
Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Claus-Christian Hilgetag, Markus Diesmann, Sacha Jennifer van Albada
P169 Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups
Haoqi Sun, Olga Sourina, Guang-Bin Huang, Felix Klanner, Cornelia Denk
P170 Tensor decomposition reveals RSNs in simulated resting state fMRI
Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco
P171 Getting in the groove: testing a new model-based method for comparing task-evoked vs resting-state activity in fMRI data on music listening
Matthieu Gilson, Maria AG Witek, Eric F. Clarke, Mads Hansen, Mikkel Wallentin, Gustavo Deco, Morten L. Kringelbach, Peter Vuust
P172 STochastic engine for pathway simulation (STEPS) on massively parallel processors
Guido Klingbeil, Erik De Schutter
P173 Toolkit support for complex parallel spatial stochastic reaction–diffusion simulation in STEPS
Weiliang Chen, Erik De Schutter
P174 Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input
Yunliang Zang, Erik De Schutter
P175 Dendritic morphology determines how dendrites are organized into functional subunits
Sungho Hong, Akira Takashima, Erik De Schutter
P176 A model of Ca2+/calmodulin-dependent protein kinase II activity in long term depression at Purkinje cells
Criseida Zamora, Andrew R. Gallimore, Erik De Schutter
P177 Reward-modulated learning of population-encoded vectors for insect-like navigation in embodied agents
Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta
P178 Data-driven neural models part II: connectivity patterns of human seizures
Philippa J. Karoly, Dean R. Freestone, Daniel Soundry, Levin Kuhlmann, Liam Paninski, Mark Cook
P179 Data-driven neural models part I: state and parameter estimation
Dean R. Freestone, Philippa J. Karoly, Daniel Soundry, Levin Kuhlmann, Mark Cook
P180 Spectral and spatial information processing in human auditory streaming
Jaejin Lee, Yonatan I. Fishman, Yale E. Cohen
P181 A tuning curve for the global effects of local perturbations in neural activity: Mapping the systems-level susceptibility of the brain
Leonardo L. Gollo, James A. Roberts, Luca Cocchi
P182 Diverse homeostatic responses to visual deprivation mediated by neural ensembles
Yann Sweeney, Claudia Clopath
P183 Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography
Soohyun Lee, Woo-Sung Jung, Jee Hyun Choi
P184 Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep
Bowon Kim, Youngsoo Kim, Eunjin Hwang, Jee Hyun Choi
P185 Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response
Younginha Jung, Eunjin Hwang, Yoon-Kyu Song, Jee Hyun Choi
P186 Neural field model of localized orientation selective activation in V1
James Rankin, Frédéric Chavane
P187 An oscillatory network model of Head direction and Grid cells using locomotor inputs
Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P188 A computational model of hippocampus inspired by the functional architecture of basal ganglia
Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P189 A computational architecture to model the microanatomy of the striatum and its functional properties
Sabyasachi Shivkumar, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P190 A scalable cortico-basal ganglia model to understand the neural dynamics of targeted reaching
Vignesh Muralidharan, Alekhya Mandali, B. Pragathi Priyadharsini, Hima Mehta, V. Srinivasa Chakravarthy
P191 Emergence of radial orientation selectivity from synaptic plasticity
Catherine E. Davey, David B. Grayden, Anthony N. Burkitt
P192 How do hidden units shape effective connections between neurons?
Braden A. W. Brinkman, Tyler Kekona, Fred Rieke, Eric Shea-Brown, Michael Buice
P193 Characterization of neural firing in the presence of astrocyte-synapse signaling
Maurizio De Pittà, Hugues Berry, Nicolas Brunel
P194 Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics
James A. Roberts, Leonardo L. Gollo, Michael Breakspear
P195 Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings
Gary Marsat, Jordan Drew, Phillip D. Chapman, Kevin C. Daly, Samual P. Bradley
P196 Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses
Sat Byul Seo, Jianzhong Su, Ege T. Kavalali, Justin Blackwell
P199 Gamma oscillation via adaptive exponential integrate-and-fire neurons
LieJune Shiau, Laure Buhry, Kanishka Basnayake
P200 Visual face representations during memory retrieval compared to perception
Sue-Hyun Lee, Brandon A. Levy, Chris I. Baker
P201 Top-down modulation of sequential activity within packets modeled using avalanche dynamics
Timothée Leleu, Kazuyuki Aihara
Q28 An auto-encoder network realizes sparse features under the influence of desynchronized vascular dynamics
Ryan T. Philips, Karishma Chhabria, V. Srinivasa Chakravarthy
doi:10.1186/s12868-016-0283-6
PMCID: PMC5001212  PMID: 27534393
15.  A SVM-based Quantitative fMRI Method For Resting State Functional Network Detection 
Magnetic resonance imaging  2014;32(7):819-831.
Resting state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting state fMRI data analysis. Specifically, the resting state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting state analysis were extracted and examined using a SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting state quantitative fMRI studies.
doi:10.1016/j.mri.2014.04.004
PMCID: PMC4135463  PMID: 24928301
Functional network; support vector machine; resting state; quantitative fMRI
16.  Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep 
eLife  null;4:e10781.
Propagation of slow intrinsic brain activity has been widely observed in electrophysiogical studies of slow wave sleep (SWS). However, in human resting state fMRI (rs-fMRI), intrinsic activity has been understood predominantly in terms of zero-lag temporal synchrony (functional connectivity) within systems known as resting state networks (RSNs). Prior rs-fMRI studies have found that RSNs are generally preserved across wake and sleep. Here, we use a recently developed analysis technique to study propagation of infra-slow intrinsic blood oxygen level dependent (BOLD) signals in normal adults during wake and SWS. This analysis reveals marked changes in propagation patterns in SWS vs. wake. Broadly, ordered propagation is preserved within traditionally defined RSNs but lost between RSNs. Additionally, propagation between cerebral cortex and subcortical structures reverses directions, and intra-cortical propagation becomes reorganized, especially in visual and sensorimotor cortices. These findings show that propagated rs-fMRI activity informs theoretical accounts of the neural functions of sleep.
DOI: http://dx.doi.org/10.7554/eLife.10781.001
eLife digest
The brain shows spontaneous activity all the time, even when we are sleeping. A technique called functional magnetic resonance imaging (fMRI) has revealed that this spontaneous activity can occur in distinct groups of brain regions at roughly at the same time. Each group is referred to as a resting-state network and the brain regions that make up these networks are largely the same between individuals, and between the sleep and awake states.
However, when spontaneous brain activity is measured in rodents and humans using electrodes, it appears that there are actually waves of electrical activity that spread both within and across resting-state networks. In other words, these studies suggest that brain regions tend to become active in turn rather than at the same time. This led Mitra et al. to question whether the techniques used to analyze fMRI scans of spontaneous brain activity might have overlooked differences in the timing of brain activity.
Mitra et al. used a new technique to analyze fMRI data from healthy adult volunteers. The experiments show that brain regions are activated in a different order depending on whether the individuals are awake or asleep. Specifically, in conscious individuals information from the senses is first processed by a structure deep within the brain called the thalamus before it is passed to the brain’s outer layer, known as the cortex. During deep sleep, this flow of information is reversed and signals are instead sent from the cortex to the thalamus. This may contribute to our loss of sensory awareness during sleep, and even to the occurrence of dreaming.
The exchange of informationbetween resting-state networks also becomes disorganized during sleep. This lends support to the idea that the coordinated transfer of information between networks in the awake state may contribute to consciousness. Future experiments should explore differences in spontaneous brain activity in different phases of sleep, and investigate how such activity is able to spread throughout the brain.
DOI: http://dx.doi.org/10.7554/eLife.10781.002
doi:10.7554/eLife.10781
PMCID: PMC4737658  PMID: 26551562
slow wave sleep; dynamics; resting state; fMRI; propagation; consciousness; Human
17.  Stability of Whole Brain and Regional Network Topology within and between Resting and Cognitive States 
PLoS ONE  2013;8(8):e70275.
Background
Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well.
Methodology/Principal Findings
fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state.
Conclusions/Significance
These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.
doi:10.1371/journal.pone.0070275
PMCID: PMC3734135  PMID: 23940554
18.  A Systematic Review of Relations between Resting-State functional-MRI and Treatment Response in Major Depressive Disorder 
Background
Resting-state functional magnetic resonance imaging (fMRI) is a promising predictor of treatment response in major depressive disorder (MDD).
Methods
A search for papers published in English was conducted using PubMed with the following words: depression, treatment, resting-state, connectivity, and fMRI. Findings from 21 studies of relations between resting-state fMRI and treatment response in MDD are presented, and common findings and themes are discussed.
Results
The use of resting-state fMRI in research on MDD treatment response has yielded a number of consistent findings that provide a basis for understanding the potential mechanisms of action of antidepressant treatment response. These included (1) associations between response to antidepressant medications and increased functional connectivity between frontal and limbic brain regions, possibly resulting in greater inhibitory control over neural circuits that process emotions; (2) connectivity of visual recognition circuits in studies that compared treatment resistant and treatment sensitive patients; (3) response to TMS was consistently predicted by subcallosal cortex connectivity; and (4) hyperconnectivity of the default mode network and hypoconnectivity of the cognitive control network differentiated treatment-resistant from treatment-sensitive MDD patients.
Limitations
There was also considerable variability between studies with respect to study designs and analytic strategies that made direct comparisons across all studies difficult.
Conclusions
Continued standardization of study designs and analytic strategies as well as aggregation of larger datasets will allow the field to better elucidate the potential mechanisms of action of treatment response in patients with MDD to ultimately generate algorithms to predict which patients will response to which antidepressant treatments.
doi:10.1016/j.jad.2014.09.028
PMCID: PMC4375066  PMID: 25451389
Resting state; fMRI; Connectivity; Depression; Treatment; Default-mode
19.  Identifying Neural Drivers with Functional MRI: An Electrophysiological Validation 
PLoS Biology  2008;6(12):e315.
Whether functional magnetic resonance imaging (fMRI) allows the identification of neural drivers remains an open question of particular importance to refine physiological and neuropsychological models of the brain, and/or to understand neurophysiopathology. Here, in a rat model of absence epilepsy showing spontaneous spike-and-wave discharges originating from the first somatosensory cortex (S1BF), we performed simultaneous electroencephalographic (EEG) and fMRI measurements, and subsequent intracerebral EEG (iEEG) recordings in regions strongly activated in fMRI (S1BF, thalamus, and striatum). fMRI connectivity was determined from fMRI time series directly and from hidden state variables using a measure of Granger causality and Dynamic Causal Modelling that relates synaptic activity to fMRI. fMRI connectivity was compared to directed functional coupling estimated from iEEG using asymmetry in generalised synchronisation metrics. The neural driver of spike-and-wave discharges was estimated in S1BF from iEEG, and from fMRI only when hemodynamic effects were explicitly removed. Functional connectivity analysis applied directly on fMRI signals failed because hemodynamics varied between regions, rendering temporal precedence irrelevant. This paper provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on brain connectivity using functional neuroimaging.
Author Summary
Our understanding of how the brain works relies on the development of neuropsychological models, which describe how brain activity is coordinated among different regions during the execution of a given task. Knowing the directionality of information transfer between connected regions, and in particular distinguishing neural drivers, or the source of forward connections in the brain, from other brain regions, is critical to refine models of the brain. However, whether functional magnetic resonance imaging (fMRI), the most common technique for imaging brain function, allows one to identify neural drivers remains an open question. Here, we used a rat model of absence epilepsy, a form of nonconvulsive epilepsy that occurs during childhood in humans, showing spontaneous spike-and-wave discharges (nonconvulsive seizures) originating from the first somatosensory cortex, to validate several functional connectivity measures derived from fMRI. Standard techniques estimating interactions directly from fMRI data failed because blood flow dynamics varied between regions. However, we were able to identify the neural driver of spike-and-wave discharges when hemodynamic effects were explicitly removed using appropriate modelling. This study thus provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on connectivity in the functional neuroimaging literature.
Neural long-range interactions can be distinguished from hemodynamic confounds in functional magnetic resonance imaging using new data analysis techniques that will allow experimental validation of models of brain function.
doi:10.1371/journal.pbio.0060315
PMCID: PMC2605917  PMID: 19108604
20.  Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal 
The Journal of Neuroscience  2016;36(22):6030-6040.
Spontaneous activity observed with resting-state fMRI is used widely to uncover the brain's intrinsic functional networks in health and disease. Although many networks appear modular and specific, global and nonspecific fMRI fluctuations also exist and both pose a challenge and present an opportunity for characterizing and understanding brain networks. Here, we used a multimodal approach to investigate the neural correlates to the global fMRI signal in the resting state. Like fMRI, resting-state power fluctuations of broadband and arrhythmic, or scale-free, macaque electrocorticography and human magnetoencephalography activity were correlated globally. The power fluctuations of scale-free human electroencephalography (EEG) were coupled with the global component of simultaneously acquired resting-state fMRI, with the global hemodynamic change lagging the broadband spectral change of EEG by ∼5 s. The levels of global and nonspecific fluctuation and synchronization in scale-free population activity also varied across and depended on arousal states. Together, these results suggest that the neural origin of global resting-state fMRI activity is the broadband power fluctuation in scale-free population activity observable with macroscopic electrical or magnetic recordings. Moreover, the global fluctuation in neurophysiological and hemodynamic activity is likely modulated through diffuse neuromodulation pathways that govern arousal states and vigilance levels.
SIGNIFICANCE STATEMENT This study provides new insights into the neural origin of resting-state fMRI. Results demonstrate that the broadband power fluctuation of scale-free electrophysiology is globally synchronized and directly coupled with the global component of spontaneous fMRI signals, in contrast to modularly synchronized fluctuations in oscillatory neural activity. These findings lead to a new hypothesis that scale-free and oscillatory neural processes account for global and modular patterns of functional connectivity observed with resting-state fMRI, respectively.
doi:10.1523/JNEUROSCI.0187-16.2016
PMCID: PMC4887567  PMID: 27251624
global fMRI; oscillation; resting state; scale free
21.  Amplitude of Low-frequency Oscillations in Parkinson's Disease: A 2-year Longitudinal Resting-state Functional Magnetic Resonance Imaging Study 
Chinese Medical Journal  2015;128(5):593-601.
Background:
Neuroimaging studies have found that functional changes exist in patients with Parkinson's disease (PD). However, the majority of functional magnetic resonance imaging (fMRI) studies in patients with PD are task-related and cross-sectional. This study investigated the functional changes observed in patients with PD, at both baseline and after 2 years, using resting-state fMRI. It further investigated the relationship between whole-brain spontaneous neural activity of patients with PD and their clinical characteristics.
Methods:
Seventeen patients with PD underwent an MRI procedure at both baseline and after 2 years using resting-state fMRI that was derived from the same 3T MRI. In addition, 20 age- and sex-matched, healthy controls were examined using resting-state fMRI. The fractional amplitude of low-frequency fluctuation (fALFF) approach was used to analyze the fMRI data. Nonlinear registration was used to model within-subject changes over the scanning interval, as well as changes between the patients with PD and the healthy controls. A correlative analysis between the fALFF values and clinical characteristics was performed in the regions showing fALFF differences.
Results:
Compared to the control subjects, the patients with PD showed increased fALFF values in the left inferior temporal gyrus, right inferior parietal lobule (IPL) and right middle frontal gyrus. Compared to the baseline in the 2 years follow-up, the patients with PD presented with increased fALFF values in the right middle temporal gyrus and right middle occipital gyrus while also having decreased fALFF values in the right cerebellum, right thalamus, right striatum, left superior parietal lobule, left IPL, left precentral gyrus, and left postcentral gyrus (P < 0.01, after correction with AlphaSim). In addition, the fALFF values in the right cerebellum were positively correlated with the Unified PD Rating Scale (UPDRS) motor scores (r = 0.51, P < 0.05, uncorrected) and the change in the UPDRS motor score (r = 0.61, P < 0.05, uncorrected).
Conclusions:
The baseline and longitudinal changes of the fALFF values in our study suggest that dysfunction in the brain may affect the regions related to cortico-striato-pallido-thalamic loops and cerebello-thalamo-cortical loops as the disease progresses and that alterations to the spontaneous neural activity of the cerebellum may also play an important role in the disease's progression in patients with PD.
doi:10.4103/0366-6999.151652
PMCID: PMC4834768  PMID: 25698189
Functional Magnetic Resonance Imaging; Longitudinal; Parkinson's Disease; Resting State
22.  Reduced functional brain connectivity prior to and after disease onset in Huntington's disease☆☆☆ 
NeuroImage : Clinical  2013;2:377-384.
Background
Huntington's disease (HD) is characterised by both regional and generalised neuronal cell loss in the brain. Investigating functional brain connectivity patterns in rest in HD has the potential to broaden the understanding of brain functionality in relation to disease progression. This study aims to establish whether brain connectivity during rest is different in premanifest and manifest HD as compared to controls.
Methods
At the Leiden University Medical Centre study site of the TRACK-HD study, 20 early HD patients (disease stages 1 and 2), 28 premanifest gene carriers and 28 healthy controls underwent 3 T MRI scanning. Standard and high-resolution T1-weighted images and a resting state fMRI scan were acquired. Using FSL, group differences in resting state connectivity were examined for eight networks of interest using a dual regression method. With a voxelwise correction for localised atrophy, group differences in functional connectivity were examined.
Results
Brain connectivity of the left middle frontal and pre-central gyrus, and right post central gyrus with the medial visual network was reduced in premanifest and manifest HD as compared to controls (0.05 > p > 0.0001). In manifest HD connectivity of numerous widespread brain regions with the default mode network and the executive control network were reduced (0.05 > p > 0.0001).
Discussion
Brain regions that show reduced intrinsic functional connectivity are present in premanifest gene carriers and to a much larger extent in manifest HD patients. These differences are present even when the potential influence of atrophy is taken into account. Resting state fMRI could potentially be used for early disease detection in the premanifest phase of HD and for monitoring of disease modifying compounds.
Highlights
•We applied resting state fMRI in premanifest and manifest Huntington's disease.•Reduced functional brain connectivity was present in premanifest HD gene carriers.•Large regions demonstrate reduced functional brain connectivity in manifest HD.•Medial visual, default mode and executive control networks were affected.
doi:10.1016/j.nicl.2013.03.001
PMCID: PMC3778251  PMID: 24179791
Huntington's disease; Resting state fMRI; Premanifest gene carriers; Functional connectivity
23.  Effects of fMRI-EEG Mismatches in Cortical Current Density Estimation Integrating fMRI and EEG: A Simulation Study 
Objective
Multimodal functional neuroimaging by combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has been studied to achieve high-resolution reconstruction of the spatiotemporal cortical current density (CCD) distribution. However, mismatches between these two imaging modalities may occur due to their different underlying mechanisms. The aim of the present study is to investigate the effects of different types of fMRI-EEG mismatches, including fMRI invisible sources, fMRI extra regions and fMRI displacement, on the fMRI-constrained cortical imaging in a computer simulation based on realistic-geometry boundary-element-method (BEM) model.
Methods
Two methods have been adopted to integrate the synthetic fMRI and EEG data for CCD imaging. In addition to the well-known 90% fMRI-constrained Wiener filter approach (Liu AK, Belliveau JW and Dale AM, PNAS, 95: 8945–8950, 1998), we propose a novel two-step algorithm (referred to as “Twomey algorithm”) for fMRI-EEG integration. In the first step, a “hard” spatial prior derived from fMRI is imposed to solve the EEG inverse problem with a reduced source space; in the second step, the fMRI constraint is removed and the source estimate from the first step is re-entered as the initial guess of the desired solution into an EEG least squares fitting procedure with Twomey regularization. Twomey regularization is a modified Tikhonov technique that attempts to simultaneously minimize the distance between the desired solution and the initial estimate, and the residual errors of fitness to EEG data. The performance of the proposed Twomey algorithm has been evaluated both qualitatively and quantitatively along with the lead-field normalized minimum norm (WMN) and the 90% fMRI-weighted Wiener filter approach, under repeated and randomized source configurations. Point spread function (PSF) and localization error (LE) are used to measure the performance of different imaging approaches with or without a variety of fMRI-EEG mismatches.
Results
The results of the simulation show that the Twomey algorithm can successfully reduce the PSF of fMRI invisible sources compared to the Wiener estimation, without losing the merit of having much lower PSF of fMRI visible sources relative to the WMN solution. In addition, the existence of fMRI extra sources does not significantly affect the accuracy of the fMRI-EEG integrated CCD estimation for both the Wiener filter method and the proposed Twomey algorithm, while the Twomey algorithm may further reduce the chance of occurring spurious sources in the extra fMRI regions. The fMRI displacement away from the electrical source causes enlarged localization error in the imaging results of both the Twomey and Wiener approaches, while Twomey gives smaller LE than Wiener with the fMRI displacement ranging from 1-cm to 2-cm. With less than 2-cm fMRI displacement, the LEs for the Twomey and Wiener approaches are still smaller than in the WMN solution.
Conclusions
The present study suggests that the presence of fMRI invisible sources is the most problematic factor responsible for the error of fMRI-EEG integrated imaging based on the Wiener filter approach, whereas this approach is relatively robust against the fMRI extra regions and small displacement between fMRI activation and electrical current sources. While maintaining the above advantages possessed by the Wiener filter approach, the Twomey algorithm can further effectively alleviate the underestimation of fMRI invisible sources, suppress fMRI spurious sources and improve the robustness against fMRI displacement. Therefore, the Twomey algorithm is expected to improve the reliability of multimodal cortical source imaging against fMRI-EEG mismatches.
Significance
The proposed method promises to provide a useful alternative for multimodal neuroimaging integrating fMRI and EEG.
doi:10.1016/j.clinph.2006.03.031
PMCID: PMC1945186  PMID: 16765085
multimodal neuroimaging; EEG; fMRI; lead-field normalized minimum norm; point spread function; Twomey regularization; Wiener estimation; boundary element method
24.  Reorganization of brain networks in aging: a review of functional connectivity studies 
Healthy aging (HA) is associated with certain declines in cognitive functions, even in individuals that are free of any process of degenerative illness. Functional magnetic resonance imaging (fMRI) has been widely used in order to link this age-related cognitive decline with patterns of altered brain function. A consistent finding in the fMRI literature is that healthy old adults present higher activity levels in some brain regions during the performance of cognitive tasks. This finding is usually interpreted as a compensatory mechanism. More recent approaches have focused on the study of functional connectivity, mainly derived from resting state fMRI, and have concluded that the higher levels of activity coexist with disrupted connectivity. In this review, we aim to provide a state-of-the-art description of the usefulness and the interpretations of functional brain connectivity in the context of HA. We first give a background that includes some basic aspects and methodological issues regarding functional connectivity. We summarize the main findings and the cognitive models that have been derived from task-activity studies, and we then review the findings provided by resting-state functional connectivity in HA. Finally, we suggest some future directions in this field of research. A common finding of the studies included is that older subjects present reduced functional connectivity compared to young adults. This reduced connectivity affects the main brain networks and explains age-related cognitive alterations. Remarkably, the default mode network appears as a highly compromised system in HA. Overall, the scenario given by both activity and connectivity studies also suggests that the trajectory of changes during task may differ from those observed during resting-state. We propose that the use of complex modeling approaches studying effective connectivity may help to understand context-dependent functional reorganizations in the aging process.
doi:10.3389/fpsyg.2015.00663
PMCID: PMC4439539  PMID: 26052298
fMRI; brain networks; aging; memory; connectivity; independent component analysis; default mode network
25.  A connectivity-based test-retest dataset of multi-modal magnetic resonance imaging in young healthy adults 
Scientific Data  2015;2:150056.
Recently, magnetic resonance imaging (MRI) has been widely used to investigate the structures and functions of the human brain in health and disease in vivo. However, there are growing concerns about the test-retest reliability of structural and functional measurements derived from MRI data. Here, we present a test-retest dataset of multi-modal MRI including structural MRI (S-MRI), diffusion MRI (D-MRI) and resting-state functional MRI (R-fMRI). Fifty-seven healthy young adults (age range: 19–30 years) were recruited and completed two multi-modal MRI scan sessions at an interval of approximately 6 weeks. Each scan session included R-fMRI, S-MRI and D-MRI data. Additionally, there were two separated R-fMRI scans at the beginning and at the end of the first session (approximately 20 min apart). This multi-modal MRI dataset not only provides excellent opportunities to investigate the short- and long-term test-retest reliability of the brain’s structural and functional measurements at the regional, connectional and network levels, but also allows probing the test-retest reliability of structural-functional couplings in the human brain.
doi:10.1038/sdata.2015.56
PMCID: PMC4623457  PMID: 26528395
Bioinformatics; Magnetic resonance imaging; Brain imaging; Functional magnetic resonance imaging

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