Posts Tagged diffusion tensor imaging
[ARTICLE] Body-Machine Interfaces after Spinal Cord Injury: Rehabilitation and Brain Plasticity – Full Text HTML
[ARTICLE] Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? – Full Text
Background. There is growing interest to establish recovery biomarkers, especially neurological biomarkers, in order to develop new therapies and prediction models for the promotion of stroke rehabilitation and recovery. However, there is no consensus among the neurorehabilitation community about which biomarker(s) have the highest predictive value for motor recovery. Objective. To review the evidence and determine which neurological biomarker(s) meet the high evidence quality criteria for use in predicting motor recovery. Methods. We searched databases for prognostic neuroimaging/neurophysiological studies. Methodological quality of each study was assessed using a previously employed comprehensive 15-item rating system. Furthermore, we used the GRADE approach and ranked the overall evidence quality for each category of neurologic biomarker. Results. Seventy-one articles met our inclusion criteria; 5 categories of neurologic biomarkers were identified: diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), conventional structural MRI (sMRI), and a combination of these biomarkers. Most studies were conducted with individuals after ischemic stroke in the acute and/or subacute stage (~70%). Less than one-third of the studies (21/71) were assessed with satisfactory methodological quality (80% or more of total quality score). Conventional structural MRI and the combination biomarker categories ranked “high” in overall evidence quality. Conclusions. There were 3 prevalent methodological limitations: (a) lack of cross-validation, (b) lack of minimal clinically important difference (MCID) for motor outcomes, and (c) small sample size. More high-quality studies are needed to establish which neurological biomarkers are the best predictors of motor recovery after stroke. Finally, the quarter-century old methodological quality tool used here should be updated by inclusion of more contemporary methods and statistical approaches.
There is growing interest in establishing stroke recovery biomarkers. Researchers define stroke recovery biomarkers as surrogate indicators of disease state that can have predictive value for recovery or treatment response.1 Specifically, previous studies have suggested that better understanding of neurological biomarkers, derived from brain imaging and neurophysiological assessments, is likely to move stroke rehabilitation research forward.1,2
Recovery biomarkers acquired during the acute and subacute phases (acute—within 1 week after onset; subacute—between 1 week and 3 months after onset) may be vital to set attainable neurorehabilitation goals and to choose proper therapeutic approaches based on the recovery capacity. Furthermore, motor recovery prediction using neurological biomarkers in the chronic phase (more than 3 months after onset) can be useful to determine whether an individual will benefit from specific therapeutic interventions applied after the normal period of rehabilitation has ended. Hence, use of recovery biomarkers is likely to improve customization of physical interventions for individual stroke survivors regarding their capacity for recovery, and to facilitate development of new neurorehabilitation approaches.
There have been fundamental changes in recovery biomarkers from simple clinical behavioral biomarkers to brain imaging and neurophysiological biomarkers. In particular, a number of recent studies have shown that neurologic biomarkers (ie, neuroimaging and/or neurophysiological measures of brain) are more predictive of motor recovery than clinical behavioral biomarkers.3–5
Although there is some evidence that neurological biomarkers are more valuable as predictors of motor recovery than clinical behavioral biomarkers, there are significant gaps between the published evidence and clinical usage. First, there is no consensus on which specific neurological biomarkers would be best for prediction models.4,6,7 Viable neurological biomarker of motor recovery have evolved from lesion size and location, prevalent in the early 1990s8 to more contemporary complex brain network analysis variables.9 Despite this evolution, there is a paucity of high-level evidence for determining the most critical neurological biomarkers of motor recovery. A number of literature reviews and systematic reviews of studies published since the 1990s aimed to identify the most appropriate biomarkers of motor recovery or functional independence.8,10–12 Among these reviews, only one by Schiemanck and colleagues8 assessed the evidence quality of neurologic biomarkers, while many focused on clinical measures (ie, clinical motor and/or functional measures).11 Their review was limited to only 13 studies that employed structural magnetic resonance imaging (sMRI) measures of lesion volume as neurologic biomarkers. Besides lesion volume derived from structural MRI, there are other viable neurological biomarkers of brain impairment. Therefore, this systematic review includes a broad set of relevant biomarkers for consideration as critical predictors for inclusion in motor recovery prediction models.
Furthermore, there is some evidence to suggest that multivariate prediction models that use neurological biomarkers in addition to clinical outcome measures are more accurate than those that use clinical outcome measures alone.2,13 However there is still no consensus about whether incorporating behavioral and neurological predictors in a multimodal prediction model is superior (ie, more accurate) to a univariate model that includes either behavioral or neurological predictors alone.
Taken together, this systematic review has 2 aims. The first is to conduct a critical and systematic comparison of selected studies to determine which neurological biomarker(s) is likely to have sufficient high-level evidence in order to render the most accurate prediction of motor recovery after stroke. The second aim is to identify whether adding clinical measures along with neurological biomarkers in the model improves the accuracy of the model compared to the models that use neurological biomarkers alone.
[Abstract] Role of corpus callosum integrity in arm function differs based on motor severity after stroke
- Corpus callosum structural integrity could impact motor function after stroke.Corpus callosum integrity was decreased and correlated with motor function.Correlation was strongest in the subgroup with relatively greater motor capacity.In subgroup with less motor capacity, only CST integrity correlated with motor function.
While the corpus callosum (CC) is important to normal sensorimotor function, its role in motor function after stroke is less well understood.
This study examined the relationship between structural integrity of the motor and sensory sections of the CC, as reflected by fractional anisotropy (FA), and motor function in individuals with a range of motor impairment level due to stroke.
Fifty-five individuals with chronic stroke (Fugl-Meyer motor score range 14 to 61) and 18 healthy controls underwent diffusion tensor imaging and a set of motor behavior tests. Mean FA from the motor and sensory regions of the CC and from corticospinal tract (CST) were extracted and relationships with behavioral measures evaluated. Across all participants, FA in both CC regions was significantly decreased after stroke (p < 0.001) and showed a significant, positive correlation with level of motor function. However, these relationships varied based on degree of motor impairment: in individuals with relatively less motor impairment (Fugl-Meyer motor score > 39), motor status correlated with FA in the CC but not the CST, while in individuals with relatively greater motor impairment (Fugl-Meyer motor score ≤ 39), motor status correlated with FA in the CST but not the CC.
The role interhemispheric motor connections play in motor function after stroke may differ based on level of motor impairment. These findings emphasize the heterogeneity of stroke, and suggest that biomarkers and treatment approaches targeting separate subgroups may be warranted.
[ARTICLE] Prediction of Upper Limb Motor Recovery after Subacute Ischemic Stroke Using Diffusion Tensor Imaging: A Systematic Review and Meta-Analysis – Full Text
[ARTICLE] A review of transcranial magnetic stimulation and multimodal neuroimaging to characterize post-stroke neuroplasticity – Full Text PDF
Following stroke, the brain undergoes various stages of recovery where the central nervous system can reorganize neural circuitry (neuroplasticity) both spontaneously and with the aid of behavioural rehabilitation and non-invasive brain stimulation. Multiple neuroimaging techniques can characterize common structural and functional stroke-related deficits, and importantly, help predict recovery of function. Diffusion tensor imaging (DTI) typically reveals increased overall diffusivity throughout the brain following stroke, and is capable of indexing the extent of white matter damage. Magnetic resonance spectroscopy (MRS) provides an index of metabolic changes in surviving neural tissue after stroke, serving as a marker of brain function. The neural correlates of altered brain activity after stroke have been demonstrated by abnormal activation of sensorimotor cortices during task performance, and at rest, using functional magnetic resonance imaging (fMRI). Electroencephalography (EEG) has been used to characterize motor dysfunction in terms of increased cortical amplitude in the sensorimotor regions when performing upper-limb movement, indicating abnormally increased cognitive effort and planning in individuals with stroke. Transcranial magnetic stimulation (TMS) work reveals changes in ipsilesional and contralesional cortical excitability in the sensorimotor cortices. The severity of motor deficits indexed using TMS has been linked to the magnitude of activity imbalance between the sensorimotor cortices.
In this paper we will provide a narrative review of data from studies utilizing DTI, MRS, fMRI, EEG and brain stimulation techniques focusing on TMS and its combination with uni and multi-modal neuroimaging methods to assess recovery after stroke. Approaches that delineate the best measures with which to predict or positively alter outcomes will be highlighted.
[REVIEW] Stroke Connectome and Its Implications for Cognitive and Behavioral Sequela of Stroke – Full Text Pdf
Systems-based approaches to neuroscience, using network analysis and the human connectome, have been adopted by many researchers by virtue of recent progress in neuroimaging and computational technologies. Various neurological disorders have been evaluated from a network perspective, including stroke, Alzheimer’s disease, Parkinson’s disease, and traumatic brain injury. Until now, dynamic processes after stroke and during recovery were investigated through multimodal neuroimaging techniques. Many studies have shown disruptions in structural and functional connectivity, including in large-scale neural networks, in patients with stroke sequela such as motor weakness, aphasia, hemianopia, neglect, and general cognitive dysfunction. A connectome-based approach might shed light on the underlying mechanisms of stroke sequela and the recovery process, and could identify candidates for individualized rehabilitation programs. In this review, we briefly outline the basic concepts of structural and functional connectivity, and the connectome. Then, we explore current evidence regarding how stroke lesions cause changes in connectivity and network architecture parameters. Finally, the clinical implications of perspectives on the connectome are discussed in relation to the cognitive and behavioral sequela of stroke.
An imaging study by Stanford University School of Medicine investigators has found distinct differences between the brains of patients with chronic fatigue syndrome and those of healthy people.
The findings could lead to more definitive diagnoses of the syndrome and may also point to an underlying mechanism in the disease process.
It’s not uncommon for CFS patients to face several mischaracterizations of their condition, or even suspicions of hypochondria, before receiving a diagnosis of CFS. The abnormalities identified in the study, to be published Oct. 29 in Radiology, may help to resolve those ambiguities, said lead author Michael Zeineh, MD, PhD, assistant professor of radiology.
“Using a trio of sophisticated imaging methodologies, we found that CFS patients’ brains diverge from those of healthy subjects in at least three distinct ways,” Zeineh said.
CFS affects between 1 million and 4 million individuals in the United States and millions more worldwide. Coming up with a more precise number of cases is tough because it’s difficult to actually diagnose the disease. While all CFS patients share a common symptom — crushing, unremitting fatigue that persists for six months or longer — the additional symptoms can vary from one patient to the next, and they often overlap with those of other conditions.
ARTICLE: Corticospinal Tract Integrity and Lesion Volume Play Different Roles in Chronic Hemiparesis and Its Improvement Through Motor Practice
…CST integrity correlated best in this small trial with chronic long-term status but not treatment-induced improvements. The CST may play a different role in the mechanisms mediating long-term outcome compared to those underlying practice-induced gains after a chronic plateau in motor function…