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Desai, Virendra R., Aditya Vedantam, Sandi K. Lam, Lucia Mirea, Stephen T. Foldes, Daniel J. Curry, P. David Adelson, Angus A. Wilfong, and Varina L. Boerwinkle. "Language lateralization with resting-state and task-based functional MRI in pediatric epilepsy." Journal of Neurosurgery: Pediatrics 23, no. 2 (February 2019): 171–77. http://dx.doi.org/10.3171/2018.7.peds18162.
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OBJECTIVEDetermining language laterality in patients with intractable epilepsy is important in operative planning. Wada testing is the gold standard, but it has a risk of stroke. Both Wada and task-based functional MRI (tb-fMRI) require patient cooperation. Recently, resting-state fMRI (rs-fMRI) has been explored for language lateralization. In the present study, the correlation between rs-fMRI and tb-fMRI in language lateralization is estimated in a pediatric population with intractable epilepsy.METHODSrs-fMRI and tb-fMRI language lateralization testing performed as part of epilepsy surgery evaluation was retrospectively reviewed.RESULTSTwenty-nine patients underwent rs-fMRI and tb-fMRI; a total of 38 rs-fMRI studies and 30 tb-fMRI studies were obtained. tb-fMRI suggested left dominance in 25 of 30 cases (83%), right in 3 (10%), and in 2 (7%) the studies were nondiagnostic. In rs-fMRI, 26 of 38 studies (68%) suggested left dominance, 3 (8%) right dominance, 6 (16%) bilateral, and 3 (8%) were nondiagnostic. When tb-fMRI lateralized to the left hemisphere (25 cases), rs-fMRI was lateralized to the left in 23 patients (92%) and it was bilateral/equal in 2 (8%). When tb-fMRI lateralized to the right (3 cases), rs-fMRI lateralized to the right in all cases (100%). The overall concordance rate was 0.93 (95% CI 0.76–0.99) when considering cases with tb-fMRI and rs-fMRI performed within 6 months of each other, and tb-fMRI results were not nondiagnostic.CONCLUSIONSrs-fMRI significantly correlated with tb-fMRI in lateralizing language and suggests the potential role for identifying hemispheric dominance via rs-fMRI. Further investigation and validation studies are warranted.
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Zacà, Domenico, Jorge Jovicich, Francesco Corsini, Umberto Rozzanigo, Franco Chioffi, and Silvio Sarubbo. "ReStNeuMap: a tool for automatic extraction of resting-state functional MRI networks in neurosurgical practice." Journal of Neurosurgery 131, no. 3 (September 2019): 764–71. http://dx.doi.org/10.3171/2018.4.jns18474.
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OBJECTIVEResting-state functional MRI (rs-fMRI) represents a promising and cost-effective alternative to task-based fMRI for presurgical mapping. However, the lack of clinically streamlined and reliable rs-fMRI analysis tools has prevented wide adoption of this technique. In this work, the authors introduce an rs-fMRI processing pipeline (ReStNeuMap) for automatic single-patient rs-fMRI network analysis.METHODSThe authors provide a description of the rs-fMRI network analysis steps implemented in ReStNeuMap and report their initial experience with this tool after performing presurgical mapping in 6 patients. They verified the spatial agreement between rs-fMRI networks derived by ReStNeuMap and localization of activation with intraoperative direct electrical stimulation (DES).RESULTSThe authors automatically extracted rs-fMRI networks including eloquent cortex in spatial proximity with the resected lesion in all patients. The distance between DES points and corresponding rs-fMRI networks was less than 1 cm in 78% of cases for motor, 100% of cases for visual, 87.5% of cases for language, and 100% of cases for speech articulation mapping.CONCLUSIONSThe authors’ initial experience with ReStNeuMap showed good spatial agreement between presurgical rs-fMRI predictions and DES findings during awake surgery. The availability of the rs-fMRI analysis tools for clinicians aiming to perform noninvasive mapping of brain functional networks may extend its application beyond surgical practice.
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Lang, ST, B. Goodyear, J. Kelly, and P. Federico. "Neurophysiology (fMRI)." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 42, S1 (May 2015): S38. http://dx.doi.org/10.1017/cjn.2015.173.
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Background: Resting state functional MRI (rs-fMRI) provides many advantages to task-based fMRI in neurosurgical populations, foremost of which is the lack of the need to perform a task. Many networks can be identified by rs-fMRI in a single period of scanning. Despite the advantages, there is a paucity of literature on rs-fMRI in neurosurgical populations. Methods: Eight patients with tumours near areas traditionally considered as eloquent cortex participated in a five minute rs-fMRI scan. Resting-state fMRI data underwent Independent Component Analysis (ICA) using the Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) toolbox in FSL. Resting state networks (RSNs) were identified on a visual basis. Results: Several RSNs, including language (N=7), sensorimotor (N=7), visual (N=7), default mode network (N=8) and frontoparietal attentional control (n=7) networks were readily identifiable using ICA of rs-fMRI data. Conclusion: These pilot data suggest that ICA applied to rs-fMRI data can be used to identify motor and language networks in patients with brain tumours. We have also shown that RSNs associated with cognitive functioning, including the default mode network and the frontoparietal attentional control network can be identified in individual subjects with brain tumours. While preliminary, this suggests that rs-fMRI may be used pre-operatively to localize areas of cortex important for higher order cognitive functioning.
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Medina, Jean Paul, Anna Nigri, Mario Stanziano, Ludovico D’Incerti, Davide Sattin, Stefania Ferraro, Davide Rossi Sebastiano, et al. "Resting-State fMRI in Chronic Patients with Disorders of Consciousness: The Role of Lower-Order Networks for Clinical Assessment." Brain Sciences 12, no. 3 (March 7, 2022): 355. http://dx.doi.org/10.3390/brainsci12030355.
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Resting-state fMRI (rs-fMRI) is a widely used technique to investigate the residual brain functions of patients with Disorders of Consciousness (DoC). Nonetheless, it is unclear how the networks that are more associated with primary functions, such as the sensory–motor, medial/lateral visual and auditory networks, contribute to clinical assessment. In this study, we examined the rs-fMRI lower-order networks alongside their structural MRI data to clarify the corresponding association with clinical assessment. We studied 109 chronic patients with DoC and emerged from DoC with structural MRI and rs-fMRI: 65 in vegetative state/unresponsive wakefulness state (VS/UWS), 34 in minimally conscious state (MCS) and 10 with severe disability. rs-fMRI data were analyzed with independent component analyses and seed-based analyses, in relation to structural MRI and clinical data. The results showed that VS/UWS had fewer networks than MCS patients and the rs-fMRI activity in each network was decreased. Visual networks were correlated to the clinical status, and in cases where no clinical response occurred, rs-fMRI indicated distinctive networks conveying information in a similar way to other techniques. The information provided by single networks was limited, whereas the four networks together yielded better classification results, particularly when the model included rs-fMRI and structural MRI data (AUC = 0.80). Both quantitative and qualitative rs-fMRI analyses yielded converging results; vascular etiology might confound the results, and disease duration generally reduced the number of networks observed. The lower-order rs-fMRI networks could be used clinically to support and corroborate visual function assessments in DoC.
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Sacco, Rosaria, Simona Bonavita, Fabrizio Esposito, Gioacchino Tedeschi, and Antonio Gallo. "The Contribution of Resting State Networks to the Study of Cortical Reorganization in MS." Multiple Sclerosis International 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/857807.
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Resting State fMRI (RS-fMRI) represents an emerging and powerful tool to explore brain functional connectivity (FC) changes associated with neurologic disorders. Compared to activation/task-related fMRI, RS-fMRI has the advantages that (i) BOLD fMRI signals are self-generated and independent of subject’s performance during the task and (ii) a single dataset is sufficient to extract a set of RS networks (RSNs) that allows to explore whole brain FC. According to these features RS-fMRI appears particularly suitable for the study of FC changes related to multiple sclerosis (MS). In the present review we will first give a brief description of RS-fMRI methodology and then an overview of most relevant studies conducted so far in MS by using this approach. The most interesting results, in particular, regard the default-mode network (DMN), whose FC changes have been correlated with cognitive status of MS patients, and the visual RSN (V-RSN) whose FC changes have been correlated with visual recovery after optic neuritis. The executive control network (ECN), the lateralized frontoparietal network (FPN), and the sensory motor network (SMN) have also been investigated in MS, showing significant FC rearrangements. All together, RS-fMRI studies conducted so far in MS suggest that prominent RS-FC changes can be detected in many RSNs and correlate with clinical and/or structural MRI measures. Future RS-fMRI studies will further clarify the dynamics and clinical impact of RSNs changes in MS.
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Cieri, Filippo, and Roberto Esposito. "Neuroaging through the Lens of the Resting State Networks." BioMed Research International 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/5080981.
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Resting state functional magnetic resonance imaging (rs-fMRI) allows studying spontaneous brain activity in absence of task, recording changes of Blood Oxygenation Level Dependent (BOLD) signal. rs-fMRI enables identification of brain networks also called Resting State Networks (RSNs) including the most studied Default Mode Network (DMN). The simplicity and speed of execution make rs-fMRI applicable in a variety of normal and pathological conditions. Since it does not require any task, rs-fMRI is particularly useful for protocols on patients, children, and elders, increasing participant’s compliance and reducing intersubjective variability due to the task performance. rs-fMRI has shown high sensitivity in identification of RSNs modifications in several diseases also in absence of structural modifications. In this narrative review, we provide the state of the art of rs-fMRI studies about physiological and pathological aging processes. First, we introduce the background of resting state; then we review clinical findings provided by rs-fMRI in physiological aging, Mild Cognitive Impairment (MCI), Alzheimer Dementia (AD), and Late Life Depression (LLD). Finally, we suggest future directions in this field of research and its potential clinical applications.
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Li, Kai, Wen Su, Shu-Hua Li, Ying Jin, and Hai-Bo Chen. "Resting State fMRI: A Valuable Tool for Studying Cognitive Dysfunction in PD." Parkinson's Disease 2018 (2018): 1–5. http://dx.doi.org/10.1155/2018/6278649.
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Cognitive impairment is a common disabling symptom in PD. Unlike motor symptoms, the mechanism underlying cognitive dysfunction in Parkinson’s disease (PD) remains unclear and may involve multiple pathophysiological processes. Resting state functional magnetic resonance imaging (rs-fMRI) is a fast-developing research field, and its application in cognitive impairments in PD is rapidly growing. In this review, we summarize rs-fMRI studies on cognitive function in PD and discuss the strong potential of rs-fMRI in this area. rs-fMRI can help reveal the pathophysiology of cognitive symptoms in PD, facilitate early identification of PD patients with cognitive impairment, distinguish PD dementia from dementia with Lewy bodies, and monitor and guide treatment for cognitive impairment in PD. In particular, ongoing and future longitudinal studies would enhance the ability of rs-fMRI in predicting PD dementia. In combination with other modalities such as positron emission tomography, rs-fMRI could give us more information on the underlying mechanism of cognitive deficits in PD.
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Liu, Xiaoxue, Jianrui Li, Qiang Xu, Qirui Zhang, Xian Zhou, Hao Pan, Nan Wu, Guangming Lu, and Zhiqiang Zhang. "RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas." Cancers 14, no. 12 (June 7, 2022): 2818. http://dx.doi.org/10.3390/cancers14122818.
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Rs-fMRI can provide rich information about functional processes in the brain with a large array of imaging parameters and is also suitable for investigating the biological processes in cerebral gliomas. We aimed to propose an imaging analysis method of RP-Rs-fMRIomics by adopting omics analysis on rs-fMRI with exhaustive regional parameters and subsequently estimating its feasibility on the prediction diagnosis of gliomas. In this retrospective study, preoperative rs-fMRI data were acquired from patients confirmed with diffuse gliomas (n = 176). A total of 420 features were extracted through measuring 14 regional parameters of rs-fMRI as much as available currently in 10 specific narrow frequency bins and three parts of gliomas. With a randomly split training and testing dataset (ratio 7:3), four classifiers were implemented to construct and optimize RP-Rs-fMRIomics models for predicting glioma grade, IDH status and Karnofsky Performance Status scores. The RP-Rs-fMRIomics models (AUROC 0.988, 0.905, 0.801) were superior to the corresponding traditional single rs-fMRI index (AUROC 0.803, 0.731, 0.632) in predicting glioma grade, IDH and survival. The RP-Rs-fMRIomics analysis, featuring high interpretability, was competitive for prediction of glioma grading, IDH genotype and prognosis. The method expanded the clinical application of rs-fMRI and also contributed a new imaging analysis for brain tumor research.
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Canario, Edgar, Donna Chen, and Bharat Biswal. "A review of resting-state fMRI and its use to examine psychiatric disorders." Psychoradiology 1, no. 1 (March 2021): 42–53. http://dx.doi.org/10.1093/psyrad/kkab003.
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Abstract Resting-state fMRI (rs-fMRI) has emerged as an alternative method to study brain function in human and animal models. In humans, it has been widely used to study psychiatric disorders including schizophrenia, bipolar disorder, autism spectrum disorders, and attention deficit hyperactivity disorders. In this review, rs-fMRI and its advantages over task based fMRI, its currently used analysis methods, and its application in psychiatric disorders using different analysis methods are discussed. Finally, several limitations and challenges of rs-fMRI applications are also discussed.
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Smirnov, A. S., M. G. Sharaev, T. V. Melnikova-Pitskhelauri, V. Yu Zhukov, A. E. Bikanov, E. V. Sharova, E. L. Pogosbekyan, et al. "Resting state fMRI in pre-surgical brain mapping. Literature review." Medical Visualization, no. 5 (October 28, 2018): 6–13. http://dx.doi.org/10.24835/1607-0763-2018-5-6-13.
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Today, functional magnetic resonance imaging (fMRI) allows to plan surgery based on the topography of functionally important areas of the human brain cortex and tumor. This method can complement the surgical strategy with significant clinical information. The stimulus-dependent fMRI with motor and language paradigms is generally used for preoperative planning. The study outcome depends on the patient's ability to perform tasks paradigm, which is broken in brain tumors. In an attempt to overcome this problem, resting-state fMRI (rs-fMRI) is used for brain mapping. Rs-fMRI is based on the measurement of spontaneous fluctuations of the BOLD signal (blood oxygen level-dependent), representing the functional structure of the brain. In contrast to stimulus-dependent fMRI, rs-fMRI provides more complete information about functional architecture of the brain. rs-fMRI is used in conditions where the results of stimulusdependent fMRI may be falsely positive or in the absence of the possibility of its implementation. In aggregate, both methods significantly expand the efficiency and specificity of preoperative planning.
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Pur, Daiana R., Roy Eagleson, Marcus Lo, Michael T. Jurkiewicz, Andrea Andrade, and Sandrine de Ribaupierre. "Presurgical brain mapping of the language network in pediatric patients with epilepsy using resting-state fMRI." Journal of Neurosurgery: Pediatrics 27, no. 3 (March 2021): 259–68. http://dx.doi.org/10.3171/2020.8.peds20517.
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OBJECTIVEEpilepsy affects neural processing and often causes intra- or interhemispheric language reorganization, rendering localization solely based on anatomical landmarks (e.g., Broca’s area) unreliable. Preoperative brain mapping is necessary to weigh the risk of resection with the risk of postoperative deficit. However, the use of conventional mapping methods (e.g., somatosensory stimulation, task-based functional MRI [fMRI]) in pediatric patients is technically difficult due to low compliance and their unique neurophysiology. Resting-state fMRI (rs-fMRI), a “task-free” technique based on the neural activity of the brain at rest, has the potential to overcome these limitations. The authors hypothesized that language networks can be identified from rs-fMRI by applying functional connectivity analyses.METHODSCases in which both task-based fMRI and rs-fMRI were acquired as part of the preoperative clinical protocol for epilepsy surgery were reviewed. Task-based fMRI consisted of 2 language tasks and 1 motor task. Resting-state fMRI data were acquired while the patients watched an animated movie and were analyzed using independent component analysis (i.e., data-driven method). The authors extracted language networks from rs-fMRI data by performing a similarity analysis with functionally defined language network templates via a template-matching procedure. The Dice coefficient was used to quantify the overlap.RESULTSThirteen children underwent conventional task-based fMRI (e.g., verb generation, object naming), rs-fMRI, and structural imaging at 1.5T. The language components with the highest overlap with the language templates were identified for each patient. Language lateralization results from task-based fMRI and rs-fMRI mapping were comparable, with good concordance in most cases. Resting-state fMRI–derived language maps indicated that language was on the left in 4 patients (31%), on the right in 5 patients (38%), and bilateral in 4 patients (31%). In some cases, rs-fMRI indicated a more extensive language representation.CONCLUSIONSResting-state fMRI–derived language network data were identified at the patient level using a template-matching method. More than half of the patients in this study presented with atypical language lateralization, emphasizing the need for mapping. Overall, these data suggest that this technique may be used to preoperatively identify language networks in pediatric patients. It may also optimize presurgical planning of electrode placement and thereby guide the surgeon’s approach to the epileptogenic zone.
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Choi, Uk-Su, Yul-Wan Sung, and Seiji Ogawa. "Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics." Brain Sciences 13, no. 1 (December 20, 2022): 8. http://dx.doi.org/10.3390/brainsci13010008.
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Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.
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Kashyap, Amrit, and Shella Keilholz. "Brain network constraints and recurrent neural networks reproduce unique trajectories and state transitions seen over the span of minutes in resting-state fMRI." Network Neuroscience 4, no. 2 (January 2020): 448–66. http://dx.doi.org/10.1162/netn_a_00129.
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Large-scale patterns of spontaneous whole-brain activity seen in resting-state functional magnetic resonance imaging (rs-fMRI) are in part believed to arise from neural populations interacting through the structural network (Honey, Kötter, Breakspear, & Sporns, 2007 ). Generative models that simulate this network activity, called brain network models (BNM), are able to reproduce global averaged properties of empirical rs-fMRI activity such as functional connectivity (FC) but perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole-brain data (Cabral, Kringelbach, & Deco, 2017 ; Kashyap & Keilholz, 2019 ). The manuscript demonstrates that by using recurrent neural networks, it can fit the BNM in a novel way to the rs-fMRI data and predict large amounts of variance between subsequent measures of rs-fMRI data. Simulated data also contain unique repeating trajectories observed in rs-fMRI, called quasiperiodic patterns (QPP), that span 20 s and complex state transitions observed using k-means analysis on windowed FC matrices (Allen et al., 2012 ; Majeed et al., 2011 ). Our approach is able to estimate the manifold of rs-fMRI dynamics by training on generating subsequent time points, and it can simulate complex resting-state trajectories better than the traditional generative approaches.
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Epstein, Russell A., Whitney E. Parker, and Alana M. Feiler. "Two Kinds of fMRI Repetition Suppression? Evidence for Dissociable Neural Mechanisms." Journal of Neurophysiology 99, no. 6 (June 2008): 2877–86. http://dx.doi.org/10.1152/jn.90376.2008.
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Repetition suppression (RS) is a reduction of neural response that is often observed when stimuli are presented more than once. Many functional magnetic resonance imaging (fMRI) studies have exploited RS to probe the sensitivity of cortical regions to variations in different stimulus dimensions; however, the neural mechanisms underlying fMRI-RS are not fully understood. Here we test the hypothesis that long-interval (between-trial) and short-interval (within-trial) RS effects are caused by distinct and independent neural mechanisms. Subjects were scanned while viewing visual scenes that were repeated over both long and short intervals. Within the parahippocampal place area (PPA) and other brain regions, suppression effects relating to both long- and short-interval repetition were observed. Critically, two sources of evidence indicated that these effects were engendered by different underlying mechanisms. First, long- and short-interval RS effects were entirely noninteractive even although they were measured within the same set of trials during which subjects performed a constant behavioral task, thus fulfilling the formal requirements for a process dissociation. Second, long- and short-interval RS were differentially sensitive to viewpoint: short-interval RS was only significant when scenes were repeated from the same viewpoint while long-interval RS less viewpoint-dependent. Taken together, these results indicate that long- and short-interval fMRI-RS are mediated by different neural mechanisms that independently modulate the overall fMRI signal. These findings have important implications for understanding the results of studies that use fMRI-RS to explore representational spaces.
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Li, Xiaoling, Lina Cai, Xiaoxu Jiang, Xiaohui Liu, Jingxian Wang, Tiansong Yang, and Feng Wang. "Resting-State fMRI in Studies of Acupuncture." Evidence-Based Complementary and Alternative Medicine 2021 (March 23, 2021): 1–7. http://dx.doi.org/10.1155/2021/6616060.
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Research exploring the mechanism of acupuncture has been a hot topic in medicine. Resting-state functional magnetic resonance imaging (rs-fMRI) research is a noninvasive and extensive method, which is aimed at the research of the mechanism of acupuncture. Researchers use fMRI technologies to inspect the acupuncture process. The authors reviewed the application of rs-fMRI in acupuncture research in recent 10 years from the aspects of studying acupoints, subjects, acupuncture methods, and intensities. The results found that the application of rs-fMRI in acupuncture research mainly includes research on the onset mechanism of acupuncture treatment; visual evidence of diagnosis and treatment of dominant diseases; efficacy assessments; physiological mechanism of acupoint stimulation; and specific visualization of acupoints.
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Boerwinkle, Varina L., Lucia Mirea, William D. Gaillard, Bethany L. Sussman, Diana Larocque, Alexandra Bonnell, Jennifer S. Ronecker, et al. "Resting-state functional MRI connectivity impact on epilepsy surgery plan and surgical candidacy: prospective clinical work." Journal of Neurosurgery: Pediatrics 25, no. 6 (June 2020): 574–81. http://dx.doi.org/10.3171/2020.1.peds19695.
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OBJECTIVEThe authors’ goal was to prospectively quantify the impact of resting-state functional MRI (rs-fMRI) on pediatric epilepsy surgery planning.METHODSFifty-one consecutive patients (3 months to 20 years old) with intractable epilepsy underwent rs-fMRI for presurgical evaluation. The team reviewed the following available diagnostic data: video-electroencephalography (n = 51), structural MRI (n = 51), FDG-PET (n = 42), magnetoencephalography (n = 5), and neuropsychological testing (n = 51) results to formulate an initial surgery plan blinded to the rs-fMRI findings. Subsequent to this discussion, the connectivity results were revealed and final recommendations were established. Changes between pre– and post–rs-fMRI treatment plans were determined, and changes in surgery recommendation were compared using McNemar’s test.RESULTSResting-state fMRI was successfully performed in 50 (98%) of 51 cases and changed the seizure onset zone localization in 44 (88%) of 50 patients. The connectivity results prompted 6 additional studies, eliminated the ordering of 11 further diagnostic studies, and changed the intracranial monitoring plan in 10 cases. The connectivity results significantly altered surgery planning with the addition of 13 surgeries, but it did not eliminate planned surgeries (p = 0.003). Among the 38 epilepsy surgeries performed, the final surgical approach changed due to rs-fMRI findings in 22 cases (58%), including 8 (28%) of 29 in which extraoperative direct electrical stimulation mapping was averted.CONCLUSIONSThis study demonstrates the impact of rs-fMRI connectivity results on the decision-making for pediatric epilepsy surgery by providing new information about the location of eloquent cortex and the seizure onset zone. Additionally, connectivity results may increase the proportion of patients considered eligible for surgery while optimizing the need for further testing.
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Boegle, R., V. Kirsch, J. Gerb, and M. Dieterich. "Modulatory effects of magnetic vestibular stimulation on resting-state networks can be explained by subject-specific orientation of inner-ear anatomy in the MR static magnetic field." Journal of Neurology 267, S1 (June 11, 2020): 91–103. http://dx.doi.org/10.1007/s00415-020-09957-3.
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AbstractStrong static magnetic fields, as used in magnetic resonance imaging (MRI), stimulate the vestibular inner ear leading to a state of imbalance within the vestibular system that causes nystagmus. This magnetic vestibular stimulation (MVS) also modulates fluctuations of resting-state functional MRI (RS-fMRI) networks. MVS can be explained by a Lorentz force model, indicating that MVS is the result of the interaction of the static magnetic field strength and direction (called “B0 magnetic field” in MRI) with the inner ear’s continuous endolymphatic ionic current. However, the high variability between subjects receiving MVS (measured as nystagmus slow-phase velocity and RS-fMRI amplitude modulations) despite matching head position, remains to be explained. Furthermore, within the imaging community, an “easy-to-acquire-and-use” proxy accounting for modulatory MVS effects in RS-fMRI fluctuations is needed. The present study uses MRI data of 60 healthy volunteers to examine the relationship between RS-fMRI fluctuations and the individual orientation of inner-ear anatomy within the static magnetic field of the MRI. The individual inner-ear anatomy and orientation were assessed via high-resolution anatomical CISS images and related to fluctuations of RS-fMRI networks previously associated with MVS. More specifically, we used a subject-specific proxy for MVS (pMVS) that corresponds to the orientation of the individual inner-ear anatomy within the static magnetic field direction (also called “z-direction” in MR imaging). We found that pMVS explained a considerable fraction of the total variance in RS-fMRI fluctuations (for instance, from 11% in the right cerebellum up to 36% in the cerebellar vermis). In addition to pMVS, we examined the angle of Reid’s plane, as determined from anatomical imaging as an alternative and found that this angle (with the same sinus transformation as for pMVS) explained considerably less variance, e.g., from 2 to 16%. In our opinion, an excess variability due to MVS should generally be addressed in fMRI research analogous to nuisance regression for movement, pulsation, and respiration effects. We suggest using the pMVS parameter to deal with modulations of RS-fMRI fluctuations due to MVS. MVS-induced variance can easily be accounted by using high-resolution anatomical imaging of the inner ear and including the proposed pMVS parameter in fMRI group-level analysis.
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Hacker, Carl D., Jarod L. Roland, Albert H. Kim, Joshua S. Shimony, and Eric C. Leuthardt. "Resting-state network mapping in neurosurgical practice: a review." Neurosurgical Focus 47, no. 6 (December 2019): E15. http://dx.doi.org/10.3171/2019.9.focus19656.
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Resting-state functional MRI (rs-fMRI) is a well-established method for studying intrinsic connectivity and mapping the topography of functional networks in the human brain. In the clinical setting, rs-fMRI has been used to define functional topography, typically language and motor systems, in the context of preoperative planning for neurosurgery. Intraoperative mapping of critical speech and motor areas with electrocortical stimulation (ECS) remains standard practice, but preoperative noninvasive mapping has the potential to reduce operative time and provide functional localization when awake mapping is not feasible. Task-based fMRI has historically been used for this purpose, but it can be limited by the young age of the patient, cognitive impairment, poor cooperation, and need for sedation. Resting-state fMRI allows reliable analysis of all functional networks with a single study and is inherently independent of factors affecting task performance. In this review, the authors provide a summary of the theory and methods for resting-state network mapping. They provide case examples illustrating clinical implementation and discuss limitations of rs-fMRI and review available data regarding performance in comparison to ECS. Finally, they discuss novel opportunities for future clinical applications and prospects for rs-fMRI beyond mapping of regions to avoid during surgery but, instead, as a tool to guide novel network-based therapies.
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Berge, Daniel, Tyler Lesh, Jason Smucny, and Cameron Carter. "O5.4. THALAMIC CONNECTIVITY IN EARLY PSYCHOSIS DURING TASK AND AT REST." Schizophrenia Bulletin 46, Supplement_1 (April 2020): S12. http://dx.doi.org/10.1093/schbul/sbaa028.027.
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Abstract Background Resting state (rs)-fMRI studies have found abnormal connectivity between the thalamus and other areas in schizophrenia. rs-fMRI may not have the capacity to establish connections between the functional patterns and symptoms or deficits that characterize the illness, and thus, the clinical consequences of this dysconnectivity remain poorly understood. Task-based functional connectivity can capture activity of a particular cognitive process and has shown to better characterize individual differences related to cognitive traits than rs-fMRI (Greene, Gao, Scheinost, & Constable, 2018). Only a few studies have explored thalamo-cortical connectivity using task-based fMRI, which could potentially enhance the capacity to match aberrant connectivity patterns with schizophrenia features. The aim of this study is to compare thalamic connectivity in rs-fMRI and AX-CPT task-based and its clinical correlations in subjects during the early phase of the illness Methods 115 FEP and 78 healthy controls were recruited from the UCDavis Early Psychosis Program. FEP were assessed using BPRS, SANS and SAPS. All participants underwent an MRI scan that included 6 minutes of resting state and 4 runs (40 trials per run) of the AX-CPT task. A subject-specific mask of the bilateral thalamus was extracted using Freesurfer segmentation of the structural images to generate subject-specific seed regions for both rs-fMRI and task-based connectivity. From the latter, only images during Cue A and Cue B were selected, as during these events a proactive cognitive strategy is gathered. To obtain thalamic connectivity, time-series BOLD activity in the seed region was correlated with functional images (both rs-fMRI and task-based fMRI) previously preprocessed in CONN toolbox including slice-time correction, realignment, normalization to an EPI template, smoothing and scrubbing. Voxel-wise correlation maps were compared between groups and corrected for multiple comparison using a threshold of FDR < 0.05. All analysis included age, gender, number of volumes scrubbed and mean motion as covariates. Results Increased connectivity was observed in FEP compared to HC between the thalamic seed and somatosensory regions and temporal areas. In contrast, decreased connectivity was observed between the seed and prefrontal regions (frontal pole) and cerebellum. AX-CPT task-based Likewise, increased connectivity was also observed in FEP in the AX-CPT between the thalamic seed and somatosensory and temporal regions. Decreased connectivity was also observed in FEP between the seed and the anterior cingulate and cerebellum. Increased connectivity in FEP during rs-fMRI was significantly correlated with total SANS score (p = 0.03). Discussion Besides rs-fMRI and task-based fMRI involve different cognitive processes, both study paradigms show similar pattern of between-group differences in thalamic connectivity, which reinforces the validity and reliability of previous studies on functional connectivity in FEP and schizophrenia. Additionally, decreased connectivity between the thalamus and anterior cingulate during task was also observed, suggesting that task-based connectivity may additionally demonstrate between-group differences in regions related to specific cognitive processes. Future studies comparing time-varying dynamic connectivity in rs and task-based fMRI may help elucidate the time course of these changes in connectivity in FEP.
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Sahu, Arpita, Vineeth Kurki, Antariksh Vijan, Amit Janu, Prakash Shetty, and Aliasgar Moiyadi. "Case Series of Applications of Resting State Functional MRI in Brain Tumor Surgery: A Novel Technique." Indian Journal of Radiology and Imaging 31, no. 04 (October 2021): 990–97. http://dx.doi.org/10.1055/s-0041-1741046.
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Abstract Background The extent of resection for brain tumors is a critical factor in determining the oncologic outcome for a patient. However, a balance between preservation of neurological function and maximal resection is essential for true benefit.Functional magnetic resonance imaging (fMRI) is one of the approaches that augments the neurosurgeon's ability to attain maximal safe resection by providing preoperative mapping. It may not be possible to perform awake craniotomy with intraoperative localization by direct cortical stimulation in all patients, such as children and those with neurocognitive impairment. Task-based fMRI may have limited value in these cases due to low patient cooperability. Methods In this article we present in a case-based format, the various clinical scenarios where resting state fMRI (rs-fMRI) can be helpful in guiding neurosurgical resection. rs-fMRI of the patients has been acquired on Philips 1.5 T system. Seed voxel method has been used for processing and analysis. Conclusion rs-fMRI does not require active patient cooperation to generate useful information and thus can be a promising tool in patients unable to cooperate for task-based studies.
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Roland, Jarod L., Natalie Griffin, Carl D. Hacker, Ananth K. Vellimana, S. Hassan Akbari, Joshua S. Shimony, Matthew D. Smyth, Eric C. Leuthardt, and David D. Limbrick. "Resting-state functional magnetic resonance imaging for surgical planning in pediatric patients: a preliminary experience." Journal of Neurosurgery: Pediatrics 20, no. 6 (December 2017): 583–90. http://dx.doi.org/10.3171/2017.6.peds1711.
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OBJECTIVECerebral mapping for surgical planning and operative guidance is a challenging task in neurosurgery. Pediatric patients are often poor candidates for many modern mapping techniques because of inability to cooperate due to their immature age, cognitive deficits, or other factors. Resting-state functional MRI (rs-fMRI) is uniquely suited to benefit pediatric patients because it is inherently noninvasive and does not require task performance or significant cooperation. Recent advances in the field have made mapping cerebral networks possible on an individual basis for use in clinical decision making. The authors present their initial experience translating rs-fMRI into clinical practice for surgical planning in pediatric patients.METHODSThe authors retrospectively reviewed cases in which the rs-fMRI analysis technique was used prior to craniotomy in pediatric patients undergoing surgery in their institution. Resting-state analysis was performed using a previously trained machine-learning algorithm for identification of resting-state networks on an individual basis. Network maps were uploaded to the clinical imaging and surgical navigation systems. Patient demographic and clinical characteristics, including need for sedation during imaging and use of task-based fMRI, were also recorded.RESULTSTwenty patients underwent rs-fMRI prior to craniotomy between December 2013 and June 2016. Their ages ranged from 1.9 to 18.4 years, and 12 were male. Five of the 20 patients also underwent task-based fMRI and one underwent awake craniotomy. Six patients required sedation to tolerate MRI acquisition, including resting-state sequences. Exemplar cases are presented including anatomical and resting-state functional imaging.CONCLUSIONSResting-state fMRI is a rapidly advancing field of study allowing for whole brain analysis by a noninvasive modality. It is applicable to a wide range of patients and effective even under general anesthesia. The nature of resting-state analysis precludes any need for task cooperation. These features make rs-fMRI an ideal technology for cerebral mapping in pediatric neurosurgical patients. This review of the use of rs-fMRI mapping in an initial pediatric case series demonstrates the feasibility of utilizing this technique in pediatric neurosurgical patients. The preliminary experience presented here is a first step in translating this technique to a broader clinical practice.
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Aradhya, Abhay M. S., Aditya Joglekar, Sundaram Suresh, and M. Pratama. "Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2556–63. http://dx.doi.org/10.1609/aaai.v33i01.33012556.
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Analysis of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data has been a challenging problem due to a high homogeneity, large intra-class variability, limited samples and difference in acquisition technologies/techniques. These issues are predominant in the case of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data. The hidden transformation layer in DTM projects the original rs-fMRI data into a new space using the learning policy and extracts the spatio-temporal correlations of the functional activities as a latent feature space. The subsequent convolution and decision layers transform the latent feature space into high-level features and provide accurate classification. The performance of DTM has been evaluated using the ADHD200 rs-fMRI benchmark data with crossvalidation. The results show that the proposed DTM achieves a mean classification accuracy of 70.36% and an improvement of 8.25% on the state of the art methodologies was observed. The improvement is due to concurrent analysis of the spatio-temporal correlations between the different regions of the brain and can be easily extended to study other cognitive disorders using rs-fMRI. Further, brain network analysis has been studied to identify the difference in functional activities and the corresponding regions behind cognitive symptoms in ADHD.
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Temniy, A., K. Markin, M. Poplyak, A. Trufanov, D. Tarumov, and I. Litvinenko. "Depression in multiple sclerosis: RS-FMRI research." European Psychiatry 64, S1 (April 2021): S90. http://dx.doi.org/10.1192/j.eurpsy.2021.265.
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IntroductionMultiple sclerosis (MS) is a demyelinating and neurodegenerative disorder of the CNS, which incapacitates people of working age. Due to progressive disability, the quality of life decreases, adding a number of other diseases to the main one. Several studies have reported high rates of depression in MS with a lifetime prevalence of approximately 50%.ObjectivesTherefore, we would like to pattern the functional activation of the brain of patients with different phenotypes of MS. This would objectify the patient’s condition and the effectiveness of therapy for these diseases.Methods68 patients with MS were examined: 40 with a relapsing-remitting type of course (RRMS) in remission and 28 with secondary - progressive MS (SPMS). Patients underwent MRI of the brain on a Siemens Tim Trio 3.0 T tomograph and processed the data using CONN 18b software. Clinical features were estimated by tests (BDI, HADS) results.Results91% of all MS patients in research have signs of depression. We noted that decreased FC in RRMS patients has a whole-brain type, but it is only decreasing, not losing the connections between brain clusters. Decreased FC and losing the connections between large-scale brain networks and brain clusters. Due to tests, more severe depression was observed in SPMS patients.ConclusionsOur findings suggest that patients with SPMS have depression, cause of decreasing in FC between the main clusters of the brain, and patients with SPMS have more severe depression, which, as we assume, neurodegeneration has turned into atrophy and loosing all connections between clusters even in large-scale brain networks.DisclosureNo significant relationships.
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Siniscalchi, Antonio. "Use of RS-fMRI in Fabry disease." Neurology 88, no. 19 (April 12, 2017): 1784–85. http://dx.doi.org/10.1212/wnl.0000000000003925.
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Cha, Jungho, Jung-Min Hwang, Hang Joon Jo, Sang Won Seo, Duk L. Na, and Jong-Min Lee. "Assessment of Functional Characteristics of Amnestic Mild Cognitive Impairment and Alzheimer’s Disease Using Various Methods of Resting-State FMRI Analysis." BioMed Research International 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/907464.
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Resting-state functional magnetic resonance imaging (RS FMRI) has been widely used to analyze functional alterations in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) patients. Although many clinical studies of aMCI and AD patients using RS FMRI have been undertaken, conducting a meta-analysis has not been easy because of seed selection bias by the investigators. The purpose of our study was to investigate the functional differences in aMCI and AD patients compared with healthy subjects in a meta-analysis. Thus, a multimethod approach using regional homogeneity, amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and global brain connectivity was used to investigate differences between three groups based on previously published data. According to the choice of RS FMRI approach used, the patterns of functional alteration were slightly different. Nevertheless, patients with aMCI and AD displayed consistently decreased functional characteristics with all approaches. All approaches showed that the functional characteristics in the left parahippocampal gyrus were decreased in AD patients compared with healthy subjects. Although some regions were slightly different according to the different RS FMRI approaches, patients with aMCI and AD showed a consistent pattern of decreased functional characteristics with all approaches.
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Mitchell, Timothy J., Carl D. Hacker, Jonathan D. Breshears, Nick P. Szrama, Mohit Sharma, David T. Bundy, Mrinal Pahwa, et al. "A Novel Data-Driven Approach to Preoperative Mapping of Functional Cortex Using Resting-State Functional Magnetic Resonance Imaging." Neurosurgery 73, no. 6 (September 26, 2013): 969–83. http://dx.doi.org/10.1227/neu.0000000000000141.
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Abstract BACKGROUND: Recent findings associated with resting-state cortical networks have provided insight into the brain's organizational structure. In addition to their neuroscientific implications, the networks identified by resting-state functional magnetic resonance imaging (rs-fMRI) may prove useful for clinical brain mapping. OBJECTIVE: To demonstrate that a data-driven approach to analyze resting-state networks (RSNs) is useful in identifying regions classically understood to be eloquent cortex as well as other functional networks. METHODS: This study included 6 patients undergoing surgical treatment for intractable epilepsy and 7 patients undergoing tumor resection. rs-fMRI data were obtained before surgery and 7 canonical RSNs were identified by an artificial neural network algorithm. Of these 7, the motor and language networks were then compared with electrocortical stimulation (ECS) as the gold standard in the epilepsy patients. The sensitivity and specificity for identifying these eloquent sites were calculated at varying thresholds, which yielded receiver-operating characteristic (ROC) curves and their associated area under the curve (AUC). RSNs were plotted in the tumor patients to observe RSN distortions in altered anatomy. RESULTS: The algorithm robustly identified all networks in all patients, including those with distorted anatomy. When all ECS-positive sites were considered for motor and language, rs-fMRI had AUCs of 0.80 and 0.64, respectively. When the ECS-positive sites were analyzed pairwise, rs-fMRI had AUCs of 0.89 and 0.76 for motor and language, respectively. CONCLUSION: A data-driven approach to rs-fMRI may be a new and efficient method for preoperative localization of numerous functional brain regions.
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Schabdach, Jenna, Rafael Ceschin, Vanessa Schmithorst, M. Dylan Tisdall, Aaron Alexander-Bloch, and Ashok Panigrahy. "A Descriptive Review of the Impact of Patient Motion in Early Childhood Resting-State Functional Magnetic Resonance Imaging." Diagnostics 12, no. 5 (April 20, 2022): 1032. http://dx.doi.org/10.3390/diagnostics12051032.
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Resting-state functional magnetic images (rs-fMRIs) can be used to map and delineate the brain activity occurring while the patient is in a task-free state. These resting-state activity networks can be informative when diagnosing various neurodevelopmental diseases, but only if the images are high quality. The quality of an rs-fMRI rapidly degrades when the patient moves during the scan. Herein, we describe how patient motion impacts an rs-fMRI on multiple levels. We begin with how the electromagnetic field and pulses of an MR scanner interact with a patient’s physiology, how movement affects the net signal acquired by the scanner, and how motion can be quantified from rs-fMRI. We then present methods for preventing motion through educational and behavioral interventions appropriate for different age groups, techniques for prospectively monitoring and correcting motion during the acquisition process, and pipelines for mitigating the effects of motion in existing scans.
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Zhu, Qiao Yun, HanHua Bai, Yi Wu, Yu Jia Zhou, and Qianjin Feng. "Identity-mapping cascaded network for fMRI registration." Physics in Medicine & Biology 66, no. 22 (November 15, 2021): 225011. http://dx.doi.org/10.1088/1361-6560/ac34b1.
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Abstract Neuroscience researches based on functional magnetic resonance imaging (fMRI) rely on accurate inter-subject image registration of functional regions. The intersubject alignment of fMRI can improve the statistical power of group analyses. Recent studies have shown the deep learning-based registration methods can be used for registration. In our work, we proposed a 30-Identity-Mapping Cascaded network (30-IMCNet) for rs-fMRI registration. It is a cascaded network that can warp the moving image progressively and finally align to the fixed image. A Combination unit with an identity-mapping path is added to the inputs of each IMCNet to guide the network training. We implemented 30-IMCNet on an rs-fMRI dataset (1000 Functional Connectomes Project dataset) and a task-related fMRI dataset (Eyes Open Eyes Closed fMRI dataset). To evaluate our method, a group-level analysis was implemented in the testing dataset. For rs-fMRI, the criterions such as peak t-value of group-level t-maps, cluster-level evaluation, and intersubject functional network correlation were used to evaluate the quality of the registrations. For task-related fMRI, peak t-value in ALFF paired-t map and peak t-value in ReHo paired-t maps were used. Compared with traditional algorithm FSL, SPM, and deep learning algorithm Kim et al, Zhao et al our method has improvements of 48.90%, 30.73%, 36.38%, and 16.73% in the peak t value of t-maps. Our proposed method can achieve superior functional registration performance and thus gain a significant improvement in functional consistency.
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Ott, Christian, Katharina Rosengarth, Christian Doenitz, Julius Hoehne, Christina Wendl, Frank Dodoo-Schittko, Elmar Lang, Nils Ole Schmidt, and Markus Goldhacker. "Preoperative Assessment of Language Dominance through Combined Resting-State and Task-Based Functional Magnetic Resonance Imaging." Journal of Personalized Medicine 11, no. 12 (December 9, 2021): 1342. http://dx.doi.org/10.3390/jpm11121342.
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Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years, the resting-state fMRI (RS-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere. Twenty patients suffering from brain lesions close to supposed language-relevant cortical areas were included. RS-fMRI and task-based (TB-fMRI) were performed for the purpose of preoperative language assessment. TB-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language-critical and language-supportive processes. Subsequently, the best fitting ICA component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the TB-fMRI (including models with and without linguistic control conditions) was identified using an algorithm based on the Dice index. Thereby, the RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly. In general, the results suggest that determining language dominance in the human brain is feasible both with TB-fMRI and RS-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.
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Catalino, Michael P., Shun Yao, Deborah L. Green, Edward R. Laws, Alexandra J. Golby, and Yanmei Tie. "Mapping cognitive and emotional networks in neurosurgical patients using resting-state functional magnetic resonance imaging." Neurosurgical Focus 48, no. 2 (February 2020): E9. http://dx.doi.org/10.3171/2019.11.focus19773.
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Neurosurgery has been at the forefront of a paradigm shift from a localizationist perspective to a network-based approach to brain mapping. Over the last 2 decades, we have seen dramatic improvements in the way we can image the human brain and noninvasively estimate the location of critical functional networks. In certain patients with brain tumors and epilepsy, intraoperative electrical stimulation has revealed direct links between these networks and their function. The focus of these techniques has rightfully been identification and preservation of so-called “eloquent” brain functions (i.e., motor and language), but there is building momentum for more extensive mapping of cognitive and emotional networks. In addition, there is growing interest in mapping these functions in patients with a broad range of neurosurgical diseases. Resting-state functional MRI (rs-fMRI) is a noninvasive imaging modality that is able to measure spontaneous low-frequency blood oxygen level–dependent signal fluctuations at rest to infer neuronal activity. Rs-fMRI may be able to map cognitive and emotional networks for individual patients. In this review, the authors give an overview of the rs-fMRI technique and associated cognitive and emotional resting-state networks, discuss the potential applications of rs-fMRI, and propose future directions for the mapping of cognition and emotion in neurosurgical patients.
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Kadota, Katsuhiko, Keiichi Onoda, Satoshi Abe, Chizuko Hamada, Shingo Mitaki, Hiroaki Oguro, Atsushi Nagai, Hajime Kitagaki, and Shuhei Yamaguchi. "Multiscale Entropy of Resting-State Functional Magnetic Resonance Imaging Differentiates Progressive Supranuclear Palsy and Multiple System Atrophy." Life 11, no. 12 (December 16, 2021): 1411. http://dx.doi.org/10.3390/life11121411.
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Distinguishing progressive supranuclear palsy (PSP) from multiple system atrophy (MSA) in the early clinical stages is challenging; few sensitive and specific biomarkers are available for their differential diagnosis. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to study the fluctuations in blood oxygen level-dependent (BOLD) signals at rest, which provides evidence for aberrant brain functional networks in neurodegenerative diseases. We aimed to examine whether rs-fMRI data could differentiate between PSP and MSA via a multiscale entropy (MSE) analysis of BOLD signals, which estimates the complexity of temporal fluctuations in brain activity. We recruited 14 and 18 patients with PSP and MSA, respectively, who underwent neuropsychological tests and rs-fMRI. PSP patients demonstrated greater cognitive function impairments, particularly in the frontal executive function. The bilateral prefrontal cortex revealed lower entropy BOLD signal values in multiple time scales for PSP, compared to the values observed in MSA patients; however, the functional connectivity of the representative brain networks was comparable between the diseases. The reduced complexity of BOLD signals in the prefrontal cortex was associated with frontal dysfunction. Thus, an MSE analysis of rs-fMRI could differentiate between PSP and MSA, and the reduced complexity of BOLD signals could be associated with cognitive impairment.
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Chu, Ying, Haonan Ren, Lishan Qiao, and Mingxia Liu. "Resting-State Functional MRI Adaptation with Attention Graph Convolution Network for Brain Disorder Identification." Brain Sciences 12, no. 10 (October 20, 2022): 1413. http://dx.doi.org/10.3390/brainsci12101413.
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Multi-site resting-state functional magnetic resonance imaging (rs-fMRI) data can facilitate learning-based approaches to train reliable models on more data. However, significant data heterogeneity between imaging sites, caused by different scanners or protocols, can negatively impact the generalization ability of learned models. In addition, previous studies have shown that graph convolution neural networks (GCNs) are effective in mining fMRI biomarkers. However, they generally ignore the potentially different contributions of brain regions- of-interest (ROIs) to automated disease diagnosis/prognosis. In this work, we propose a multi-site rs-fMRI adaptation framework with attention GCN (A2GCN) for brain disorder identification. Specifically, the proposed A2GCN consists of three major components: (1) a node representation learning module based on GCN to extract rs-fMRI features from functional connectivity networks, (2) a node attention mechanism module to capture the contributions of ROIs, and (3) a domain adaptation module to alleviate the differences in data distribution between sites through the constraint of mean absolute error and covariance. The A2GCN not only reduces data heterogeneity across sites, but also improves the interpretability of the learning algorithm by exploring important ROIs. Experimental results on the public ABIDE database demonstrate that our method achieves remarkable performance in fMRI-based recognition of autism spectrum disorders.
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Yang, Shuqin, Xiaoyan Bie, Yanmei Wang, Junnan Li, Yujing Wang, and Xiaoyan Sun. "Image Features of Resting-State Functional Magnetic Resonance Imaging in Evaluating Poor Emotion and Sleep Quality in Patients with Chronic Pain under Artificial Intelligence Algorithm." Contrast Media & Molecular Imaging 2022 (January 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/5002754.
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The balanced iterative reducing and clustering using hierarchies (BIRCH) method was adopted to optimize the results of the resting-state functional magnetic resonance imaging (RS-fMRI) to analyze the changes in the brain function of patients with chronic pain accompanied by poor emotion or abnormal sleep quality in this study, so as to provide data support for the prevention and treatment of clinical chronic pain with poor emotion or sleep quality. 159 patients with chronic pain who visited the hospital were selected as the research objects, and they were grouped according to the presence or absence of abnormalities in emotion and sleep. The patients without poor emotion and sleep quality were set as the control group (60 cases), and the patients with the above symptoms were defined in the observation group (90 cases). The brain function was detected by RS-fMRI technology based on the BIRCH algorithm. The results showed that the rand index (RI), adjustment of RI (ARI), and Fowlkes–Mallows index (FMI) results in the k-means, flow cytometry (FCM), and BIRCH algorithms were 0.82, 0.71, and 0.88, respectively. The scores of Hamilton Depression Scale (HAHD), Hamilton Anxiety Scale (HAMA), and Pittsburgh Sleep Quality Index (PSQI) were 7.26 ± 3.95, 7.94 ± 3.15, and 8.03 ± 4.67 in the observation group and 4.03 ± 1.95, 5.13 ± 2.35, and 4.43 ± 2.07 in the control group; the higher proportion of RS-fMRI was with abnormal brain signal connections. A score of 7 or more meant that the number of brain abnormalities was more than 90% and that of less than 7 was less than 40%, showing a statistically obvious difference in contrast P < 0.05 . Therefore, the BIRCH clustering algorithm showed reliable value in the optimization of RS-fMRI images, and RS-fMRI showed high application value in evaluating the emotion and sleep quality of patients with chronic pain.
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Ulrich, Martin, Katharina Heckel, Markus Kölle, and Georg Grön. "Methylphenidate Differentially Affects Intrinsic Functional Connectivity of the Salience Network in Adult ADHD Treatment Responders and Non-Responders." Biology 11, no. 9 (September 6, 2022): 1320. http://dx.doi.org/10.3390/biology11091320.
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Positron emission tomography (PET) studies have shown involvement of the striatum when treating adult attention-deficit/hyperactivity disorder (ADHD) with methylphenidate (MPH). Results from resting-state functional magnetic resonance imaging (rs-fMRI) for the same issue were less unequivocal. Here, a new analytical framework was set up to investigate medication effects using seed-based rs-fMRI analysis to infer brain regions with alterations in intrinsic functional connectivity (IFC) corresponding with ADHD symptom reduction. In a within-subjects study design, 53 stimulant-naïve adult ADHD patients were investigated before and after 6 weeks of MPH treatment, using two major clinical symptom scales and rs-fMRI. The same data were acquired in a sample of 50 age- and sex-matched healthy controls at baseline. A consensual atlas provided seeds for five predefined major resting-state networks. In order to avoid biasing of medication effects due to putative treatment failure, the entire ADHD sample was first categorized into treatment Responders (N = 36) and Non-Responders (N = 17) using machine learning-based classification with the clinical scales as primary data. Imaging data revealed medication effects only in Responders. In that group, IFC of bilateral putamen changed significantly with medication and approached almost normal levels of IFC. Present results align well with results from previous PET studies, with seed-based rs-fMRI as an entirely different neuroimaging method.
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Heed, Tobias, Frank T. M. Leone, Ivan Toni, and W. Pieter Medendorp. "Functional versus effector-specific organization of the human posterior parietal cortex: revisited." Journal of Neurophysiology 116, no. 4 (October 1, 2016): 1885–99. http://dx.doi.org/10.1152/jn.00312.2014.
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It has been proposed that the posterior parietal cortex (PPC) is characterized by an effector-specific organization. However, strikingly similar functional MRI (fMRI) activation patterns have been found in the PPC for hand and foot movements. Because the fMRI signal is related to average neuronal activity, similar activation levels may result either from effector-unspecific neurons or from intermingled subsets of effector-specific neurons within a voxel. We distinguished between these possibilities using fMRI repetition suppression (RS). Participants made delayed, goal-directed eye, hand, and foot movements to visual targets. In each trial, the instructed effector was identical or different to that of the previous trial. RS effects indicated an attenuation of the fMRI signal in repeat trials. The caudal PPC was active during the delay but did not show RS, suggesting that its planning activity was effector independent. Hand and foot-specific RS effects were evident in the anterior superior parietal lobule (SPL), extending to the premotor cortex, with limb overlap in the anterior SPL. Connectivity analysis suggested information flow between the caudal PPC to limb-specific anterior SPL regions and between the limb-unspecific anterior SPL toward limb-specific motor regions. These results underline that both function and effector specificity should be integrated into a concept of PPC action representation not only on a regional but also on a fine-grained, subvoxel level.
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Tong, Yunjie, Kimberly P. Lindsey, Lia M. Hocke, Gordana Vitaliano, Dionyssios Mintzopoulos, and Blaise deB Frederick. "Perfusion information extracted from resting state functional magnetic resonance imaging." Journal of Cerebral Blood Flow & Metabolism 37, no. 2 (July 20, 2016): 564–76. http://dx.doi.org/10.1177/0271678x16631755.
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It is widely known that blood oxygenation level dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) is an indirect measure for neuronal activations through neurovascular coupling. The BOLD signal is also influenced by many non-neuronal physiological fluctuations. In previous resting state (RS) fMRI studies, we have identified a moving systemic low frequency oscillation (sLFO) in BOLD signal and were able to track its passage through the brain. We hypothesized that this seemingly intrinsic signal moves with the blood, and therefore, its dynamic patterns represent cerebral blood flow. In this study, we tested this hypothesis by performing Dynamic Susceptibility Contrast (DSC) MRI scans (i.e. bolus tracking) following the RS scans on eight healthy subjects. The dynamic patterns of sLFO derived from RS data were compared with the bolus flow visually and quantitatively. We found that the flow of sLFO derived from RS fMRI does to a large extent represent the blood flow measured with DSC. The small differences, we hypothesize, are largely due to the difference between the methods in their sensitivity to different vessel types. We conclude that the flow of sLFO in RS visualized by our time delay method represents the blood flow in the capillaries and veins in the brain.
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Roder, Constantin, Edyta Charyasz-Leks, Martin Breitkopf, Karlheinz Decker, Ulrike Ernemann, Uwe Klose, Marcos Tatagiba, and Sotirios Bisdas. "Resting-state functional MRI in an intraoperative MRI setting: proof of feasibility and correlation to clinical outcome of patients." Journal of Neurosurgery 125, no. 2 (August 2016): 401–9. http://dx.doi.org/10.3171/2015.7.jns15617.
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OBJECTIVE The authors' aim in this paper is to prove the feasibility of resting-state (RS) functional MRI (fMRI) in an intraoperative setting (iRS-fMRI) and to correlate findings with the clinical condition of patients pre- and postoperatively. METHODS Twelve patients underwent intraoperative MRI-guided resection of lesions in or directly adjacent to the central region and/or pyramidal tract. Intraoperative RS (iRS)–fMRI was performed pre- and intraoperatively and was correlated with patients' postoperative clinical condition, as well as with intraoperative monitoring results. Independent component analysis (ICA) was used to postprocess the RS-fMRI data concerning the sensorimotor networks, and the mean z-scores were statistically analyzed. RESULTS iRS-fMRI in anesthetized patients proved to be feasible and analysis revealed no significant differences in preoperative z-scores between the sensorimotor areas ipsi- and contralateral to the tumor. A significant decrease in z-score (p < 0.01) was seen in patients with new neurological deficits postoperatively. The intraoperative z-score in the hemisphere ipsilateral to the tumor had a significant negative correlation with the degree of paresis immediately after the operation (r = −0.67, p < 0.001) and on the day of discharge from the hospital (r = −0.65, p < 0.001). Receiver operating characteristic curve analysis demonstrated moderate prognostic value of the intraoperative z-score (area under the curve 0.84) for the paresis score at patient discharge. CONCLUSIONS The use of iRS-fMRI with ICA-based postprocessing and functional activity mapping is feasible and the results may correlate with clinical parameters, demonstrating a significant negative correlation between the intensity of the iRS-fMRI signal and the postoperative neurological changes.
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Basile, B., M. Castelli, F. Monteleone, U. Nocentini, C. Caltagirone, D. Centonze, M. Cercignani, and M. Bozzali. "Functional connectivity changes within specific networks parallel the clinical evolution of multiple sclerosis." Multiple Sclerosis Journal 20, no. 8 (December 10, 2013): 1050–57. http://dx.doi.org/10.1177/1352458513515082.
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Background: In multiple sclerosis (MS), the location of focal lesions does not always correlate with clinical symptoms, suggesting disconnection as a major pathophysiological mechanism. Resting-state (RS) functional magnetic resonance imaging (fMRI) is believed to reflect brain functional connectivity (FC) within specific neuronal networks. Objective: RS-fMRI was used to investigate changes in FC within two critical networks for the understanding of MS disabilities, namely, the sensory-motor network (SMN) and the default-mode network (DMN), respectively, implicated in sensory-motor and cognitive functions. Methods: Thirty-four relapsing–remitting (RR), 14 secondary progressive (SP) MS patients and 25 healthy controls underwent MRI at 3T, including conventional images, T1-weighted volumes, and RS-fMRI sequences. Independent component analysis (ICA) was employed to extract maps of the relevant RS networks for every participant. Group analyses were performed to assess changes in FC within the SMN and DMN in the two MS phenotypes. Results: Increased FC was found in both networks of MS patients. Interestingly, specific changes in either direction were observed also between RR and SP MS groups. Conclusions: FC changes seem to parallel patients’ clinical state and capability of compensating for the severity of clinical/cognitive disabilities.
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Savoldi, Filippo, Maria A. Rocca, Paola Valsasina, Gianna C. Riccitelli, Sarlota Mesaros, Jelena Drulovic, Marta Radaelli, and Massimo Filippi. "Functional brain connectivity abnormalities and cognitive deficits in neuromyelitis optica spectrum disorder." Multiple Sclerosis Journal 26, no. 7 (May 13, 2019): 795–805. http://dx.doi.org/10.1177/1352458519845109.
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Background: Functional magnetic resonance imaging (fMRI) correlates of cognitive deficits have not been thoroughly studied in patients with neuromyelitis optica spectrum disorders (NMOSDs). Objective: To investigate resting state (RS) functional connectivity (FC) abnormalities within the main cognitive networks in NMOSD patients and their correlation with cognitive performance. Methods: We acquired RS fMRI from 25 NMOSD patients and 30 healthy controls (HC). Patients underwent an extensive neuropsychological evaluation. Between-group RS FC comparisons and correlations with cognitive performance were assessed on the main cognitive RS networks identified by independent component analysis. Results: NMOSD patients showed higher RS FC versus HC in the precuneus of the default mode network (DMN) and right working memory network (WMN), as well as in several frontoparietal regions of the salience network (SN) and bilateral WMNs. Reduced frontal RS FC in NMOSD versus HC was detected in the left WMN. Increased RS FC in the DMN and right WMN was correlated with better cognitive performance, while decreased RS FC in the left WMN was associated with worse cognitive performance. Conclusion: Cognitive-network reorganization occurs in NMOSD. Clinico-imaging correlations suggest an adaptive role of increased RS FC. Conversely, reduced RS FC seems to be a maladaptive mechanism associated with a worse cognitive performance.
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Lee, Tien-Wen, and Gerald Tramontano. "Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements." AIMS Neuroscience 8, no. 4 (2021): 526–42. http://dx.doi.org/10.3934/neuroscience.2021028.
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<abstract> <p>To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similarity measurements (MOSI) to fulfill functional parcellation (FP) of the cortex. MOSI is carried out by iteratively dividing a module into sub-modules (via the Louvain community detection method) and unifying similar neighboring sub-modules into a new module (adjacent sub-modules with a similarity index <0.05) until the brain modular structures of successive runs become constant. By adjusting the gamma value, a parameter in the Louvain algorithm, MOSI may segment the cortex with different resolutions. rs-fMRI scans of 33 healthy subjects were selected from the dataset of the Rockland sample. MOSI was applied to the rs-fMRI data after standardized pre-processing steps. The results indicate that the parcellated modules by MOSI are more homogeneous in content. After reducing the grouped voxels to representative neural nodes, the network structures were explored. The resultant network components were comparable with previous reports. The validity of MOSI in achieving data reduction has been confirmed. MOSI may provide a novel starting point for further investigation of the network properties of rs-fMRI data. Potential applications of MOSI are discussed.</p> </abstract>
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Zhang, Jiping, Xiaowen Cai, Yanjie Wang, Yu Zheng, Shanshan Qu, Zhinan Zhang, Zengyu Yao, Guanghong Chen, Chunzhi Tang, and Yong Huang. "Different Brain Activation after Acupuncture at Combined Acupoints and Single Acupoint in Hypertension Patients: An Rs-fMRI Study Based on ReHo Analysis." Evidence-Based Complementary and Alternative Medicine 2019 (January 3, 2019): 1–10. http://dx.doi.org/10.1155/2019/5262896.
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Background. Acupuncture is proved to be effective on hypertension by numerous studies and resting-state functional magnetic resonance imaging (Rs-fMRI) is a widely used technique to study its mechanism. Along with lower blood pressure, patients with hypertension receiving acupuncture also presented improvement in function of cognition, emotion, language, sematic sensation, and so on. This study was a primary study to explore the acting path of acupuncture at combined acupoints in stimulated brain areas related to such functions. Methods. In this research, regional homogeneity (ReHo) was applied to analyze the Rs-fMRI image data of brain activities after acupuncture at LR3, KI3, and LR3+KI3 and to compare the differences of functional brain activities between stimulating combined acupoints and single acupoint under pathological conditions. A total of thirty hypertension patients underwent Rs-fMRI scanning before acupuncture treatment and then were randomly divided into three groups following random number table, the LR3 group (3 males and 7 females), the KI3 group (3 males and 7 females), and the LR3+ KI3 group (4 males and 6 females) for needling, respectively. When the 30-min treatment finished, they received a further Rs-fMRI scanning. The Rs-fMRI data before and after the acupuncture treatment were analyzed through ReHo. Results. Compared with preacupuncture, respectively, ReHo values increased in Brodmann areas (BAs) 3, 18, and 40 and decreased in BAs 7 and 31 in LR3+ KI3 group. However, ReHo values only decreased in BA7 of KI3 group while the results showed no significant difference of brain regions in LR3 group between pre- and postacupuncture. Compared with LR3 group, LR3+KI3 group exhibited decreased ReHo values in BAs 7, 9, and 31. Meanwhile, compared with KI3 group, LR3+KI3 group exhibited increased ReHo values in the BAs 2, 18, 30, and 40 and decreased ReHo values in BA13. Conclusion. Combined acupoints of LR3 and KI3 could act on wider brain areas than the sum of single acupoints, whose functions include emotional processing, cognition, somatic sensation, spatial orientation, language production, and vision.
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Don, Arjuna P. H., James F. Peters, Sheela Ramanna, and Arturo Tozzi. "Quaternionic views of rs-fMRI hierarchical brain activation regions. Discovery of multilevel brain activation region intensities in rs-fMRI video frames." Chaos, Solitons & Fractals 152 (November 2021): 111351. http://dx.doi.org/10.1016/j.chaos.2021.111351.
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Sbardella, Emilia, Nikolaos Petsas, Francesca Tona, and Patrizia Pantano. "Resting-State fMRI in MS: General Concepts and Brief Overview of Its Application." BioMed Research International 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/212693.
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Brain functional connectivity (FC) is defined as the coherence in the activity between cerebral areas under a task or in the resting-state (RS). By applying functional magnetic resonance imaging (fMRI), RS FC shows several patterns which define RS brain networks (RSNs) involved in specific functions, because brain function is known to depend not only on the activity within individual regions, but also on the functional interaction of different areas across the whole brain. Region-of-interest analysis and independent component analysis are the two most commonly applied methods for RS investigation. Multiple sclerosis (MS) is characterized by multiple lesions mainly affecting the white matter, determining both structural and functional disconnection between various areas of the central nervous system. The study of RS FC in MS is mainly aimed at understanding alterations in the intrinsic functional architecture of the brain and their role in disease progression and clinical impairment. In this paper, we will examine the results obtained by the application of RS fMRI in different multiple sclerosis (MS) phenotypes and the correlations of FC changes with clinical features in this pathology. The knowledge of RS FC changes may represent a substantial step forward in the MS research field, both for clinical and therapeutic purposes.
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Bisecco, Alvino, Federica Di Nardo, Renato Docimo, Giuseppina Caiazzo, Alessandro d’Ambrosio, Simona Bonavita, Rocco Capuano, et al. "Fatigue in multiple sclerosis: The contribution of resting-state functional connectivity reorganization." Multiple Sclerosis Journal 24, no. 13 (September 15, 2017): 1696–705. http://dx.doi.org/10.1177/1352458517730932.
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Objectives: To investigate resting-state functional connectivity (RS-FC) of the default-mode network (DMN) and of sensorimotor network (SMN) network in relapsing remitting (RR) multiple sclerosis (MS) patients with fatigue (F) and without fatigue(NF). Methods: In all, 59 RRMS patients and 29 healthy controls (HC) underwent magnetic resonance imaging (MRI) protocol including resting-state fMRI (RS-fMRI). Functional connectivity of the DMN and SMN was evaluated by independent component analysis (ICA). A linear regression analysis was performed to explore whether fatigue was mainly driven by changes observed in the DMN or in the SMN. Regional gray matter atrophy was assessed by voxel-based morphometry (VBM). Results: Compared to HC, F-MS patients showed a stronger RS-FC in the posterior cingulate cortex (PCC) and a reduced RS-FC in the anterior cingulated cortex (ACC) of the DMN. F-MS patients, compared to NF-MS patients, revealed (1) an increased RS-FC in the PCC and a reduced RS-FC in the ACC of the DMN and (2) an increased RS-FC in the primary motor cortex and in the supplementary motor cortex of the SMN. The regression analysis suggested that fatigue is mainly driven by RS-FC changes of the DMN. Conclusions: Fatigue in RRMS is mainly associated to a functional rearrangement of non-motor RS networks.
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Rodrigues, Igor D., Emerson A. de Carvalho, Caio P. Santana, and Guilherme S. Bastos. "Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity." Algorithms 15, no. 6 (June 7, 2022): 195. http://dx.doi.org/10.3390/a15060195.
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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social impairments that manifest in different severity levels. In recent years, many studies have explored the use of machine learning (ML) and resting-state functional magnetic resonance images (rs-fMRI) to investigate the disorder. These approaches evaluate brain oxygen levels to indirectly measure brain activity and compare typical developmental subjects with ASD ones. However, none of these works have tried to classify the subjects into severity groups using ML exclusively applied to rs-fMRI data. Information on ASD severity is frequently available since some tools used to support ASD diagnosis also include a severity measurement as their outcomes. The aforesaid is the case of the Autism Diagnostic Observation Schedule (ADOS), which splits the diagnosis into three groups: ‘autism’, ‘autism spectrum’, and ‘non-ASD’. Therefore, this paper aims to use ML and fMRI to identify potential brain regions as biomarkers of ASD severity. We used the ADOS score as a severity measurement standard. The experiment used fMRI data of 202 subjects with an ASD diagnosis and their ADOS scores available at the ABIDE I consortium to determine the correct ASD sub-class for each one. Our results suggest a functional difference between the ASD sub-classes by reaching 73.8% accuracy on cingulum regions. The aforementioned shows the feasibility of classifying and characterizing ASD using rs-fMRI data, indicating potential areas that could lead to severity biomarkers in further research. However, we highlight the need for more studies to confirm our findings.
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Kashyap, Amrit, and Shella Keilholz. "Dynamic properties of simulated brain network models and empirical resting-state data." Network Neuroscience 3, no. 2 (January 2019): 405–26. http://dx.doi.org/10.1162/netn_a_00070.
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Brain network models (BNMs) have become a promising theoretical framework for simulating signals that are representative of whole-brain activity such as resting-state fMRI. However, it has been difficult to compare the complex brain activity obtained from simulations to empirical data. Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the brain network model. We conclude that the dynamic properties that explicitly examine patterns of signal as a function of time rather than spatial coordination between different brain regions in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole-brain activity.
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Abdul Wahab, Nor Shafiza, Noorazrul Yahya, Ahmad Nazlim Yusoff, Rozman Zakaria, Jegan Thanabalan, Elza Othman, Soon Bee Hong, Ramesh Kumar Athi Kumar, and Hanani Abdul Manan. "Effects of Different Scan Duration on Brain Effective Connectivity among Default Mode Network Nodes." Diagnostics 12, no. 5 (May 20, 2022): 1277. http://dx.doi.org/10.3390/diagnostics12051277.
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Background: Resting-state functional magnetic resonance imaging (rs-fMRI) can evaluate brain functional connectivity without requiring subjects to perform a specific task. This rs-fMRI is very useful in patients with cognitive decline or unable to respond to tasks. However, long scan durations have been suggested to measure connectivity between brain areas to produce more reliable results, which are not clinically optimal. Therefore, this study aims to evaluate a shorter scan duration and compare the scan duration of 10 and 15 min using the rs-fMRI approach. Methods: Twenty-one healthy male and female participants (seventeen right-handed and four left-handed), with ages ranging between 21 and 60 years, were recruited. All participants underwent both 10 and 15 min of rs-fMRI scans. The present study evaluated the default mode network (DMN) areas for both scan durations. The areas involved were the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), left inferior parietal cortex (LIPC), and right inferior parietal cortex (RIPC). Fifteen causal models were constructed and inverted using spectral dynamic causal modelling (spDCM). The models were compared using Bayesian Model Selection (BMS) for group studies. Result: The BMS results indicated that the fully connected model was the winning model among 15 competing models for both 10 and 15 min scan durations. However, there was no significant difference in effective connectivity among the regions of interest between the 10 and 15 min scans. Conclusion: Scan duration in the range of 10 to 15 min is sufficient to evaluate the effective connectivity within the DMN region. In frail subjects, a shorter scan duration is more favourable.
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Musaeus, Christian Sandøe, Louise Baruël Johansen, Steen Hasselbalch, Nina Beyer, Peter Høgh, Hartwig Roman Siebner, and Kristian Steen Frederiksen. "Sixteen Weeks of Aerobic Exercise does not Alter Resting-state Connectivity of the Precuneus in Patients with Alzheimer’s Disease." Current Alzheimer Research 19, no. 2 (February 2022): 171–77. http://dx.doi.org/10.2174/1567205019666220304091241.
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Introduction: In healthy elderly persons and patients with mild cognitive impairment, physical exercise can increase functional brain connectivity in the default mode network (DMN) measured by restingstate functional magnetic resonance imaging (rs-fMRI). However, no studies have so far investigated the effect of physical exercise on functional resting-state connectivity in the DMN in patients with Alzheimer’s disease (AD). Objective: In a single-blinded randomized controlled trial, we assessed the effects of an aerobic exercise intervention of 16 weeks of physical exercise on DMN connectivity using rs-fMRI in patients with AD. Methods: Forty-five patients were randomly assigned to either a control or exercise group. The exercise group performed 60-min of aerobic exercise three times per week for 16 weeks. All the patients underwent whole-brain rs-fMRI at 3 T, at baseline, and after 16 weeks. Since the posterior cingulate cortex (PCC) and adjacent precuneus constitute a central hub of the DMN, this parietal region was defined as region-ofinterest and used as the seed region for functional connectivity analysis of the rs-fMRI data treating age and gender as covariates. Results: Neither seed-based analysis, seeded in the PCC/precuneus region nor ICA-based analyses, focusing on components of the DMN network, showed any exercise-induced changes in functional resting-state connectivity from baseline to follow-up. Conclusion: 16 weeks of aerobic exercise does not modify functional connectivity of the PCC/precuneus region in patients with AD. A longer intervention may be needed to show the effect of exercise on brain connectivity. Clinical Trial Registration Number: The trial was registered at ClinicalTrials.gov (identifier: NCT01681602) on September 10, 2012.
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Al-Hiyali, Mohammed Isam, Norashikin Yahya, Ibrahima Faye, Maged S. Al-Quraishi, and Abdulhakim Al-Ezzi. "Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder." Applied Sciences 12, no. 18 (September 18, 2022): 9339. http://dx.doi.org/10.3390/app12189339.
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The study of functional connectivity (FC) of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) has gained traction for uncovering FC patterns related to autism spectrum disorder (ASD). It is believed that the neurodynamic components of neuroimaging data enhance the measurement of the FC of brain nodes. Hence, methods based on linear correlations of rs-fMRI may not accurately represent the FC patterns of brain nodes in ASD patients. In this study, we proposed a new biomarker for ASD detection based on wavelet coherence and singular value decomposition. In essence, the proposed method provides a novel feature-vector based on extraction of the principal component of the neuronal dynamic FC patterns of rs-fMRI BOLD signals. The method, known as principal wavelet coherence (PWC), is implemented by applying singular value decomposition (SVD) on wavelet coherence (WC) and extracting the first principal component. ASD biomarkers are selected by analyzing the relationship between ASD severity scores and the amplitude of wavelet coherence fluctuation (WCF). The experimental rs-fMRI dataset is obtained from the publicly available Autism Brain Image Data Exchange (ABIDE), and includes 505 ASD patients and 530 normal control subjects. The data are randomly divided into 90% for training and cross-validation and the remaining 10% unseen data used for testing the performance of the trained network. With 95.2% accuracy on the ABIDE database, our ASD classification technique has better performance than previous methods. The results of this study illustrate the potential of PWC in representing FC dynamics between brain nodes and opens up possibilities for its clinical application in diagnosis of other neuropsychiatric disorders.
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Guan, Sihai, Dongyu Wan, Yanmiao Yang, and Bharat Biswal. "Sources of multifractality of the brain rs-fMRI signal." Chaos, Solitons & Fractals 160 (July 2022): 112222. http://dx.doi.org/10.1016/j.chaos.2022.112222.
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