Functional magnetic resonance imaging as experienced by stroke survivors.
Ontology highlight
ABSTRACT:
SUBMITTER: Hadidi NN
PROVIDER: S-EPMC5551904 | biostudies-other | 2014 Sep-Oct
REPOSITORIES: biostudies-other
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Project description:Focal brain lesions can have important remote effects on the function of distant brain regions. The resulting network dysfunction may contribute significantly to behavioral deficits observed after stroke. This study investigates the behavioral significance of changes in the coherence of spontaneous activity in distributed networks after stroke by measuring resting state functional connectivity (FC) using functional magnetic resonance imaging.In acute stroke patients, we measured FC in a dorsal attention network and an arm somatomotor network, and determined the correlation of FC with performance obtained in a separate session on tests of attention and motor function. In particular, we compared the behavioral correlation with intrahemispheric FC to the behavioral correlation with interhemispheric FC.In the attention network, disruption of interhemispheric FC was significantly correlated with abnormal detection of visual stimuli (Pearson r with field effect = -0.624, p = 0.002). In the somatomotor network, disruption of interhemispheric FC was significantly correlated with upper extremity impairment (Pearson r with contralesional Action Research Arm Test = 0.527, p = 0.036). In contrast, intrahemispheric FC within the normal or damaged hemispheres was not correlated with performance in either network. Quantitative lesion analysis demonstrated that our results could not be explained by structural damage alone.These results suggest that lesions cause state changes in the spontaneous functional architecture of the brain, and constrain behavioral output. Clinically, these results validate using FC for assessing the health of brain networks, with implications for prognosis and recovery from stroke, and underscore the importance of interhemispheric interactions.
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Project description:For ∼40 years, thinking about reasoning has been dominated by dual-process theories. This model, consisting of two distinct types of human reasoning, one fast and effortless and the other slow and deliberate, has also been applied to medical diagnosis. Medical experts are trained to diagnose patients based on their symptoms. When symptoms are prototypical for a certain diagnosis, practitioners may rely on fast, recognition-based reasoning. However, if they are confronted with ambiguous clinical information slower, analytical reasoning is required. To examine the neural underpinnings of these two hypothesized forms of reasoning, 16 highly experienced clinical neurologists were asked to diagnose two types of medical cases, straightforward and ambiguous cases, while functional magnetic resonance imaging was being recorded. Compared with reading control sentences, diagnosing cases resulted in increased activation in brain areas typically found to be active during reasoning such as the caudate nucleus and frontal and parietal cortical regions. In addition, we found vast increased activity in the cerebellum. Regarding the activation differences between the two types of reasoning, no pronounced differences were observed in terms of regional activation. Notable differences were observed, though, in functional connectivity: cases containing ambiguous information showed stronger connectivity between specific regions in the frontal, parietal and temporal cortex in addition to the cerebellum. Based on these results, we propose that the higher demands in terms of controlled cognitive processing during analytical medical reasoning may be subserved by stronger communication between key regions for detecting and resolving uncertainty.
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Project description:ObjectiveImagined walking has yielded insights into normal locomotor control and could improve understanding of neurologic gait dysfunction. This study evaluated brain activation during imagined walking in chronic stroke.MethodsTen persons with stroke and 10 matched controls completed a walking test battery and a magnetic resonance imaging session including imagined walking and knee extension tasks. Brain activations were compared between tasks and groups. Associations between activations and composite gait score were also calculated, while controlling for lesion load.ResultsStroke and worse gait score were each associated with lesser overall brain activation during knee extension but greater overall activation during imagined walking. During imagined walking, the stroke group significantly activated the primary motor cortex lower limb region and cerebellar locomotor region. Better walking function was associated with less activation of these regions and greater activation of medial superior frontal gyrus area 9.ConclusionsCompared with knee extension, imagined walking was less sensitive to stroke-related deficits in brain activation but better at revealing compensatory changes, some of which could be maladaptive.SignificanceThe identified associations for imagined walking suggest potential neural mechanisms of locomotor adaptation after stroke, which could be useful for future intervention development and prognostication.
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Project description:The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. Firstly, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Secondly, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, which decomposes fMRI data into modes. Each mode comprises of a neural activation time course and a spatial mapping. Two versions of HMF are proposed in which the neural activation time course of each mode is convolved with either the canonical HRF or predetermined subject-specific HRFs. Firstly, HMF with the canonical HRF is applied to two open-source cohorts. These cohorts comprise of several task experiments including motor, incidental memory, spatial coherence discrimination, verbal discrimination task and a very short localization task, engaging multiple parts of the eloquent cortex. HMF modes were obtained whose neural activation time course followed original task timings and whose corresponding spatial map matched cortical areas known to be involved in the respective task processing. Secondly, the alignment of these neural activation time courses to task timings were further improved by replacing the canonical HRF with subject-specific HRFs during HMF mode computation. In addition to task-related modes, HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks. The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.
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Project description:BackgroundPrevious functional magnetic resonance imaging (fMRI) sleep studies have been hampered by the difficulty of obtaining extended amounts of sleep in the sleep-adverse environment of the scanner and often have resorted to manipulations such as sleep depriving subjects before scanning. These manipulations limit the generalizability of the results.New methodThe current study is a methodological validation of procedures aimed at obtaining all-night fMRI data in sleeping subjects with minimal exposure to experimentally induced sleep deprivation. Specifically, subjects slept in the scanner on two consecutive nights, allowing the first night to serve as an adaptation night.Results/comparison with existing method(s)Sleep scoring results from simultaneously acquired electroencephalography data on Night 2 indicate that subjects (n = 12) reached the full spectrum of sleep stages including slow-wave (M = 52.1 min, SD = 26.5 min) and rapid eye movement (REM, M = 45.2 min, SD = 27.9 min) sleep and exhibited a mean of 2.1 (SD = 1.1) nonREM-REM sleep cycles.ConclusionsIt was found that by diligently applying fundamental principles and methodologies of sleep and neuroimaging science, performing all-night fMRI sleep studies is feasible. However, because the two nights of the study were performed consecutively, some sleep deprivation from Night 1 as a cause of the Night 2 results is likely, so consideration should be given to replicating the current study with a washout period. It is envisioned that other laboratories can adopt the core features of this protocol to obtain similar results.
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Project description:Cerebral stroke is one of the most frequent causes of permanent disability or death in the western world and a major burden in healthcare system. The major portion is caused by acute ischemia due to cerebral artery occlusion by a clot. The minority of strokes is related to intracerebral hemorrhage or other sources. To limit the permanent disability in ischemic stroke patients resulting from irreversible infarction of ischemic brain tissue, major efforts were made in the last decade. To extend the time window for thrombolysis, which is the only approved therapy, several imaging parameters in computed tomography and magnetic resonance imaging (MRI) have been investigated. However, the current guidelines neglect the fact that the portion of potentially salvageable ischemic tissue (penumbra) is not dependent on the time window but the individual collateral blood flow. Within the last years, the differentiation of infarct core and penumbra with MRI using diffusion-weighted images (DWI) and perfusion imaging (PI) with parameter maps was established. Current trials transform these technical advances to a redefined patient selection based on physiological parameters determined by MRI. This review article presents the current status of MRI for acute stroke imaging. A special focus is the ischemic stroke. In dependence on the pathophysiology of cerebral ischemia, the basic principle and diagnostic value of different MRI sequences are illustrated. MRI techniques for imaging of the main differential diagnoses of ischemic stroke are mentioned. Moreover, perspectives of MRI for imaging-based acute stroke treatment as well as monitoring of restorative stroke therapy from recent trials are discussed.
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Project description:Group differences in resting state functional magnetic resonance imaging connectivity between individuals with autism and typically developing controls have been widely replicated for a small number of discrete brain regions, yet the whole-brain distribution of connectivity abnormalities in autism is not well characterized. It is also unclear whether functional connectivity is sufficiently robust to be used as a diagnostic or prognostic metric in individual patients with autism. We obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the entire grey matter (26.4 million connections) in a well-characterized set of 40 male adolescents and young adults with autism and 40 age-, sex- and IQ-matched typically developing subjects. A single resting state blood oxygen level-dependent scan of 8 min was used for the classification in each subject. A leave-one-out classifier successfully distinguished autism from control subjects with 83% sensitivity and 75% specificity for a total accuracy of 79% (P = 1.1 × 10(-7)). In subjects <20 years of age, the classifier performed at 89% accuracy (P = 5.4 × 10(-7)). In a replication dataset consisting of 21 individuals from six families with both affected and unaffected siblings, the classifier performed at 71% accuracy (91% accuracy for subjects <20 years of age). Classification scores in subjects with autism were significantly correlated with the Social Responsiveness Scale (P = 0.05), verbal IQ (P = 0.02) and the Autism Diagnostic Observation Schedule-Generic's combined social and communication subscores (P = 0.05). An analysis of informative connections demonstrated that region of interest pairs with strongest correlation values were most abnormal in autism. Negatively correlated region of interest pairs showed higher correlation in autism (less anticorrelation), possibly representing weaker inhibitory connections, particularly for long connections (Euclidean distance >10 cm). Brain regions showing greatest differences included regions of the default mode network, superior parietal lobule, fusiform gyrus and anterior insula. Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age. Classification scores of unaffected siblings of individuals with autism were more similar to those of the control subjects than to those of the subjects with autism. These findings indicate feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism.
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Project description:Background and purpose The dynamic alterations in spontaneous neural activity of the brain during the acute phase of post-stroke aphasia (PSA) remain unclear. Therefore, in this study, dynamic amplitude of low-frequency fluctuation (dALFF) was applied to explore abnormal temporal variability in local functional activity of the brain during acute PSA. Materials and methods Resting-state functional magnetic resonance imaging (rs-fMRI) data from 26 patients with PSA and 25 healthy controls (HCs) were acquired. The sliding window method was used to assess dALFF, with the k-means clustering method used to identify dALFF states. The two-sample t-test was applied to compare differences in dALFF variability and state metrics between the PSA and HC groups. Results (1) In the PSA group, greater variance of dALFF in the cerebellar network (CBN) and left fronto-temporo-parietal network (FTPN) was observed. (2) Three dALFF states were identified among all subjects. States 1 and 2 were identified in the PSA patients, and the two dALFF states shared a similar proportion. Moreover, the number of transitions between the two dALFF states was higher in the patients compared with that in HCs. Conclusion The results of this study provide valuable insights into brain dysfunction that occurs during the acute phase (6.00 ± 3.52 days) of PSA. The observed increase in variability of local functional activities in CBN and left FTPN may be related to the spontaneous functional recovery of language during acute PSA, and it also suggests that cerebellum plays an important role in language.
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