Posts Tagged diffusion tensor imaging

[BLOG POST] Brain Imaging: What Are the Different Types? – BrainLine

Positron Emission Topography
Positron Emission Topography (PET) measures brain metabolism. Different applications of PET allow one to “see” pathology associated with Alzheimer’s disease, for instance, that cannot be visualized any other way. Used in a different way, PET also allows doctors to see how different areas of the brain use oxygen or glucose – both very important to understanding not just what the damage might look like but also how the brain provides energy to itself.
T1-Weighted MRI
The T1-Weighted MRI is the standard imaging test and part of every general MRI exam. It provides doctors with a very clear view of brain anatomy and structure. It can also show damage in brain injury but generally only when the damage is very significant.
T2-Weighted MRI
The T2-Weighted MRI is also a standard part of every MRI exam. But unlike T1-weighted imaging, the T2 allows visualization of severe diffuse axonal injury such as what is expected following severe TBI.
Diffusion Weighted Magnetic Resonance Imaging
Diffusion Weighted MRI (DWI) shows alterations in tissue integrity. In ischemic injury — such as many types of stroke or when blood is not able to get to all parts of the brain — there is a chemical reaction in the cells. As the cells die because of lack of blood flow (with oxygen), there is an increase in sodium and this changes (increases) the amount of water in the tissue. DWI is very sensitive to this change. In fact, using DWI, doctors can identify a stroke or ischemic injury within seconds of occurrance.
Fluid-Attenuated Inversion Recovery MRI
Fluid-Attenuated Inversion Recovery (FLAIR) MRI is also sensitive to water content in brain tissue. This is very useful in patients who have reductions in brain tissue following an injury. Most commonly, however, FLAIR is used to visualize alterations in tissue in diseases such as multiple sclerosis.
Diffusion Tensor Imaging
Diffusion Tensor Imaging (DTI) shows white matter tracts in brain tissue. These tracts allow different parts of the brain to talk to each other. Think of the brain as if it were a computer. With DTI doctors can see and measure the “cables” connecting parts of the brain. DTI can provide information about damage to parts of the nervous system as well as about connections among brain regions.
Gradient Record MRI
Gradient Record MRI (GRE) shows blood or hemorrhaging in the brain tissue. This is very important in acute head injury. CT scans are also very useful in this stage but sometimes miss very small bleeds ― or so called microbleeds ― in the brain. MRI and types of MRI more sensitive to blood can identify these and allow doctors to monitor the patient.
Functional MRI
Functional MRI (fMRI) is a newer type of MRI that takes advantage of the iron in blood and the fact that when neurons fire there is ― eventually ― an increase in local iron in the areas where the neurons fired. For this imaging test, doctors ask patients to do something while in the MRI machine like opening and closing their right hand for 30 seconds and then opening and closing their left hand for 30 seconds. Then, the doctors model the change in signal associated with an increase in blood related to that task. So, areas involved in opening the right hand will show increased signal. This allows images to be created that reveal how the brain does tasks. This is potentially useful in TBI when the brain structures all appear normal but the brain is functioning in a different way. It is important to know that fMRI is not approved for clinical use for diagnosis of TBI.

via Brain Imaging: What Are the Different Types? | BrainLine

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[Abstract] Impact of transcranial direct current stimulation on structural plasticity of the somatosensory system

Significance Recently, it has been shown that repeated anodal transcranial direct current stimulation (tDCS) applied to the somatosensory system induces functionalchanges at its site of application in the primary somatosensory cortex and therefore at an early stage of information processing (Hilgenstock, Weiss, Huonker, & Witte, ). The present study complements this finding by showing that tDCS also affects the somatosensory system on a structural level, however, at a late stage of decision making, probably at the stage of decision readout. Thus, tDCS seems capable of inducing changes at all levels of the somatosensory processing hierarchy.


While there is a growing body of evidence regarding the behavioral and neurofunctional changes in response to the longitudinal delivery of transcranial direct current stimulation (tDCS), there is limited evidence regarding its structural effects. Therefore, the present study was intended to investigate the effect of repeatedly applied anodal tDCS over the primary somatosensory cortex on the gray matter (GM) and white matter (WM) compartment of the brain. Structural tDCS effects were, moreover, related to effects evidenced by functional imaging and behavioral assessment. tDCS was applied over the course of 5 days in 25 subjects with concomitant assessment of tactile acuity of the right and left index finger as well as imaging at baseline, after the last delivery of tDCS and at follow‐up 4 weeks thereafter. Irrespective of the stimulation condition (anodal vs. sham), voxel‐based morphometry revealed a behaviorally relevant decrease of GM in the precuneus co‐localized with a functional change of its activity. Moreover, there was a decrease in GM of the bilateral lingual gyrus and the right cerebellum. Diffusion tensor imaging analysis showed an increase of fractional anisotropy exclusively in the tDCSanodal condition in the left frontal cortex affecting the final stretch of a somatosensory decision making network comprising the middle and superior frontal gyrus as well as regions adjacent to the genu of the corpus callosum. Thus, this is the first study in humans to identify structural plasticity in the GM compartment and tDCS‐specific changes in the WM compartment in response to somatosensory learning.


via Impact of transcranial direct current stimulation on structural plasticity of the somatosensory system – Hirtz – 2018 – Journal of Neuroscience Research – Wiley Online Library

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[ARTICLE] Impact of transcranial direct current stimulation on structural plasticity of the somatosensory system – Full Text


While there is a growing body of evidence regarding the behavioral and neurofunctional changes in response to the longitudinal delivery of transcranial direct current stimulation (tDCS), there is limited evidence regarding its structural effects. Therefore, the present study was intended to investigate the effect of repeatedly applied anodal tDCS over the primary somatosensory cortex on the gray matter (GM) and white matter (WM) compartment of the brain. Structural tDCS effects were, moreover, related to effects evidenced by functional imaging and behavioral assessment. tDCS was applied over the course of 5 days in 25 subjects with concomitant assessment of tactile acuity of the right and left index finger as well as imaging at baseline, after the last delivery of tDCS and at follow‐up 4 weeks thereafter. Irrespective of the stimulation condition (anodal vs. sham), voxel‐based morphometry revealed a behaviorally relevant decrease of GM in the precuneus co‐localized with a functional change of its activity. Moreover, there was a decrease in GM of the bilateral lingual gyrus and the right cerebellum. Diffusion tensor imaging analysis showed an increase of fractional anisotropy exclusively in the tDCSanodal condition in the left frontal cortex affecting the final stretch of a somatosensory decision making network comprising the middle and superior frontal gyrus as well as regions adjacent to the genu of the corpus callosum. Thus, this is the first study in humans to identify structural plasticity in the GM compartment and tDCS‐specific changes in the WM compartment in response to somatosensory learning.


Recently, we reported effects of repeatedly applied anodal transcranial direct current stimulation (tDCS) on tactile acuity learning of the dominant (right) index finger (IF) and concomitant neural changes as investigated by functional magnetic resonance imaging (fMRI) (Hilgenstock, Weiss, Huonker, & Witte, 2016). Relying on the same study, this article focuses on the structural underpinnings of these findings as investigated by voxel‐based morphometry (VBM) and diffusion tensor imaging (DTI).

tDCS is the application of a weak current exerting local effects on membrane potential (Bindman, 1965; Purpura & McMurtry, 1965) and neurotransmitter release (e.g., Clark, Coffman, Trumbo, & Gasparovic, 2011; Hone‐Blanchet, Edden, & Fecteau, 2016) as well as global effects on network functioning (e.g., Bachtiar, Near, Johansen‐Berg, & Stagg, 2015; Kim, Stephenson, Morris, & Jackson, 2014; Polania, Nitsche, & Paulus, 2011; Polania, Paulus, & Nitsche, 2012). Therefore, tDCS has widely been applied to study cognition, motor, and somatosensory functioning (e.g., Das, Holland, Frens, & Donchin, 2016; Stagg & Nitsche, 2011) but also to explore its beneficial potential in pathological states (e.g., Allman et al., 2016; Lindenberg, Renga, Zhu, Nair, & Schlaug, 2010; Lindenberg, Zhu, & Schlaug, 2012; Mori et al., 2013). With regard to the somatosensory system, anodal tDCS induces short‐term (e.g., Fujimoto, Yamaguchi, Otaka, Kondo, & Tanaka, 2014; Fujimoto et al., 2016; Ragert, Vandermeeren, Camus, & Cohen, 2008) as well as long‐term (Hilgenstock et al., 2016) effects on tactile acuity. After 5 days of anodal tDCS delivery, there was a profound and bilateral improvement of tactile acuity that persisted for at least 4 weeks. These improvements in tactile acuity were accompanied by changes in brain metabolism interpreted to indicate a more effective recruitment of neural machinery to process somatosensory information (Hilgenstock et al., 2016). Yet, despite the widespread application of tDCS and insights into changes in brain metabolism and connectivity, structural changes in the gray matter (GM) and white matter (WM) compartment of the brain in response to its repeated application have hardly been investigated.

Recently, Allman et al. (2016) were the first to show the capability of tDCS to induce structural changes in the GM compartment of the brain in response to repeatedly applied tDCS in stroke patients undergoing a course of 9 days of tDCS delivery with concomitant daily motor training. To the best of the authors’ knowledge, no study has investigated changes in GM in the somatosensory system in response to somatosensory training or the delivery of tDCS. Studies in the blind, however, indicate changes in GM focusing on the visual system. For example, Modi, Bhattacharya, Singh, Tripathi, and Khushu (2012) observed GM decreases in the lingual gyrus (primary visual cortex), the precuneus, the cerebellum, especially lobule VIIIa and intraparietal areas as well as an GM increase in the middle frontal gyrus (BA 6). Likewise, Voss, Pike, and Zatorre (2014) observed a significantly lower GM density in parts of the visual system and adjacent regions (pre‐/cuneus) in late blinded opposed to sighted individuals.

Changes in the WM compartment of the brain have primarily been investigated by studying fractional anisotropy (FA) (Zheng & Schlaug, 2015) that indicates organizational and directional changes in the diffusivity of water molecules in the WM compartment (Basser, 1995). Lindenberg, Nachtigall, Meinzer, Sieg, and Flöel (2013) could show that effects of tDCS on motor performance in stroke patients depended on the integrity of transcallosal and corticospinal fibers as characterized by FA (Lindenberg et al., 20122013). Moreover, Zheng and Schlaug (2015) were the first to provide evidence of a behaviorally relevant increase in FA of the so‐called alternate motor fibers (cortico‐rubro‐spinal and cortico‐reticulo‐spinal fibers) in response to repeated anodal tDCS in stroke patients. While there is no study investigating FA changes in the somatosensory domain in response to tDCS, Debowska et al. (2016) revealed changes after training of Braille reading in sighted individuals affecting the primary and secondary somatosensory system, the visual system, and the middle and superior frontal gyrus. In the blind, there is a decrease in WM in the visual system (BA 17, 18) and an increase in the superior frontal gyrus (Modi et al., 2012).

Thus, this article was intended to provide insight into where changes in tactile (acuity) perception emerge both in the GM and WM compartment of the brain (sham stimulation) and how these changes are modified by the repeated delivery of anodal tDCS. Moreover, we were interested in how somatosensory learning (sham stimulation) and its modification by anodal tDCS are implemented by combining findings from the analysis of VBM and DTI data with our previously reported findings from the analysis of fMRI and behavioral data (Hilgenstock et al., 2016). To this end, the analysis of fMRI data will be extended. Given the current state of research, we hypothesized that somatosensory learning and the repeated delivery of anodal tDCS will affect the visual system and its adjacent brain regions as well as prefrontal areas, especially the middle and superior frontal gyrus. There are only a few reports of sex‐specific tDCS‐induced effects (e.g., Chaieb, Antal, & Paulus, 2008; Fumagalli et al., 2010; Kuo, Paulus, & Nitsche, 2006). Yet, to also investigate the possibility of sex‐specific effects of tDCS in the somatosensory domain, we conducted additional exploratory analyses.[…]


Continue —> Impact of transcranial direct current stimulation on structural plasticity of the somatosensory system – Hirtz – 2018 – Journal of Neuroscience Research – Wiley Online Library

Figure 2 
(a) Significant decrease of gray matter (GM) density (red) and increase of the BOLD response (green) over the course of study projected on the MNI152 template provided by FSL, family‐wise error‐corrected on a cluster‐level at p < .05. (b) Changes in the BOLD response (green) over the course of the study and changes in relative GM volume (red) relative to baseline at the fifth day of the study (T2) and at follow‐up 4 weeks thereafter (T3) separately plotted for the sham (dark green/dark red) and anodal (light green/light red) condition (Mean ± SEM). (c) Pearson correlation between changes in relative GM volume at T3 relative to T1 and the GOT threshold (millimeter) of the right and left IF. Regression line and 95% confidence interval [Color figure can be viewed at]

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[Abstract+References] Longitudinal Structural and Functional Differences Between Proportional and Poor Motor Recovery After Stroke

Background. Evolution of motor function during the first months after stroke is stereotypically bifurcated, consisting of either recovery to about 70% of maximum possible improvement (“proportional recovery, PROP”) or in little to no improvement (“poor recovery, POOR”). There is currently no evidence that any rehabilitation treatment will prevent POOR and favor PROP. Objective. To perform a longitudinal and multimodal assessment of functional and structural changes in brain organization associated with PROP. Methods. Fugl-Meyer Assessments of the upper extremity and high-density electroencephalography (EEG) were obtained from 63 patients, diffusion tensor imaging from 46 patients, at 2 and 4 weeks (T0) and at 3 months (T1) after stroke onset. Results. We confirmed the presence of 2 distinct recovery patterns (PROP and POOR) in our sample. At T0, PROP patients had greater integrity of the corticospinal tract (CST) and greater EEG functional connectivity (FC) between the affected hemisphere and rest of the brain, in particular between the ventral premotor and the primary motor cortex. POOR patients suffered from degradation of corticocortical and corticofugal fiber tracts in the affected hemisphere between T0 and T1, which was not observed in PROP patients. Better initial CST integrity correlated with greater initial global FC, which was in turn associated with less white matter degradation between T0 and T1. Conclusions. These findings suggest links between initial CST integrity, systems-level cortical network plasticity, reduction of white matter atrophy, and clinical motor recovery after stroke. This identifies candidate treatment targets.

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via Longitudinal Structural and Functional Differences Between Proportional and Poor Motor Recovery After StrokeNeurorehabilitation and Neural Repair – Adrian G. Guggisberg, Pierre Nicolo, Leonardo G. Cohen, Armin Schnider, Ethan R. Buch, 2017

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[ARTICLE] Body-Machine Interfaces after Spinal Cord Injury: Rehabilitation and Brain Plasticity – Full Text HTML


The purpose of this study was to identify rehabilitative effects and changes in white matter microstructure in people with high-level spinal cord injury following bilateral upper-extremity motor skill training. Five subjects with high-level (C5–C6) spinal cord injury (SCI) performed five visuo-spatial motor training tasks over 12 sessions (2–3 sessions per week). Subjects controlled a two-dimensional cursor with bilateral simultaneous movements of the shoulders using a non-invasive inertial measurement unit-based body-machine interface. Subjects’ upper-body ability was evaluated before the start, in the middle and a day after the completion of training. MR imaging data were acquired before the start and within two days of the completion of training. Subjects learned to use upper-body movements that survived the injury to control the body-machine interface and improved their performance with practice. Motor training increased Manual Muscle Test scores and the isometric force of subjects’ shoulders and upper arms. Moreover, motor training increased fractional anisotropy (FA) values in the cingulum of the left hemisphere by 6.02% on average, indicating localized white matter microstructure changes induced by activity-dependent modulation of axon diameter, myelin thickness or axon number. This body-machine interface may serve as a platform to develop a new generation of assistive-rehabilitative devices that promote the use of, and that re-strengthen, the motor and sensory functions that survived the injury.

1. Introduction

Despite progress in the field of assistive technologies for people who suffered an injury to the spinal cord, most of the current devices to control computers and wheelchairs are set in place to require as little physical effort from the user as possible, and little attention has been paid to maintaining and strengthening the neural and muscular resources that survived the injury [1,2,3,4]. Spinal cord injury (SCI) leads to motor impairment, weakness, muscular and cortical atrophy and altered reflexes, and these have been shown to progress further with lack of exercise [5,6,7,8,9,10]. Even in individuals with injuries to the cervical spinal cord, some motor and sensory capacities may remain available in the upper body. Several studies have shown that using their remaining functions and keeping an active body is critical for people with SCI in order to avoid the collateral effects of paralysis and to potentially recover some of the lost mobility [5,6,7,11]. Therefore, it is crucial to develop the next generation of assistive-rehabilitative devices that promote learning through upper-body coordination.
Acquisition, retention and refinement of motor skills all rely on the capability of the nervous system to create new patterns of neural activation for accomplishing new tasks and for recovering lost motor functions [12]. Recent advances in neural imaging have allowed learning studies on juggling [13], balance [14] and body-machine interfaces (BMIs) by our group [15], to demonstrate motor skill learning-induced structural changes of cortical and subcortical areas in both gray matter and white matter by using diffusion tensor imaging (DTI). DTI non-invasively measures the direction and rate of water diffusion within tissue. White matter integrity is commonly measured by fractional anisotropy (FA), a normalized measure of the variance of the diffusion ellipsoid at each voxel [16]. FA values for white matter tissue have been shown to be affected by physiological parameters, such as axon diameter, axon number and myelin thickness [17].
Loss of somatosensory afference leads to functional cortical reorganization [18,19,20]. SCI has been shown to lead to spinal cord atrophy, cortical atrophy of primary and sensory cortex [8], descending motor tracts [9] and cortical reorganization of the sensorimotor system [8,10], and the degree of cortical reorganization is associated with the level of disability. Although the goal of most SCI treatments is to re-establish neural connections in order to restore motor function, it is unclear whether the anatomical and functional changes that follow injury can be reversed.
In this study, we investigated the rehabilitative effects and learning-induced changes in the brain white matter microstructure of people with high-level SCI after they practiced coordinated upper-body movements to control a computer cursor through a novel body-machine interface. Subjects learned to use the remaining ability of their shoulders and upper arms to perform movements that controlled a computer cursor to complete different related tasks. Complementary to [15], the purpose of this study was to identify changes in motor function and white matter by comparing clinical scores and FA values pre- and post-bilateral upper-body motor skill training in people with a high-level spinal cord injury. We started from the assumption that motor learning is likely to be associated with different brain reorganization in unimpaired subjects compared to subjects with tetraplegia, in consideration also of the greater need for the reorganization of motor functions in the latter group.

Continue —> Brain Sciences | Free Full-Text | Body-Machine Interfaces after Spinal Cord Injury: Rehabilitation and Brain Plasticity | HTML

Figure 5. Regions showing lower fractional anisotropy (FA) in spinal cord injury (SCI) subjects compared to controls. (A) Brain regions associated with motor function used to perform tract-based spatial statistics (TBSS) and ROI analyses; (B) TBSS results. Regions showing significantly higher (red-yellow) and lower (blue-light blue) FA values in SCI versus control subjects overlaid over the standard Montreal Neurological Institute (MNI)152 T1-weighted anatomical scan (p < 0.05, uncorrected). The location of each slice in Montreal Neurological Institute space is shown at the lower left section. a-s-pCR, anterior, superior and posterior corona radiata; CG, cingulum; g-bCC, genu and body of corpus callosum; a-pIC, anterior and posterior limbs of internal capsule.

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[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.35

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.8,1012 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.

Continue —> Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? A Systematic Review – Aug 08, 2016


Figure 1. Evidence search strategy diagram.

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[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.

Source: Role of corpus callosum integrity in arm function differs based on motor severity after stroke

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[ARTICLE] Prediction of Upper Limb Motor Recovery after Subacute Ischemic Stroke Using Diffusion Tensor Imaging: A Systematic Review and Meta-Analysis – Full Text


Early evaluation of the pyramidal tract using Diffusion Tensor Imaging (DTI) is a prerequisite to decide the optimal treatment or to assess appropriate rehabilitation. The early predictive value of DTI for assessing motor and functional recovery in ischemic stroke (IS) has yielded contradictory results. The purpose is to systematically review and summarize the current available literature on the value of Fractional Anisotropy (FA) parameter of the DTI in predicting upper limb motor recovery after sub-acute IS. MEDLINE, PubMed, EMBASE, Google Scholar and Cochrane CENTRAL searches were conducted from January 1, 1950, to July 31, 2015, which was supplemented with relevant articles identified in the references. Correlation between FA and upper limb motor recovery measure was done. Heterogeneity was examined using Higgins I-squared, Tau-squared. Summary of correlation coefficient was determined using Random Effects model. Out of 166 citations, only eleven studies met the criteria for inclusion in the systematic review and six studies were included in the meta-analysis. A random effects model revealed that DTI parameter FA is a significant predictor for upper limb motor recovery after sub-acute IS [Correlation Coefficient=0.82; 95% Confidence Interval-0.66 to 0.90, P value<0.001]. Moderate heterogeneity was observed (Tau-squared=0.12, I-squared=62.14). The studies reported so far on correlation between DTI and upper limb motor recovery are few with small sample sizes. This meta-analysis suggests strong correlation between DTI parameter FA and upper limb motor recovery. Well-designed prospective trials embedded with larger sample size are required to establish these findings.


Stroke is a major leading cause of death and disability worldwide especially in the elderly population [1]. Upper limb motor weakness is one of the most frequent complications after stroke with over 50% of stroke patients experiencing residual motor deficit [2]. Despite advances in treatment of acute ischemic stroke (IS) and post-stroke rehabilitation, the dependency rate after stroke still reaches 20%-30% [3]. Prognostication of upper limb motor outcomes after stroke is an important for specific rehabilitation strategies and final motor outcomes but, considered a difficult task. Many studies have tried to predict motor outcome in hemiparetic stroke patients using clinical findings [4,5], electrophysiological methods [6,7], and functional neuroimaging [8,9]. However, these studies have an inherent weakness that they were unable to visualize the corticospinal tract (CST), the most important structure for motor control, especially for fine motor control of the hand in humans [10].
Diffusion tensor imaging (DTI) is an advance non-invasive magnetic resonance imaging technique used to characterize the orientation properties of diffusion process of water molecules. DTI has a unique advantage in visualization and estimation of CST which is the most important neural tract for mainly upper limb motor function [11]. DTI permits the imaging of axonal pathways of the living brain and provides information about tissue microstructure by measuring fractional anisotropy (FA) [12]. FA is an index of the diffusion characteristics of water molecules preferentially directed along the axis of major axonal pathways. FA of the entire tract, acquired early after stroke, reflect acute and permanent damage to pyramidal tracts to determine clinical motor deficit and outcome. A tissue is considered to be fully isotropic when its FA is equal to 0, and fully anisotropic when its FA is equal to 1 [13].
Over the past two decades, numerous cross-sectional DTI studies have examined the relationships between age and the degree of anisotropy FA in white matter tracts [14]. Cross-sectional studies have demonstrated that older adults display lower FA values and higher mean diffusivity and radial diffusivity values compared with younger adults [15,16], with age correlations relatively weak during adulthood and stronger in senescence [17,18]. Currently, the most widely used invariant measure of anisotropy is FA described originally by Basser and Pierpaoli [12]. In the parametric data obtained from DTI, taking advantage of the much larger FA values of highly directional white matter structures, FA images are used to distinguish white matter and non-white matter tissues [19]. Studies that have examined small homogeneous samples of subcortical stroke patients have found that large asymmetries in FA are associated with poorer motor recovery [20,21]. Findings from recent studies have demonstrated the predictive value of DTI for motor outcome after stroke [22,23], however, it is not yet used routinely to make a prognosis but there have been some interesting recent developments in this area. Therefore, the purpose of this review is to establish the predictive value of DTI for upper limb motor recovery in IS patients.

Continue —> Prediction of Upper Limb Motor Recovery after Subacute Ischemic Stroke Using Diffusion Tensor Imaging: A Systematic Review and Meta-Analysis

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[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.

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[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.

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