Resting-state fMRI: a review of methods and clinical applications

Testing a dual-systems model of adolescent brain development using resting-state connectivity analyses

The current study aimed to test a dual-systems model of adolescent brain development by studying changes in intrinsic functional connectivity within and across networks typically associated with cognitive-control and affective-motivational processes. To this end, resting-state and task-related fMRI data were collected of 269 participants (ages 8-25). Resting-state analyses focused on seeds derived from task-related neural activation in the same participants: the dorsal lateral prefrontal cortex (dlPFC) from a cognitive rule-learning paradigm and the nucleus accumbens (NAcc) from a reward-paradigm. Whole-brain seed-based resting-state analyses showed an age-related increase in dlPFC connectivity with the caudate and thalamus, and an age-related decrease in connectivity with the (pre)motor cortex. nAcc connectivity showed a strengthening of connectivity with the dorsal anterior cingulate cortex (ACC) and subcortical structures such as the hippocampus, and a specific age-related decrease in connectivity with the ventral medial PFC (vmPFC). Behavioral measures from both functional paradigms correlated with resting-state connectivity strength with their respective seed. That is, age-related change in learning performance was mediated by connectivity between the dlPFC and thalamus, and age-related change in winning pleasure was mediated by connectivity between the nAcc and vmPFC. These patterns indicate (i) strengthening of connectivity between regions that support control and learning, (ii) more independent functioning of regions that support motor and control networks, and (iii) more independent functioning of regions that support motivation and valuation networks with age. These results are interpreted vis-à-vis a dual-systems model of adolescent brain development.

Modern brain tumor imaging

The imaging and clinical management of patients with brain tumor continue to evolve over time and now heavily rely on physiologic imaging in addition to high-resolution structural imaging. Imaging remains a powerful noninvasive tool to positively impact the management of patients with brain tumor. This article provides an overview of the current state-of-the art clinical brain tumor imaging. In this review, we discuss general magnetic resonance (MR) imaging methods and their application to the diagnosis of, treatment planning and navigation, and disease monitoring in patients with brain tumor. We review the strengths, limitations, and pitfalls of structural imaging, diffusion-weighted imaging techniques, MR spectroscopy, perfusion imaging, positron emission tomography/MR, and functional imaging. Overall this review provides a basis for understudying the role of modern imaging in the care of brain tumor patients.

Diffusion-MRI in neurodegenerative disorders

The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance imaging (MRI), "diffusion" (i.e. the random, thermally-induced displacements of water molecules over time) represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords has proven useful in a vast number of clinical as well as research applications. Since diffusion-MRI is a truly quantitative imaging technique, the indices it provides can serve as potential imaging biomarkers which could allow early detection of pathological alterations as well as tracking and possibly predicting subtle changes in follow-up examinations and clinical trials. Accordingly, diffusion-MRI has proven useful in obtaining information to better understand the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various neurodegenerative disorders including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and degenerative ataxias.

Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory

OBJECTIVE

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation.

METHOD

In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD.

RESULTS

Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%.

CONCLUSION

Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD.

SIGNIFICANCE

Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease.

The potential of infant fMRI research and the study of early life stress as a promising exemplar

Functional magnetic resonance imaging (fMRI) research with infants and toddlers has increased rapidly over the past decade, and provided a unique window into early brain development. In the current report, we review the state of the literature, which has established the feasibility and utility of task-based fMRI and resting state functional connectivity MRI (rs-fcMRI) during early periods of brain maturation. These methodologies have been successfully applied beginning in the neonatal period to increase understanding of how the brain both responds to environmental stimuli, and becomes organized into large-scale functional systems that support complex behaviors. We discuss the methodological challenges posed by this promising area of research. We also highlight that despite these challenges, early work indicates a strong potential for these methods to influence multiple research domains. As an example, we focus on the study of early life stress and its influence on brain development and mental health outcomes. We illustrate the promise of these methodologies for building on, and making important contributions to, the existing literature in this field.

Default mode network connectivity as a function of familial and environmental risk for psychotic disorder

Background

Research suggests that altered interregional connectivity in specific networks, such as the default mode network (DMN), is associated with cognitive and psychotic symptoms in schizophrenia. In addition, frontal and limbic connectivity alterations have been associated with trauma, drug use and urban upbringing, though these environmental exposures have never been examined in relation to DMN functional connectivity in psychotic disorder.

Methods

Resting-state functional MRI scans were obtained from 73 patients with psychotic disorder, 83 non-psychotic siblings of patients with psychotic disorder and 72 healthy controls. Posterior cingulate cortex (PCC) seed-based correlation analysis was used to estimate functional connectivity within the DMN. DMN functional connectivity was examined in relation to group (familial risk), group × environmental exposure (to cannabis, developmental trauma and urbanicity) and symptomatology.

Results

There was a significant association between group and PCC connectivity with the inferior parietal lobule (IPL), the precuneus (PCu) and the medial prefrontal cortex (MPFC). Compared to controls, patients and siblings had increased PCC connectivity with the IPL, PCu and MPFC. In the IPL and PCu, the functional connectivity of siblings was intermediate to that of controls and patients. No significant associations were found between DMN connectivity and (subclinical) psychotic/cognitive symptoms. In addition, there were no significant interactions between group and environmental exposures in the model of PCC functional connectivity.

Discussion

Increased functional connectivity in individuals with (increased risk for) psychotic disorder may reflect trait-related network alterations. The within-network “connectivity at rest” intermediate phenotype was not associated with (subclinical) psychotic or cognitive symptoms. The association between familial risk and DMN connectivity was not conditional on environmental exposure.

Love-related changes in the brain: a resting-state functional magnetic resonance imaging study

Romantic love is a motivational state associated with a desire to enter or maintain a close relationship with a specific other person. Functional magnetic resonance imaging (fMRI) studies have found activation increases in brain regions involved in the processing of reward, motivation and emotion regulation, when romantic lovers view photographs of their partners. However, not much is known about whether romantic love affects the brain’s functional architecture during rest. In the present study, resting state functional magnetic resonance imaging (rsfMRI) data was collected to compare the regional homogeneity (ReHo) and functional connectivity (FC) across an “in-love” group (LG, N = 34, currently intensely in love), an “ended-love” group (ELG, N = 34, ended romantic relationship recently), and a “single” group (SG, N = 32, never fallen in love). Results show that: (1) ReHo of the left dorsal anterior cingulate cortex (dACC) was significantly increased in the LG (in comparison to the ELG and the SG); (2) ReHo of the left dACC was positively correlated with length of time in love in the LG, and negatively correlated with the lovelorn duration since breakup in the ELG; (3) FC within the reward, motivation, and emotion regulation network (dACC, insula, caudate, amygdala, and nucleus accumbens) as well as FC in the social cognition network [temporo-parietal junction (TPJ), posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), inferior parietal, precuneus, and temporal lobe] was significantly increased in the LG (in comparison to the ELG and SG); (4) in most regions within both networks FC was positively correlated with the duration of love in the LG but negatively correlated with the lovelorn duration of time since breakup in the ELG. This study provides first empirical evidence of love-related alterations in brain functional architecture. Furthermore, the results shed light on the underlying neural mechanisms of romantic love, and demonstrate the possibility of applying a resting-state fMRI approach for investigating romantic love.

Evaluation of resting state networks in patients with gliomas: connectivity changes in the unaffected side and its relation to cognitive function

In this study, we investigated changes in resting state networks (RSNs) in patients with gliomas located in the left hemisphere and its relation to cognitive function. We hypothesized that long distance connection, especially between hemispheres, would be affected by the presence of the tumor. We further hypothesized that these changes would correlate with, or reflect cognitive changes observed in patients with gliomas. Resting state functional MRI datasets from 12 patients and 12 healthy controls were used in the analysis. The tumor’s effect on three well-known RSNs including the default mode network (DMN), executive control network (ECN), and salience network (SN) identified using independent component analysis were investigated using dual regression analysis. Scores of neuropsychometric testing (WAIS-III and WMS-R) were also compared. Compared to the healthy control group, the patient group showed significant decrease in functional connectivity in the right angular gyrus/inferior parietal lobe of the ventral DMN and in the dorsolateral prefrontal cortex of the left ECN, whereas a significant increase in connectivity in the right ECN was observed in the right parietal lobe. Changes in connectivity in the right ECN correlated with spatial memory, while that on the left ECN correlated with attention. Connectivity changes in the ventral DMN correlated with attention, working memory, full IQ, and verbal IQ measures. Although the tumors were localized in the left side of the brain, changes in connectivity were observed in the contralateral side. Moreover, these changes correlated with some aspects of cognitive function indicating that patients with gliomas may undergo cognitive changes even in the absence of or before the onset of major symptoms. Evaluation of resting state networks could be helpful in advancing our hodological understanding of brain function in glioma cases.

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 respond to which antidepressant treatments.

Advances in pediatric neuroimaging

Conventional MRI protocols are an integral part of routine clinical imaging in pediatric patients. The advent of several newer MRI techniques provides crucial insight into the structural integrity and functional aspects of the developing brain, especially with the introduction of 3T MRI systems in clinical practice. The field of pediatric neuroimaging continues to evolve, with greater emphasis on high spatial resolution, faster scan time, as well as a quest for visualization of the functional aspects of the human brain. MR vendors are increasingly focusing on optimizing MR technology to make it suitable for children, in whom as compared to adults the head size is usually smaller and demonstrates inherent neuroanatomical differences relating to brain development. The eventual goal of these advances would be to evolve as potential biomarkers for predicting neurodevelopment outcomes and prognostication, in addition to their utility in routine diagnostic and therapeutic decision-making. Advanced MR techniques like diffusion tensor imaging, functional MRI, MR perfusion, spectroscopy, volumetric imaging and arterial spin labeling add to our understanding of normal brain development and pathophysiology of various neurological disease processes. This review is primarily focused on outlining advanced MR techniques and their current and potential pediatric neuroimaging applications as well as providing a brief overview of advances in hardware and machine design.

Old wine in new bottles: validating the clinical utility of SPECT in predicting cognitive performance in mild traumatic brain injury

The neural underpinnings of cognitive dysfunction in mild traumatic brain injury (TBI) are not fully understood. Consequently, patient prognosis using existing clinical imaging is somewhat imprecise. Single photon emission computed tomography (SPECT) is a frequently employed investigation in this population, notwithstanding uncertainty over the clinical utility of the data obtained. In this study, subjects with mild TBI underwent (99m)Tc-ECD SPECT scanning, and were administered a brief battery of cognitive tests and self-report symptom scales of concussion and emotional distress. Testing took place 2 weeks (n=84) and 1 year (n=49) post-injury. Multivariate analysis (i.e., partial least squares analysis) revealed that frontal perfusion in right superior frontal and middle frontal gyri predicted poorer performance on the Stroop test, an index of executive function, both at initial and follow-up testing. Conversely, SPECT scans categorized as normal or abnormal by radiologists did not differentiate cognitively impaired from intact subjects. These results demonstrate the clinical utility of SPECT in mild TBI, but only when data are subjected to blood flow quantification analysis.

Neuroplasticity in Human Alcoholism: Studies of Extended Abstinence with Potential Treatment Implications

Alcoholism is characterized by a lack of control over excessive alcohol consumption despite significant negative consequences. This impulsive and compulsive behavior may be related to functional abnormalities within networks of brain regions responsible for how we make decisions. The abnormalities may result in strengthened networks related to appetitive drive-or the need to fulfill desires-and simultaneously weakened networks that exercise control over behaviors. Studies using functional magnetic resonance imaging (fMRI) in abstinent alcoholics suggest that abstinence is associated with changes in the tone of such networks, decreasing resting tone in appetitive drive networks, and increasing resting tone in inhibitory control networks to support continued abstinence. Identifying electroencephalographic (EEG) measures of resting tone in these networks initially identified using fMRI, and establishing in longitudinal studies that these abstinence-related changes in network tone are progressive would motivate treatment initiatives to facilitate these changes in network tone, thereby supporting successful ongoing abstinence.

Altered resting-state connectivity in college students with nonclinical depressive symptoms

Background

The underlying brain basis of nonclinical depressive symptoms (nCDSs) is largely unknown. Recently, the seed-based functional connectivity (FC) approach for analyzing resting-state fMRI (rs-fMRI) data has been increasingly used to explore the neural basis of depressive disorders. Other than common seed-based FC method using an a priori seed region, we conducted FC analysis based on regions with altered spontaneous activity revealed by the fractional amplitude of low-frequency fluctuations (fALFF) approach. The aim of the present study was to provide novel insight in the underlying mechanism of nCDSs in college students.

Methodology/Principal Findings

A total number of 1105 college students were recruited to participant in a survey for assessing depressive symptoms. Subsequently, 17 individuals with nCDSs and 20 healthy controls (HCs) were enrolled to perform MR studies. Alternations of fALFF were identified in the right superior parietal lobule (SPL) and left lingual gyrus, both of which were used as ROIs for further FC analysis. With right SPL, compare with HCs, subjects with nCDSs showed reduced FCs in the bilateral dorsal lateral prefrontal cortex (DLPFC), left inferior frontal gurus (IFG), left premotor cortex (PMC), DMN network [i.e., bilateral precuneus, posterior cingulate cortex (PCC), right supramarginal gyrus (SMG), right parahippocampal gyrus (PHG), bilateral inferior temporal gurus (ITG)] and left cerebellum posterior lobe (CPL). In addition, increased FCs were observed between the left lingual gyrus and right fusiform gyrus as well as in the left precuneus.

Conclusion/Significance

Our results indicate the abnormalities of spontaneous activity in the right SPL and left lingual gyrus and their corresponding dysfunction of the brain circuits might be related to the pathophysiology of nCDSs.

When structure affects function--the need for partial volume effect correction in functional and resting state magnetic resonance imaging studies

Both functional and also more recently resting state magnetic resonance imaging have become established tools to investigate functional brain networks. Most studies use these tools to compare different populations without controlling for potential differences in underlying brain structure which might affect the functional measurements of interest. Here, we adapt a simulation approach combined with evaluation of real resting state magnetic resonance imaging data to investigate the potential impact of partial volume effects on established functional and resting state magnetic resonance imaging analyses. We demonstrate that differences in the underlying structure lead to a significant increase in detected functional differences in both types of analyses. Largest increases in functional differences are observed for highest signal-to-noise ratios and when signal with the lowest amount of partial volume effects is compared to any other partial volume effect constellation. In real data, structural information explains about 25% of within-subject variance observed in degree centrality – an established resting state connectivity measurement. Controlling this measurement for structural information can substantially alter correlational maps obtained in group analyses. Our results question current approaches of evaluating these measurements in diseased population with known structural changes without controlling for potential differences in these measurements.

Resting-state functional magnetic resonance imaging: review of neurosurgical applications

Recent research in brain imaging has highlighted the role of different neural networks in the resting state (ie, no task) in which the brain displays spontaneous low-frequency neuronal oscillations. These can be indirectly measured with resting-state functional magnetic resonance imaging, and functional connectivity can be inferred as the spatiotemporal correlations of this signal. This technique has proliferated in recent years and has allowed the noninvasive investigation of large-scale, distributed functional networks. In this review, we give a brief overview of resting-state networks and examine the use of resting-state functional magnetic resonance imaging in neurosurgical contexts, specifically with respect to neurooncology, epilepsy surgery, and deep brain stimulation. We discuss the advantages and disadvantages compared with task-based functional magnetic resonance imaging, the limitations of resting-state functional magnetic resonance imaging, and the emerging directions of this relatively new technology.

Studying brain organization via spontaneous fMRI signal

In recent years, some substantial advances in understanding human (and nonhuman) brain organization have emerged from a relatively unusual approach: the observation of spontaneous activity, and correlated patterns in spontaneous activity, in the "resting" brain. Most commonly, spontaneous neural activity is measured indirectly via fMRI signal in subjects who are lying quietly in the scanner, the so-called "resting state." This Primer introduces the fMRI-based study of spontaneous brain activity, some of the methodological issues active in the field, and some ways in which resting-state fMRI has been used to delineate aspects of area-level and supra-areal brain organization.

Close-range blast exposure is associated with altered functional connectivity in Veterans independent of concussion symptoms at time of exposure

Although there is emerging data on the effects of blast-related concussion (or mTBI) on cognition, the effects of blast exposure itself on the brain have only recently been explored. Toward this end, we examine functional connectivity to the posterior cingulate cortex, a primary region within the default mode network (DMN), in a cohort of 134 Iraq and Afghanistan Veterans characterized for a range of common military-associated comorbidities. Exposure to a blast at close range (<10 meters) was associated with decreased connectivity of bilateral primary somatosensory and motor cortices, and these changes were not different from those seen in participants with blast-related mTBI. These results remained significant when clinical factors such as sleep quality, chronic pain, or post traumatic stress disorder were included in the statistical model. In contrast, differences in functional connectivity based on concussion history and blast exposures at greater distances were not apparent. Despite the limitations of a study of this nature (e.g., assessments long removed from injury, self-reported blast history), these data demonstrate that blast exposure per se, which is prevalent among those who served in Iraq and Afghanistan, may be an important consideration in Veterans' health. It further offers a clinical guideline for determining which blasts (namely, those within 10 meters) are likely to lead to long-term health concerns and may be more accurate than using concussion symptoms alone.

What light have resting state fMRI studies shed on cognition and mood in Parkinson's disease?

Much remains unknown about non-motor symptoms of Parkinson’s disease (PD), which have variable occurrence, progression, and severity among patients. The existing suite of neuroimaging tools has yielded insight that cannot be garnered by traditional methods such as behavioral and post-mortem assessment. They provide information on brain activity and structure that is invaluable to understanding abnormalities associated with neurodegeneration in PD. Among these tools, functional magnetic resonance imaging (fMRI) is often favored for its safety and spatial resolution. Resting state fMRI research capitalizes on the wealth of information that the brain offers when a person is not performing a motor or cognitive task. It is also a good means to study impaired and heterogeneous populations, such as people with PD. The present article reviews research that applies resting state fMRI to the ongoing hunt for biomarkers of PD non-motor symptoms. Thus far, research in this subfield has focused on two of the most common and significant non-motor symptoms: cognitive impairment and depression. These studies support resting state fMRI as a valid and practical tool for the study of these symptoms, but discrepancies among findings highlight the importance of further research with standardized procedures.

Phonemic fluency and brain connectivity in age-related macular degeneration: a pilot study

Age-related macular degeneration (AMD), the leading cause of blindness in developed nations, has been associated with poor performance on tests of phonemic fluency. This pilot study sought to (1) characterize the relationship between phonemic fluency and resting-state functional brain connectivity in AMD patients and (2) determine whether regional connections associated with phonemic fluency in AMD patients were similarly linked to phonemic fluency in healthy participants. Behavior-based connectivity analysis was applied to resting-state, functional magnetic resonance imaging data from seven patients (mean age=79.9±7.5 years) with bilateral AMD who completed fluency tasks prior to imaging. Phonemic fluency was inversely related to the strength of functional connectivity (FC) among six pairs of brain regions, representing eight nodes: left opercular portion of inferior frontal gyrus (which includes Broca's area), left superior temporal gyrus (which includes part of Wernicke's area), inferior parietal lobe (bilaterally), right superior parietal lobe, right supramarginal gyrus, right supplementary motor area, and right precentral gyrus. The FC of these reference links was not related to phonemic fluency among 32 healthy individuals (16 younger adults, mean age=23.5±4.6 years and 16 older adults, mean age=68.3±3.4 years). Compared with healthy individuals, AMD patients exhibited higher mean connectivity within the reference links and within the default mode network, possibly reflecting compensatory changes to support performance in the setting of reduced vision. These findings are consistent with the hypothesis that phonemic fluency deficits in AMD reflect underlying brain changes that develop in the context of AMD.

Altered spontaneous neuronal activity of visual cortex and medial anterior cingulate cortex in treatment-naïve posttraumatic stress disorder

BACKGROUND

Although no more traumatic stimuli exists, a variety of symptoms are persisting in chronic Posttraumatic Stress Disorder (PTSD) patients. It is therefore necessary to explore the spontaneous brain activity of treatment-naïve PTSD patients during resting-state.

METHOD

Seventeen treatment-naïve PTSD patients and twenty traumatized controls were recruited and underwent a resting functional magnetic resonance imaging (Rs-fMRI) scan. The differences of regional brain spontaneous activity between the participants with and without PTSD were measured by Amplitude of Low-frequency fluctuation (ALFF). The relationship between the altered brain measurements and the symptoms of PTSD were analyzed.

RESULT

Compared to traumatized controls, the PTSD group showed significantly altered ALFF in many emotion-related brain regions, such as the medial anterior cingulate cortex (MACC), dorsolateral prefrontal cortex (DLPFC), insular (IC), middle temporal gyrus (MTG), and ventral posterior cingulate cortex (VPCC). Interestingly this is the first report of a hyperactive visual cortex (V1/V2) during resting-state in treatment-naïve PTSD patients. There were significant positive correlations between ALFF values in the bilateral visual cortex and re-experiencing or avoidance in PTSD. Negative correlation was observed between ALFF values in MACC and avoidance.

CONCLUSION

This study suggested that the visual cortex and the MACC may be involved in the characteristic symptoms of chronic PTSD, such as re-experiencing and avoidance. Future studies that focus on these areas of the brain are required, as alteration of these areas may act as a biomarker and could be targeted in future treatments for PTSD.

Resting state functional connectivity predicts neurofeedback response

Tailoring treatments to the specific needs and biology of individual patients—personalized medicine—requires delineation of reliable predictors of response. Unfortunately, these have been slow to emerge, especially in neuropsychiatric disorders. We have recently described a real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback protocol that can reduce contamination-related anxiety, a prominent symptom of many cases of obsessive-compulsive disorder (OCD). Individual response to this intervention is variable. Here we used patterns of brain functional connectivity, as measured by baseline resting-state fMRI (rs-fMRI), to predict improvements in contamination anxiety after neurofeedback training. Activity of a region of the orbitofrontal cortex (OFC) and anterior prefrontal cortex, Brodmann area (BA) 10, associated with contamination anxiety in each subject was measured in real time and presented as a neurofeedback signal, permitting subjects to learn to modulate this target brain region. We have previously reported both enhanced OFC/BA 10 control and improved anxiety in a group of subclinically anxious subjects after neurofeedback. Five individuals with contamination-related OCD who underwent the same protocol also showed improved clinical symptomatology. In both groups, these behavioral improvements were strongly correlated with baseline whole-brain connectivity in the OFC/BA 10, computed from rs-fMRI collected several days prior to neurofeedback training. These pilot data suggest that rs-fMRI can be used to identify individuals likely to benefit from rt-fMRI neurofeedback training to control contamination anxiety.

Resting state fMRI feature-based cerebral glioma grading by support vector machine

PURPOSE : Tumor grading plays an essential role in the optimal selection of solid tumor treatment. Noninvasive methods are needed for clinical grading of tumors. This study aimed to extract parameters of resting state blood oxygenation level-dependent functional magnetic resonance imaging (RS-fMRI) in the region of glioma and use the extracted features for tumor grading. METHODS : Tumor segmentation was performed with both conventional MRI and RS-fMRI. Four typical parameters, signal intensity difference ratio, signal intensity correlation (SIC), fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo), were defined to analyze tumor regions. Mann-Whitney [Formula: see text] test was employed to identify statistical difference of these four parameters between low-grade glioma (LGG) and high-grade glioma (HGG), respectively. Support vector machine (SVM) was employed to assess the diagnostic contributions of these parameters. RESULTS : Compared with LGG, HGG had more complex anatomical morphology and BOLD-fMRI features in the tumor region. SIC [Formula: see text], fALFF ([Formula: see text]) and ReHo ([Formula: see text]) were selected as features for classification according to the test [Formula: see text] value. The accuracy, sensitivity and specificity of SVM classification were better than 80, where SIC had the best classification accuracy (89). CONCLUSION : Parameters of RS-fMRI are effective to classify the tumor grade in glioma cases. The results indicate that this technique has clinical potential to serve as a complementary diagnostic tool.

Relationship between different surgical methods, hemorrhage position, hemorrhage volume, surgical timing, and treatment outcome of hypertensive intracerebral hemorrhage

BACKGROUND

The present study aimed to explore the relationship between surgical methods, hemorrhage position, hemorrhage volume, surgical timing and treatment outcome of hypertensive intracerebral hemorrhage (HICH).

METHODS

A total of 1 310 patients, who had been admitted to six hospitals from January 2004 to January 2008, were divided into six groups according to different surgical methods: craniotomy through bone flap (group A), craniotomy through a small bone window (group B), stereotactic drilling drainage (group C1 and group C2), neuron-endoscopy operation (group D) and external ventricular drainage (group E) in consideration of hemorrhage position, hemorrhage volume and clinical practice. A retrospective analysis was made of surgical timing and curative effect of the surgical methods.

RESULTS

The effectiveness rate of the methods was 74.12% for 1 310 patients after one-month follow-up. In this series, the disability rate was 44.82% 3-6 months after the operation. Among the 1 310 patients, 241 (18.40%) patients died after the operation. If hematoma volume was >80 mL and the operation was performed within 3 hours, the mortality rate of group A was significantly lower than that of groups B, C, D, and E (P<0.05). If hematoma volume was 50-80 mL and the operation was performed within 6-12 hours, the mortality rate of groups B and D was lower than that of groups A, C and E (P<0.05). If hematoma volume was 20-50 mL and the operation was performed within 6-24 hours, the mortality rate of group C was lower than that of groups A, B and D (P<0.05).

CONCLUSIONS

Craniotomy through a bone flap is suitable for patients with a large hematoma and hernia of the brain. Stereotactic drilling drainage is suggested for patients with hematoma volume less than 80 mL. The curative effect of HICH individualized treatment would be improved via the suitable selection of operation time and surgical method according to the position and volume of hemorrhage.

Toward literature-based feature selection for diagnostic classification: a meta-analysis of resting-state fMRI in depression

Information derived from functional magnetic resonance imaging (fMRI) during wakeful rest has been introduced as a candidate diagnostic biomarker in unipolar major depressive disorder (MDD). Multiple reports of resting state fMRI in MDD describe group effects. Such prior knowledge can be adopted to pre-select potentially discriminating features for diagnostic classification models with the aim to improve diagnostic accuracy. Purpose of this analysis was to consolidate spatial information about alterations of spontaneous brain activity in MDD, primarily to serve as feature selection for multivariate pattern analysis techniques (MVPA). Thirty two studies were included in final analyses. Coordinates extracted from the original reports were assigned to two categories based on directionality of findings. Meta-analyses were calculated using the non-additive activation likelihood estimation approach with coordinates organized by subject group to account for non-independent samples. Converging evidence revealed a distributed pattern of brain regions with increased or decreased spontaneous activity in MDD. The most distinct finding was hyperactivity/hyperconnectivity presumably reflecting the interaction of cortical midline structures (posterior default mode network components including the precuneus and neighboring posterior cingulate cortices associated with self-referential processing and the subgenual anterior cingulate and neighboring medial frontal cortices) with lateral prefrontal areas related to externally-directed cognition. Other areas of hyperactivity/hyperconnectivity include the left lateral parietal cortex, right hippocampus and right cerebellum whereas hypoactivity/hypoconnectivity was observed mainly in the left temporal cortex, the insula, precuneus, superior frontal gyrus, lentiform nucleus and thalamus. Results are made available in two different data formats to be used as spatial hypotheses in future studies, particularly for diagnostic classification by MVPA.

Resting-state blood oxygen level-dependent functional magnetic resonance imaging for presurgical planning

Resting-state functional MR imaging (rsfMR imaging) measures spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal and can be used to elucidate the brain's functional organization. It is used to simultaneously assess multiple distributed resting-state networks. Unlike task-based functional MR imaging, rsfMR imaging does not require task performance. This article presents a brief introduction of rsfMR imaging processing methods followed by a detailed discussion on the use of rsfMR imaging in presurgical planning. Example cases are provided to highlight the strengths and limitations of the technique.

Interhemispheric cerebral blood flow balance during recovery of motor hand function after ischemic stroke--a longitudinal MRI study using arterial spin labeling perfusion

BACKGROUND

Unilateral ischemic stroke disrupts the well balanced interactions within bilateral cortical networks. Restitution of interhemispheric balance is thought to contribute to post-stroke recovery. Longitudinal measurements of cerebral blood flow (CBF) changes might act as surrogate marker for this process.

OBJECTIVE

To quantify longitudinal CBF changes using arterial spin labeling MRI (ASL) and interhemispheric balance within the cortical sensorimotor network and to assess their relationship with motor hand function recovery.

METHODS

Longitudinal CBF data were acquired in 23 patients at 3 and 9 months after cortical sensorimotor stroke and in 20 healthy controls using pulsed ASL. Recovery of grip force and manual dexterity was assessed with tasks requiring power and precision grips. Voxel-based analysis was performed to identify areas of significant CBF change. Region-of-interest analyses were used to quantify the interhemispheric balance across nodes of the cortical sensorimotor network.

RESULTS

Dexterity was more affected, and recovered at a slower pace than grip force. In patients with successful recovery of dexterous hand function, CBF decreased over time in the contralesional supplementary motor area, paralimbic anterior cingulate cortex and superior precuneus, and interhemispheric balance returned to healthy control levels. In contrast, patients with poor recovery presented with sustained hypoperfusion in the sensorimotor cortices encompassing the ischemic tissue, and CBF remained lateralized to the contralesional hemisphere.

CONCLUSIONS

Sustained perfusion imbalance within the cortical sensorimotor network, as measured with task-unrelated ASL, is associated with poor recovery of dexterous hand function after stroke. CBF at rest might be used to monitor recovery and gain prognostic information.

Resting-state functional magnetic resonance imaging in hepatic encephalopathy: current status and perspectives

Hepatic encephalopathy (HE) is a neuropsychiatric syndrome which develops in patients with severe liver diseases and/or portal-systemic shunting. Minimal HE, the earliest manifestation of HE, has drawn increasing attention in the last decade. Minimal HE is associated with a series of brain functional changes, such as attention, working memory, and so on. Blood oxygen level dependent (BOLD) functional MRI (fMRI), especially resting-state fMRI has been used to explore the brain functional changes of HE, yielding important insights for understanding pathophysiological mechanisms and functional reorganization of HE. This paper briefly reviews the principles of BOLD fMRI, potential applications of resting-state fMRI with advanced post-processing algorithms such as regional homogeneity, amplitude of low frequency fluctuation, functional connectivity and future research perspective in this field.

Template based rotation: a method for functional connectivity analysis with a priori templates

Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,(1) a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium.

Differences in cerebral perfusion deficits in mild traumatic brain injury and depression using single-photon emission computed tomography

Background: Numerous studies have shown decreased perfusion in the prefrontal cortex following mild traumatic brain injury (mTBI). However, similar hypoperfusion can also be observed in depression. Given the high prevalence of depressive symptoms following mTBI, it is unclear to what extent depression influences hypoperfusion in TBI.

Methods: Mild TBI patients without depressive symptoms (mTBI-noD, n = 39), TBI patients with depressive symptoms (mTBI-D, n = 13), and 15 patients with major depressive disorder (MDD), but no TBI were given 99m T-ECD single-photon emission computed tomography (SPECT) scans within 2 weeks of injury. All subjects completed tests of information processing speed, complex attention, and executive functioning, and a self-report questionnaire measuring symptoms of psychological distress. Between-group comparisons of quantified SPECT perfusion were undertaken using univariate and multivariate (partial least squares) analyses.

Results: mTBI-D and mTBI-noD groups did not differ in terms of cerebral perfusion. However, patients with MDD showed hypoperfusion compared to both TBI groups in several frontal (orbitofrontal, middle frontal, and superior frontal cortex), superior temporal, and posterior cingulate regions. The mTBI-D group showed poorer performance on a measure of complex attention and working memory compared to both the mTBI-noD and MDD groups.

Conclusion: These results suggest that depressive symptoms do not affect SPECT perfusion in the sub-acute phase following a mild TBI. Conversely, MDD is associated with hypoperfusion primarily in frontal regions.

Intrinsic brain networks normalize with treatment in pediatric complex regional pain syndrome

Pediatric complex regional pain syndrome (P-CRPS) offers a unique model of chronic neuropathic pain as it either resolves spontaneously or through therapeutic interventions in most patients. Here we evaluated brain changes in well-characterized children and adolescents with P-CRPS by measuring resting state networks before and following a brief (median = 3 weeks) but intensive physical and psychological treatment program, and compared them to matched healthy controls. Differences in intrinsic brain networks were observed in P-CRPS compared to controls before treatment (disease state) with the most prominent differences in the fronto-parietal, salience, default mode, central executive, and sensorimotor networks. Following treatment, behavioral measures demonstrated a reduction of symptoms and improvement of physical state (pain levels and motor functioning). Correlation of network connectivities with spontaneous pain measures pre- and post-treatment indicated concomitant reductions in connectivity in salience, central executive, default mode and sensorimotor networks (treatment effects). These results suggest a rapid alteration in global brain networks with treatment and provide a venue to assess brain changes in CRPS pre- and post-treatment, and to evaluate therapeutic effects.

On the generalizability of resting-state fMRI machine learning classifiers

Machine learning classifiers have become increasingly popular tools to generate single-subject inferences from fMRI data. With this transition from the traditional group level difference investigations to single-subject inference, the application of machine learning methods can be seen as a considerable step forward. Existing studies, however, have given scarce or no information on the generalizability to other subject samples, limiting the use of such published classifiers in other research projects. We conducted a simulation study using publicly available resting-state fMRI data from the 1000 Functional Connectomes and COBRE projects to examine the generalizability of classifiers based on regional homogeneity of resting-state time series. While classification accuracies of up to 0.8 (using sex as the target variable) could be achieved on test datasets drawn from the same study as the training dataset, the generalizability of classifiers to different study samples proved to be limited albeit above chance. This shows that on the one hand a certain amount of generalizability can robustly be expected, but on the other hand this generalizability should not be overestimated. Indeed, this study substantiates the need to include data from several sites in a study investigating machine learning classifiers with the aim of generalizability.

Functional connectivity changes between parietal and prefrontal cortices in primary insomnia patients: evidence from resting-state fMRI

Background

Primary insomnia can severely impair daytime function by disrupting attention and working memory and imposes a danger to self and others by increasing the risk of accidents. We speculated that the neurobiological changes impeding working memory in primary insomnia patients would be revealed by resting-state functional MRI (R-fMRI), which estimates the strength of cortical pathways by measuring local and regional correlations in blood oxygen level dependent (BOLD) signs independent of specific task demands.

Methods

We compared the R-fMRI activity patterns of 15 healthy controls to 15 primary insomnia patients (all 30 participants were right-handed) using a 3.0 T MRI scanner. The SPM8 and REST1.7 software packages were used for preprocessing and analysis. Activity was expressed relative to the superior parietal lobe (SPL, the seed region) to reveal differences in functional connectivity to other cortical regions implicated in spatial working memory.

Result

In healthy controls, bilateral SPL activity was associated with activity in the posterior cingulate gyrus, precuneus, ventromedial prefrontal cortex, and superior frontal gyrus, indicating functional connectivity between these regions. Strong functional connectivity between the SPL and bilateral pre-motor cortex, bilateral supplementary motor cortex, and left dorsolateral prefrontal cortex was observed in both the control group and the primary insomnia group. However, the strength of several other functional connectivity pathways to the SPL exhibited significant group differences. Compared to healthy controls, connectivity in the primary insomnia group was stronger between the bilateral SPL and the right ventral anterior cingulate cortex, left ventral posterior cingulate cortex, right splenium of the corpus callosum, right pars triangularis (right inferior frontal gyrus/Broca’s area), and right insular lobe, while connectivity was weaker between the SPL and right superior frontal gyrus (dorsolateral prefrontal cortex).

Conclusion

Primary insomnia appears to alter the functional connectivity between the parietal and frontal lobes, cortical structures critical for spatial and verbal working memory.

Preoperative mapping of the sensorimotor cortex: comparative assessment of task-based and resting-state FMRI

Resting state fMRI (rs-fMRI) has recently been considered as a possible complement or alternative to task-based fMRI (tb-fMRI) for presurgical mapping. However, evidence of its usefulness remains scant, because existing studies have investigated relatively small samples and focused primarily on qualitative evaluation. The aim of this study is to investigate the clinical usefulness of rs-fMRI in the context of presurgical mapping of motor functions, and in particular to determine the degree of correspondence with tb-fMRI which, while not a gold-standard, is commonly used in preoperative setting. A group of 13 patients with lesions close to the sensorimotor cortex underwent rs-fMRI and tb-fMRI to localize the hand, foot and mouth motor areas. We assessed quantitatively the degree of correspondence between multiple rs-fMRI analyses (independent-component and seed-based analyses) and tb-fMRI, with reference to sensitivity and specificity of rs-fMRI with respect to tb-fMRI, and centre-of-mass distances. Agreement with electro-cortical stimulation (ECS) was also investigated, and a traditional map thresholding approach based on agreement between two experienced operators was compared to an automatic threshold determination method. Rs-fMRI can localize the sensorimotor cortex successfully, providing anatomical specificity for hand, foot and mouth motor subregions, in particular with seed-based analyses. Agreement with tb-fMRI was only partial and rs-fMRI tended to provide larger patterns of correlated activity. With respect to the ECS data available, rs-fMRI and tb-fMRI performed comparably, even though the shortest distance to stimulation points was observed for the latter. Notably, the results of both were on the whole robust to thresholding procedure. Localization performed by rs-fMRI is not equivalent to tb-fMRI, hence rs-fMRI cannot be considered as an outright replacement for tb-fMRI. Nevertheless, since there is significant agreement between the two techniques, rs-fMRI can be considered with caution as a potential alternative to tb-fMRI when patients are unable to perform the task.

Intrinsic functional connectivity networks in healthy elderly subjects: a multiparametric approach with structural connectivity analysis

Intrinsic functional connectivity magnetic resonance imaging (iFCMRI) provides an encouraging approach for mapping large-scale intrinsic connectivity networks (ICNs) in the “resting” brain. Structural connections as measured by diffusion tensor imaging (DTI) are a major constraint on the identified ICNs. This study aimed at the combined investigation of ten well-defined ICNs in healthy elderly subjects at single subject level as well as at the group level, together with the underlying structural connectivity. IFCMRI and DTI data were acquired in twelve subjects (68 ± 7 years) at a 3T scanner and were studied using the tensor imaging and fiber tracking software package. The seed-based iFCMRI analysis approach was comprehensively performed with DTI analysis, following standardized procedures including an 8-step processing of iFCMRI data. Our findings demonstrated robust ICNs at the single subject level and conclusive brain maps at the group level in the healthy elderly sample, supported by the complementary fiber tractography. The findings demonstrated here provide a methodological framework for future comparisons of pathological (e.g., neurodegenerative) conditions with healthy controls on the basis of multiparametric functional connectivity mapping.

fMRI for mapping language networks in neurosurgical cases

Evaluating language has been a long-standing application in functional magnetic resonance imaging (fMRI) studies, both in research and clinical circumstances, and still provides challenges. Localization of eloquent areas is important in neurosurgical cases, so that there is least possible damage to these areas during surgery, maintaining their function postoperatively, therefore providing good quality of life to the patient. Preoperative fMRI study is a non-invasive tool to localize the eloquent areas, including language, with other traditional methods generally used being invasive and at times perilous. In this article, we describe methods and various paradigms to study the language areas, in clinical neurosurgical cases, along with illustrations of cases from our institute.

A SVM-based quantitative fMRI method for resting-state functional network detection

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 an 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.

Network-based biomarkers in Alzheimer's disease: review and future directions

By 2050 it is estimated that the number of worldwide Alzheimer's disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.

Neuroimaging, cancer, and cognition: state of the knowledge

OBJECTIVES

To review neuroimaging research concerning cancer- and cancer treatment-related changes in brain structure and function, clinical perspectives, and future directions.

DATA SOURCES

Peer-reviewed literature.

CONCLUSION

Cancer and chemotherapy are associated with cerebral structural and functional alterations in breast cancer patients that may persist for years; many of these changes are correlated with cognitive complaints or performance. In other cancers there is some evidence that metabolism is altered by cancer, but more research is needed.

IMPLICATIONS FOR NURSING PRACTICE

Understanding the role of neuroimaging is important to identify the basis of cognitive changes associated with cancer and cancer treatment.

Functional connectivity within the default mode network is associated with saccadic accuracy in Parkinson's disease: a resting-state FMRI and videooculographic study

In addition to the skeleto-motor deficits, patients with Parkinson's disease (PD) frequently present with oculomotor dysfunctions such as impaired smooth pursuit and saccadic abnormalities. There is increasing evidence for an impaired cortical function to be responsible for oculomotor deficits that are associated with lack of inhibitory control; however, these pathomechanisms still remain poorly understood. By means of "task-free" resting-state functional magnetic resonance imaging (rs-fMRI), functional connectivity changes in PD within the default mode network (DMN) have been reported. The aim of this study was to investigate whether altered functional connectivity within the DMN was correlated with oculomotor parameter changes in PD. Twelve PD patients and 13 matched healthy controls underwent rs-fMRI at 1.5 T and videooculography (VOG) using Eye-Link-System. Rs-fMRI seed-based region-to-region connectivity analysis was performed, including medial prefrontal cortex (mPFC), medial temporal lobe (MTL), posterior cingulate cortex (PCC), and hippocampal formation (HF); while VOG examination comprised ocular reactive saccades, smooth pursuit, and executive tests. Rs-fMRI analysis demonstrated a decreased region-to-region functional connectivity between mPFC and PCC as well as increased connectivity between bilateral HF in PD compared with controls. In VOG, patients and controls differed in terms of executive tests outcome, smooth pursuit eye movement, and visually guided reactive saccades but not in peak eye velocity. A significant relationship was observed between saccadic accuracy and functional connectivity strengths between MTL and PCC. These results suggest that PD-associated changes of DMN connectivity are correlated with PD-associated saccadic hypometria, in particular in the vertical direction.

On development of functional brain connectivity in the young brain

Our brain is a complex network of structurally and functionally interconnected regions, shaped to efficiently process and integrate information. The development from a brain equipped with basic functionalities to an efficient network facilitating complex behavior starts during gestation and continues into adulthood. Resting-state functional MRI (rs-fMRI) enables the examination of developmental aspects of functional connectivity (FC) and functional brain networks. This review will discuss changes observed in the developing brain on the level of network FC from a gestational age of 20 weeks onwards. We discuss findings of resting-state fMRI studies showing that functional network development starts during gestation, creating a foundation for each of the resting-state networks (RSNs) to be established. Visual and sensorimotor areas are reported to develop first, with other networks, at different rates, increasing both in network connectivity and size over time. Reaching childhood, marked fine-tuning and specialization takes place in the regions necessary for higher-order cognitive functions.

Multivariate classification of blood oxygen level-dependent FMRI data with diagnostic intention: a clinical perspective

SUMMARY

There has been a recent upsurge of reports about applications of pattern-recognition techniques from the field of machine learning to functional MR imaging data as a diagnostic tool for systemic brain disease or psychiatric disorders. Entities studied include depression, schizophrenia, attention deficit hyperactivity disorder, and neurodegenerative disorders like Alzheimer dementia. We review these recent studies which-despite the optimism from some articles-predominantly constitute explorative efforts at the proof-of-concept level. There is some evidence that, in particular, support vector machines seem to be promising. However, the field is still far from real clinical application, and much work has to be done regarding data preprocessing, model optimization, and validation. Reporting standards are proposed to facilitate future meta-analyses or systematic reviews.

Neuroimaging biomarkers for epilepsy: advances and relevance to glial cells

Glial cells play an important role in normal brain function and emerging evidence would suggest that their dysfunction may be responsible for some epileptic disease states. Neuroimaging of glial cells is desirable, but there are no clear methods to assess neither their function nor localization. Magnetic resonance imaging (MRI) is now part of a standardized epilepsy imaging protocol to assess patients. Structural volumetric and T2-weighted imaging changes can assist in making a positive diagnosis in a majority of patients. The alterations reported in structural and T2 imaging is predominantly thought to reflect early neuronal loss followed by glial hypertrophy. MR spectroscopy for myo-inositol is a being pursued to identify glial alterations along with neuronal markers. Diffusion weighted imaging (DWI) is ideal for acute epileptiform events, but is not sensitive to either glial cells or neuronal long-term changes found in epilepsy. However, DWI variants such as diffusion tensor imaging or q-space imaging may shed additional light on aberrant glial function in the future. The sensitivity and specificity of PET radioligands, including those targeting glial cells (translocator protein) hold promise in being able to image glial cells. As the role of glial function/dysfunction in epilepsy becomes more apparent neuroimaging methods will evolve to assist the clinician and researcher in visualizing their location and function.

Resting-state functional MRI in pediatric epilepsy surgery

Resting-state functional MRI (rs-fMRI) identifies resting-state networks (RSN) in the human brain by analyzing the connectivity of anatomically remote neuronal populations with synchronous low-frequency fluctuation in blood oxygen level-dependent (BOLD) signal. Network analysis has informed the understanding of functional brain organization and is beginning to reveal the impact that neurological disorders such as epilepsy may have on the developing cerebral cortex. Among children undergoing epilepsy surgery, mapping the brain networks supporting language, sensorimotor and visual function is a critical part of the preoperative evaluation. However, task-based functional mapping techniques are particularly difficult in immature patients and those with severe impairment. Functional mapping of RSN is a promising tool that may help circumvent the challenges of adequate cooperation and limited abilities of developmentally disabled children to perform age-appropriate functions. We discuss the current methodology of rs-fMRI in the pediatric population, review the literature of rs-fMRI in pediatric epilepsy and present our experience of using rs-fMRI for functional network mapping in children undergoing epilepsy surgery.