Posts Tagged functional magnetic resonance imaging

[OPINION ARTICLE] Can Functional Magnetic Resonance Imaging Generate Valid Clinical Neuroimaging Reports? – Full Text

imageRoland Beisteiner* Study Group Clinical fMRI, High Field MR Center, Department of Neurology, Medical University of Vienna, Vienna, Austria

A highly critical issue for applied neuroimaging in neurology—and particularly for functional neuroimaging—concerns the question of validity of the final clinical result. Within a clinical context, the question of “validity” often equals the question of “instantaneous repeatability,” because clinical functional neuroimaging is done within a specific pathophysiological framework. Here, not only every brain is different but also every pathology is different, and most importantly, individual pathological brains may rapidly change in short time.

Within the brain mapping community, the problem of validity and repeatability of functional neuroimaging results has recently become a major issue. In 2016, the Committee on Best Practice in Data Analysis and Sharing from the Organization for Human Brain Mapping (OHBM) created recommendations for replicable research in neuroimaging, focused on magnetic resonance imaging and functional magnetic resonance imaging (fMRI). Here, “replication” is defined as “Independent researchers use independent data and … methods to arrive at the same original conclusion.” “Repeatability” is defined as repeated investigations performed “with the same method on identical test/measurement items in the same test or measuring facility by the same operator using the same equipment within short intervals of time” (ISO 3534-2:2006 3.3.5). An intermediate position between replication and repeatability is defined for “reproducibility”: repeated investigations performed “with the same method on identical test/measurement items in different test or measurement facilities with different operators using different equipment” (ISO 3534-2:2006 3.3.10). Further definitions vary depending on the focus, be it the “measurement stability,” the “analytical stability,” or the “generalizability” over subjects, labs, methods, or populations.

The whole discussion was recently fueled by an PNAS article published by Eklund et al. (1), which claims that certain results achieved with widely used fMRI software packages may generate false-positive results, i.e., show brain activation where is none. More specifically, when looking at activation clusters defined by the software as being significant (clusterwise inference), the probability of a false-positive brain activation is not 5% but up to 70%. This was true for group as well as single subject data (2). The reason lies in an “imperfect” model assumption about the distribution of the spatial autocorrelation of functional signals over the brain. A squared exponential distribution was assumed but found not to be correct for the empirical data. This article received heavy attention and discussion in scientific and public media and a major Austrian newspaper titled “Doubts about thousands of brain research studies.” A recent PubMed analysis indicates already 69 publications citing the Eklund work. Critical comments by Cox et al. (3)—with focusing on the AFNI software results—criticize the authors for “their emphasis on reporting the single worst result from thousands of simulation cases,” which “greatly exaggerated the scale of the problem.” Other groups extended the work. With regard to the fact that “replicability of individual studies is an acknowledged limitation,” Eickhoff et al. (4) suggest that “Coordinate-based meta-analysis offers a practical solution to this limitation.” They claim that meta-analyses allow “filtering and consolidating the enormous corpus of functional and structural neuroimaging results” but also describe “errors in multiple-comparison corrections” in GingerALE, a software package for coordinate-based meta-analysis. One of their goals is to “exemplify and promote an open approach to error management.” More generally and probably also triggered by the Eklund paper, Nissen et al. (5) discuss the current situation that “Science is facing a ‘replication crisis’.” They focus on the publicability of negative results and model “the community’s confidence in a claim as a Markov process with successive published results shifting the degree of belief.” Important findings are, that “unless a sufficient fraction of negative results are published, false claims frequently can become canonized as fact” and “Should negative results become easier to publish … true and false claims would be more readily distinguished.”

As a consequence of this discussion, public skepticism about the validity of clinical functional neuroimaging arose. At first sight, this seems to be really bad news for clinicians. However, at closer inspection, it turns out that particularly the clinical neuroimaging community has already long been aware of the problems with standard (“black box”) analyses of functional data recorded from compromised patients with largely variable pathological brains. Quite evidently, methodological assumptions as developed for healthy subjects and implemented in standard software packages may not always be valid for distorted and physiologically altered brains. There are specific problems for clinical populations and particularly for defining the functional status of an individual brain (as opposed to a “group brain” in group studies). With task-based fMRI—the most important clinical application—the major problems may be categorized in “patient problems” and “methodological problems.”

Critical patient problems concern:

  • – Patient compliance may change quickly and considerably.
  • – The patient may “change” from 1 day to the other (altered vigilance, effects of pathology and medication, mood changes—depression, exhaustion).
  • – The clinical state may “change” considerably from patient to patient (despite all having the same diagnosis). This is primarily due to location and extent of brain pathology and compliance capabilities.

Critical methodological problems concern:

  • – Selection of clinically adequate experimental paradigms (note paresis, neglect, aphasia).
  • – Performance control (particularly important in compromised patients).
  • – Restriction of head motion (in patients artifacts may be very large).
  • – Clarification of the signal source (microvascular versus remote large vessel effects).
  • – Large variability of the contrast to noise ratio from run to run.
  • – Errors with inter-image registration of brains with large pathologies.
  • – Effects of data smoothing, definition of adequate functional regions of interest, and definition of essential brain activations.
  • – Difficult data interpretation requires specific clinical fMRI expertise and independent validation of the local hardware and software performance (preferably with electrocortical stimulation).

All these problems have to be recognized and specific solutions have to be developed depending on the question at hand—generation of an individual functional diagnosis or performance of a clinical group study. To discuss such problems and define solutions, clinical functional neuroimagers have already assembled early (Austrian Society for fMRI,1 American Society of Functional Neuroradiology2) and just recently the Alpine Chapter from the OHBM3 was established with a dedicated focus on applied neuroimaging. Starting in the 1990s (6), this community published a considerable number of clinical methodological investigations focused on the improvement of individual patient results and including studies on replication, repeatability, and reproducibility [compare (7)]. Early examples comprise investigations on fMRI signal sources (8), clinical paradigms (9), reduction of head motion artifacts (10), and fMRI validation studies (11, 12). Of course the primary goal of this clinical research is improvement of the validity of the final clinical result. One of the suggested clinical procedures focuses particularly on instantaneous replicability as a measure of validity [Risk Map Technique (1315); see Figure 1] with successful long-term clinical use. This procedure was developed for presurgical fMRI and minimizes methodological assumptions to stay as close to the original data as possible. This is done by avoiding data smoothing and normalization procedures and minimization of head motion artifacts by helmet fixation (avoiding artifacts instead of correcting them). It is interesting to note that in the Eklund et al. (1) analysis it was also the method with minimal assumptions (a non-parametric permutation), which was the only one that achieved correct (nominal) results. The two general ideas of the risk map technique are (a) to use voxel replicability as a criterion for functionally most important voxels (extracting activation foci = voxels with largest risk for a functional deficit when lesioned) and (b) to consider regional variability of brain conditions (e.g., close to tumor) by variation of the hemodynamic response functions (step function/HRF/variable onset latencies) and thresholds. The technique consists only of few steps, which can easily be realized by in house programming: (i) Record up to 20 short runs of the same task type to allow checking of repeatability. (ii) Define a reference function (e.g., step function with a latency of 1 TR). (iii) Calculate Pearson correlation r for every voxel and every run. (iv) Color code voxels according to their reliability at a given correlation threshold (e.g., r > 0.5): yellow voxels >75%, orange voxels >50%, red voxels >25% of runs need to be active. (v) Repeat (i)–(iv) with different reference functions (to our experience, a classical HRF and two step functions with different latencies are sufficient to evaluate most patients) and at different correlation thresholds (e.g., r > 0.2 to r > 0.9). The clinical fMRI expert performs a comprehensive evaluation of all functional maps with consideration of patient pathology, patient performance, and the distribution and level of artifacts [compare descriptions in Ref. (13, 15)]. The final clinical result is illustrated in Figure 1, and a typical interpretation would be: most reliable activation of the Wernicke area is found with a step function of 1 TR latency and shown at Pearson correlation r > 0.5. It is important to note that risk maps extract the most active voxel(s) within a given brain area and judgment of a “true” activation extent is not possible. However, due to the underlying neurophysiological principles [gradients of functional representations (16)], it is questionable whether “true” activation extents of fMRI activations can be defined with any technique.

 

Figure 1. Example for a missing language activation (Wernicke activity, white arrow) with a “black box” standard analysis (right, SPM12 applying motion regression and smoothing, voxelwise inference FWE <0.05, standard k = 25) using an overt language design [described in Ref. (17)]. Wernicke activity is detectable with the clinical risk map analysis (left) based on activation replicability (yellow = most reliabel voxels). Patient with left temporoparietal tumor.

The importance to check individual patient data from various perspectives instead of relying on a “standard statistical significance value,” which may not correctly reflect the individual patients signal situation, has also been stressed by other authors [e.g., Ref. (18)]. Of course, clinical fMRI—as all other applied neuroimaging techniques—requires clinical fMRI expertise and particularly pathophysiological expertise to be able to conceptualize where to find what, depending on the pathologies of the given brain. One should be aware that full automatization is currently not possible neither for a comparatively simple analysis of a chest X-ray nor for applied neuroimaging. In a clinical context, error estimations still need to be supported by the fMRI expert and cannot be done by an algorithm alone. As a consequence, the international community started early with offering dedicated clinical methodological courses (compare http://oegfmrt.org or http://ohbmbrainmappingblog.com/blog/archives/12-2016). Meanwhile, there are enough methodological studies that enable an experienced clinical fMRI expert to safely judge the possibilities and limitations for a valid functional report in a given patient with his/her specific pathologies and compliance situation. Of course, this also requires adequate consideration of the local hard- and software. Therefore and particularly when considering the various validation studies, neither for patients nor for doctors there is a need to raise “doubts about clinical fMRI studies” but instead good reason to “keep calm and scan on.”4

Author Contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The methodological developments have been supported by the Austrian Science Fund (KLI455, KLI453, P23611) and Cluster Grants of the Medical University of Vienna and the University of Vienna, Austria.

Footnotes

References

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Source: Frontiers | Can Functional Magnetic Resonance Imaging Generate Valid Clinical Neuroimaging Reports? | Neurology

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[ARTICLE] Interindividual differences in motor network connectivity and behavioral response to iTBS in stroke patients – Full Text

Highlights

Multimodal assessment of motor system integrity for predicting iTBS-aftereffects

Effective connectivity of M1 predicts behavioral iTBS-aftereffects

No association between iTBS-aftereffects and BOLD activity or RMT/AMT/SICI

Effects of brain stimulation strongly influenced by connectivity of stimulated region

Abstract

Cerebral plasticity-inducing approaches like repetitive transcranial magnetic stimulation (rTMS) are of high interest in situations where reorganization of neural networks can be observed, e.g., after stroke. However, an increasing number of studies suggest that improvements in motor performance of the stroke-affected hand following modulation of primary motor cortex (M1) excitability by rTMS shows a high interindividual variability. We here tested the hypothesis that in stroke patients the interindividual variability of behavioral response to excitatory rTMS is related to interindividual differences in network connectivity of the stimulated region. Chronic stroke patients (n = 14) and healthy controls (n = 12) were scanned with functional magnetic resonance imaging (fMRI) while performing a simple hand motor task. Dynamic causal modeling (DCM) was used to investigate effective connectivity of key motor regions. On two different days after the fMRI experiment, patients received either intermittent theta-burst stimulation (iTBS) over ipsilesional M1 or control stimulation over the parieto-occipital cortex. Motor performance and TMS parameters of cortical excitability were measured before and after iTBS. Our results revealed that patients with better motor performance of the affected hand showed stronger endogenous coupling from supplemental motor area (SMA) onto M1 before starting the iTBS intervention. Applying iTBS to ipsilesional M1 significantly increased ipsilesional M1 excitability and decreased contralesional M1 excitability as compared to control stimulation. Individual behavioral improvements following iTBS specifically correlated with neural coupling strengths in the stimulated hemisphere prior to stimulation, especially for connections targeting the stimulated M1. Combining endogenous connectivity and behavioral parameters explained 82% of the variance in hand motor performance observed after iTBS. In conclusion, the data suggest that the individual susceptibility to iTBS after stroke is influenced by interindividual differences in motor network connectivity of the lesioned hemisphere.

1. Introduction

Recovery of function after stroke is driven by reorganization of neural networks in both the lesioned and unaffected hemispheres (Cramer, 2008). However, spontaneous recovery after stroke often remains incomplete (Kolominsky-Rabas et al., 2006). One strategy to improve the functional outcome of patients suffering from brain lesions is to modulate cerebral plasticity by means of non-invasive brain stimulation such as, e.g., repetitive transcranial magnetic stimulation (rTMS) (Ridding and Rothwell, 2007). Although to date a direct proof is missing, increasing evidence exist that rTMS-effects are mediated by changes in synaptic transmission (Funke and Benali, 2011 ;  Hoogendam et al., 2010). One specific strategy to ameliorate motor impairments in stroke patients is to enhance cortical excitability of the motor cortex in the lesioned hemisphere (Khedr et al., 2005). An effective protocol of rTMS to induce such increase in excitability of the motor cortex following a relatively short (i.e., 3.5 min) stimulation period is intermittent theta-burst stimulation (iTBS) (Huang et al., 2005).

Consequently, proof-of-principle studies have been able to demonstrate that iTBS applied to ipsilesional M1 improve hand motor function in stroke patients (Ackerley et al., 2010Hsu et al., 2012 ;  Talelli et al., 2007b). A major issue, however, with rTMS (including iTBS) induced cerebral plasticity is high inter-individual variability of the effects induced in both healthy subjects (Daskalakis et al., 2006Hamada et al., 2013 ;  Muller-Dahlhaus et al., 2008) and stroke patients (Ameli et al., 2009 ;  Grefkes and Fink, 2012). For example, Hamada et al. (2013) demonstrated that application of iTBS in healthy subjects leads to an increase of motor-cortical excitability in only 52% subjects, while the other half responded in an opposite way with a decrease of excitability. Likewise, Ameli et al. (2009) reported that in patients suffering from cortical strokes, only half of them showed behavioral improvements after 10 Hz rTMS while the other half even deteriorated with their stroke affected hands. Such opposed stimulation after-effects are likely to contribute to absent overall effects across the entire group (Hamada et al., 2013).

Apart from known sources of response variability following iTBS like age (Freitas et al., 2011), genetic polymorphisms of the brain-derived neurotrophic factor (Cheeran et al., 2008 ;  Kleim et al., 2006) and technical aspects such as the direction of current flow, the intensity of stimulation and the number of pulses applied (Gamboa et al., 2010Gentner et al., 2008 ;  Talelli et al., 2007a), clinical factors like lesion location, degree of neurological impairment and time since stroke are also likely to impact on the response to rTMS (Grefkes and Fink, 2012). For example, several studies demonstrated that patients with subcortical lesions have a higher probability to improve after rTMS than patients with cortical lesions (Ameli et al., 2009 ;  Hsu et al., 2012). Moreover, the pathomechanisms underlying stroke-induced motor deficits do not only depend on direct tissue damage due to ischemia, but might also comprise network disturbances remote from the stroke lesion (Grefkes and Fink, 2011 ;  Grefkes and Fink, 2014). Thus, changes in network interactions are likely to constitute another important factor for the evolution of rTMS-aftereffects as TMS does not only interfere with neural tissue of the stimulated hemisphere but also with neural activity levels of regions that are interconnected with the stimulation site (Bestmann et al., 2005).

Hence, there is good reason to assume that specific inter-individual differences (or abnormalities post-stroke) in network connectivity might – at least in part – influence response to rTMS. Support for this hypothesis stems from studies with patients suffering from dystonia in which reduced functional connectivity between premotor cortex and M1 was indicative for responding to rTMS (Huang et al., 2010 ;  Quartarone et al., 2003). Furthermore, changes in motor-evoked potential (MEP) amplitudes following rTMS have been shown to be associated with higher effective connectivity between supplementary motor area (SMA), ventral premotor cortex (vPMC) and M1 of the stimulated hemisphere (Cardenas-Morales et al., 2014).

Therefore, in stroke patients, the variability of the individual response to plasticity-inducing intervention might depend on how the stimulation interacts with the pre-existing connectivity in a given functional network, e.g., the motor system. In order to identify factors that are associated with a positive behavioral effect in response to intermittent theta burst stimulation (here: iTBS) applied to ipsilesional M1, we used a multimodal approach consisting of clinical scales, electrophysiological parameters measured using single- and paired-pulse TMS, as well as functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) to assess effective connectivity of the cortical motor network. We reasoned that the systems level perspective offered by DCM might be useful for identifying predictors that indicate whether or not a patient will respond to non-invasive brain stimulation given that (i) focal brain stimulation also impacts on activity levels of areas connected to the stimulation site (Bestmann et al., 2003 ;  Grefkes et al., 2010) and (ii) recovery of motor function depends on changes in the entire motor network rather than changes in M1 only (Rehme et al., 2012 ;  Ward et al., 2003). Here, especially the coupling strengths between ipsilesional M1 and premotor areas might be indicative for the behavioral after-effect of iTBS given the role of these connections in motor performance in both healthy subjects and stroke (Pool et al., 2013Pool et al., 2014 ;  Rehme et al., 2011a). […]

Continue —>  Interindividual differences in motor network connectivity and behavioral response to iTBS in stroke patients

Fig. 4

Fig. 4. Neural activity when patients and controls moved the affected or unaffected hand. Fist closures were conducted at a fixed movement frequency of 0.8 Hz and at a frequency adjusted to individual performance levels. Compared to controls, patients featured enhanced activity in both hemispheres during movements of the affected hand. Movements of the unaffected hand yielded a similar activation pattern in patients and controls. T-values are represented by the color bar. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

 

 

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[Abstract] Primed Physical Therapy Enhances Recovery of Upper Limb Function in Chronic Stroke Patients.

Abstract

Background. Recovery of upper limb function is important for regaining independence after stroke.

Objective. To test the effects of priming upper limb physical therapy with intermittent theta burst stimulation (iTBS), a form of noninvasive brain stimulation.

Methods. Eighteen adults with first-ever chronic monohemispheric subcortical stroke participated in this randomized, controlled, triple-blinded trial. Intervention consisted of priming with real or sham iTBS to the ipsilesional primary motor cortex immediately before 45 minutes of upper limb physical therapy, daily for 10 days. Changes in upper limb function (Action Research Arm Test [ARAT]), upper limb impairment (Fugl-Meyer Scale), and corticomotor excitability, were assessed before, during, and immediately, 1 month and 3 months after the intervention. Functional magnetic resonance images were acquired before and at one month after the intervention.

Results. Improvements in ARAT were observed after the intervention period when therapy was primed with real iTBS, but not sham, and were maintained at 1 month. These improvements were not apparent halfway through the intervention, indicating a dose effect. Improvements in ARAT at 1 month were related to balancing of corticomotor excitability and an increase in ipsilesional premotor cortex activation during paretic hand grip.

Conclusions. Two weeks of iTBS-primed therapy improves upper limb function at the chronic stage of stroke, for at least 1 month post intervention, whereas therapy alone may not be sufficient to alter function. This indicates a potential role for iTBS as an adjuvant to therapy delivered at the chronic stage.

Source: Primed Physical Therapy Enhances Recovery of Upper Limb Function in Chronic Stroke Patients

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[Abstract] Sensorimotor modulation by botulinum toxin A in post-stroke arm spasticity: Passive hand movement – Journal of the Neurological Sciences

Highlights

  • Patients with upper limb post-stroke spasticity were treated with botulinum toxin.
  • Central effects of spasticity treatment were studied using functional MRI.
  • Brain activation pattern was assessed during passive hand movements.
  • BoNT-induced spasticity relief is associated with changes in sensorimotor network.

Abstract

Introduction

In post-stroke spasticity, functional imaging may uncover modulation in the central sensorimotor networks associated with botulinum toxin type A (BoNT) therapy. Investigations were performed to localize brain activation changes in stroke patients treated with BoNT for upper limb spasticity using functional magnetic resonance imaging (fMRI).

Methods

Seven ischemic stroke patients (4 females; mean age 58.86) with severe hand paralysis and notable spasticity were studied. Spasticity was scored according to the modified Ashworth scale (MAS). fMRI examination was performed 3 times: before (W0) and 4 (W4) and 11 weeks (W11) after BoNT. The whole-brain fMRI data were acquired during paced repetitive passive movements of the plegic hand (flexion/extension at the wrist) alternating with rest. Voxel-by-voxel statistical analysis using the General Linear Model (GLM) implemented in FSL (v6.00)/FEAT yielded group session-wise statistical maps and paired between-session contrasts, thresholded at the corrected cluster-wise significance level of p < 0.05.

Results

As expected, BoNT transiently lowered MAS scores at W4. Across all the sessions, fMRI activation of the ipsilesional sensorimotor cortex (M1, S1, and SMA) dominated. At W4, additional clusters transiently emerged bilaterally in the cerebellum, in the contralesional sensorimotor cortex, and in the contralesional occipital cortex. Paired contrasts demonstrated significant differences W4 > W0 (bilateral cerebellum and contralesional occipital cortex) and W4 > W11 (ipsilesional cerebellum and SMA). The remaining paired contrast (W0 > W11) showed activation decreases mainly in the ipsilesional sensorimotor cortex (M1, S1, and SMA).

Conclusions

The present study confirms the feasibility of using passive hand movements to map the cerebral sensorimotor networks in patients with post-stroke arm spasticity and demonstrates that BoNT-induced spasticity relief is associated with changes in task-induced central sensorimotor activation, likely mediated by an altered afferent drive from the spasticity-affected muscles.

Source: Sensorimotor modulation by botulinum toxin A in post-stroke arm spasticity: Passive hand movement – Journal of the Neurological Sciences

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[ARTICLE] Interpreting Intervention Induced Neuroplasticity with fMRI: The Case for Multimodal Imaging Strategies – Full Text PDF

Abstract

Direct measurement of recovery from brain injury is an important goal in neurorehabilitation, and requires reliable, objective, and interpretable measures of changes in brain function, referred to generally as “neuroplasticity.” One popular imaging modality for measuring neuroplasticity is task-based functional magnetic resonance imaging (t-fMRI). In the field of neurorehabilitation, however, assessing neuroplasticity using t-fMRI presents a significant challenge. This commentary reviews t-fMRI changes commonly reported in patients with cerebral palsy or acquired brain injuries, with a focus on studies of motor rehabilitation, and discusses complexities surrounding their interpretations. Specifically, we discuss the difficulties in interpreting t-fMRI changes in terms of their underlying causes, that is, differentiating whether they reflect genuine reorganisation, neurological restoration, compensation, use of preexisting redundancies, changes in strategy, or maladaptive processes. Furthermore, we discuss the impact of heterogeneous disease states and essential t-fMRI processing steps on the interpretability of activation patterns. To better understand therapy-induced neuroplastic changes, we suggest that researchers utilising t-fMRI consider concurrently acquiring information from an additional modality, to quantify, for example, haemodynamic differences or microstructural changes. We outline a variety of such supplementary measures for investigating brain reorganisation and discuss situations in which they may prove beneficial to the interpretation of t-fMRI data.

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[ARTICLE] Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive Level

Abstract

Noninvasive transcranial brain stimulation (NTBS) is widely used to elucidate the contribution of different brain regions to various cognitive functions. Here we present three modeling approaches that are informed by functional or structural brain mapping or behavior profiling and discuss how these approaches advance the scientific potential of NTBS as an interventional tool in cognitive neuroscience.

(i) Leveraging the anatomical information provided by structural imaging, the electric field distribution in the brain can be modeled and simulated. Biophysical modeling approaches generate testable predictions regarding the impact of interindividual variations in cortical anatomy on the injected electric fields or the influence of the orientation of current flow on the physiological stimulation effects.

(ii) Functional brain mapping of the spatiotemporal neural dynamics during cognitive tasks can be used to construct causal network models. These models can identify spatiotemporal changes in effective connectivity during distinct cognitive states and allow for examining how effective connectivity is shaped by NTBS.

(iii) Modeling the NTBS effects based on neuroimaging can be complemented by behavior-based cognitive models that exploit variations in task performance.

For instance, NTBS-induced changes in response speed and accuracy can be explicitly modeled in a cognitive framework accounting for the speed–accuracy trade-off. This enables to dissociate between behavioral NTBS effects that emerge in the context of rapid automatic responses or in the context of slow deliberate responses. We argue that these complementary modeling approaches facilitate the use of NTBS as a means of dissecting the causal architecture of cognitive systems of the human brain.

via Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive Level.

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[Dissertation] BRAIN CONNECTIVITY CHANGES AFTER STROKE AND REHABILITATION – Full Text PDF

ABSTRACT

Several cortical and subcortical areas of brain interact coherently during various tasks such as motor-imagery (MI) and motor-execution (ME) and even during resting-state (RS). How these interactions are affected following stroke and how the functional organization is regained from rehabilitative treatments as people begin to recover have not been systematically studied. Role of primary motor area during MI task and how this differs during ME task are still questions of interest.

To answer such questions, we recorded functional magnetic resonance imaging (fMRI) signals from 30 participants: 17 young healthy controls and 13 aged stroke survivors following stroke and following rehabilitation – either mental practice (MP) or combined session of mental practice and physical therapy (MP + PT). All the participants performed RS task whereas stroke survivors performed MI and ME tasks as well. We investigated the activity of motor network consisting of the left primary motor area (LM1), the right primary motor area (RM1), the left pre-motor cortex (LPMC), the right pre-motor cortex (RPMC) and the midline supplementary motor area (SMA).

In this dissertation, first, we report that during RS the causal information flow (i) between the regions was reduced significantly following stroke (ii) did not increase significantly after MP alone and (iii) among the regions after MP+PT increased significantly towards the causal flow values for young able-bodied people. Second, we found that there was suppressive influence of SMA on M1 during MI task where as the influence was unrestricted during ME task. We reported that following intervention the connection between PMC and M1 was stronger during MI task whereas along with connection from PMC to M1, SMA to M1 also dominated during ME task. Behavioral results showed significant improvement in sensation and motor scores and significant correlation between differences in Fugl-Meyer Assessment (FMA) scores and differences in causal flow values as well differences in endogenous connectivity measures before and after intervention.

We conclude that the spectra of causal information flow can be used as a reliable biomarker for evaluating rehabilitation in stroke survivors. These studies deepen our understanding of motor network activity during the recovery of motor behaviors in stroke. Understanding the stroke specific effective connectivity may be clinically beneficial in identifying effective treatments to maximize functional recovery in stroke survivors.

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[Dissertation] BRAIN CONNECTIVITY CHANGES AFTER STROKE AND REHABILITATION – Full Text PDF

ABSTRACT

Several cortical and subcortical areas of brain interact coherently during various tasks such as motor-imagery (MI) and motor-execution (ME) and even during resting-state (RS). How these interactions are affected following stroke and how the functional organization is regained from rehabilitative treatments as people begin to recover have not been systematically studied. Role of primary motor area during MI task and how this differs during ME task are still questions of interest.

To answer such questions, we recorded functional magnetic resonance imaging (fMRI) signals from 30 participants: 17 young healthy controls and 13 aged stroke survivors following stroke and following rehabilitation – either mental practice (MP) or combined session of mental practice and physical therapy (MP + PT). All the participants performed RS task whereas stroke survivors performed MI and ME tasks as well. We investigated the activity of motor network consisting of the left primary motor area (LM1), the right primary motor area (RM1), the left pre-motor cortex (LPMC), the right pre-motor cortex (RPMC) and the midline supplementary motor area (SMA).

In this dissertation, first, we report that during RS the causal information flow (i) between the regions was reduced significantly following stroke (ii) did not increase significantly after MP alone and (iii) among the regions after MP+PT increased significantly towards the causal flow values for young able-bodied people. Second, we found that there was suppressive influence of SMA on M1 during MI task where as the influence was unrestricted during ME task. We reported that following intervention the connection between PMC and M1 was stronger during MI task whereas along with connection from PMC to M1, SMA to M1 also dominated during ME task.

Behavioral results showed significant improvement in sensation and motor scores and significant correlation between differences in Fugl-Meyer Assessment (FMA) scores and differences in causal flow values as well differences in endogenous connectivity measures before and after intervention.

We conclude that the spectra of causal information flow can be used as a reliable biomarker for evaluating rehabilitation in stroke survivors. These studies deepen our understanding of motor network activity during the recovery of motor behaviors in stroke. Understanding the stroke specific effective connectivity may be clinically beneficial in identifying effective treatments to maximize functional recovery in stroke survivors.

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[ARTICLE] A longitudinal study of brain activation during stroke recovery using BOLD-fMRI

Stroke recovery involves a battery of plastic changes in the brain. Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) provides brain activation information with exquisite spatial resolution as a powerful tool for investigating changes in brain plasticity.

In this paper, we performed a longitudinal study examining plasticity of functional activation by BOLD-fMRI following stroke. Data were collected from 11 patients with corticospinal tract (CST) damage at three stages of recovery, i.e., acute stage (3mons) post stroke. The evolution of cortical activations for both affected and unaffected hand motion tasks were studied. Quantitative activation measurements including the effective size and sum of t values were calculated and the correlations with patient Fugl-Meyer index were analyzed across all stages. Stroke patients showed a shift from bilateral activation in acute and early stage to the ipsilesional activation in chronic stage when performing a movement task with the affected hand, which suggests a compensation effect from the contralesional hemisphere during the recovery process. The correlation analysis showed a significantly negative correlation with cingulate cortex activity at early stage from both quantitative activation measurements, implying the special role of cingulate cortex in stroke recovery. Further investigations are in need to improve the understanding of brain plasticity in stroke patients.

via IEEE Xplore Abstract – A longitudinal study of brain activation during stroke recovery using BOLD-fMRI.

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[ARTICLE] Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation – Full Text HTML

Abstract

Brain areas within the motor system interact directly or indirectly during motor-imagery and motor-execution tasks. These interactions and their functionality can change following stroke and recovery. How brain network interactions reorganize and recover their functionality during recovery and treatment following stroke are not well understood.

Full-size image (52 K)To contribute to answering these questions, we recorded blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) signals from 10 stroke survivors and evaluated dynamical causal modeling (DCM)-based effective connectivity among three motor areas: primary motor cortex (M1), pre-motor cortex (PMC) and supplementary motor area (SMA), during motor-imagery and motor-execution tasks. We compared the connectivity between affected and unaffected hemispheres before and after mental practice and combined mental practice and physical therapy as treatments. The treatment (intervention) period varied in length between 14 to 51 days but all patients received the same dose of 60 h of treatment. Using Bayesian model selection (BMS) approach in the DCM approach, we found that, after intervention, the same network dominated during motor-imagery and motor-execution tasks but modulatory parameters suggested a suppressive influence of SM A on M1 during the motor-imagery task whereas the influence of SM A on M1 was unrestricted during the motor-execution task.

We found that the intervention caused a reorganization of the network during both tasks for unaffected as well as for the affected hemisphere. Using Bayesian model averaging (BMA) approach, we found that the intervention improved the regional connectivity among the motor areas during both the tasks. The connectivity between PMC and M1 was stronger in motor-imagery tasks whereas the connectivity from PMC to M1, SM A to M1 dominated in motor-execution tasks. There was significant behavioral improvement (p = 0.001) in sensation and motor movements because of the intervention as reflected by behavioral Fugl-Meyer (FMA) measures, which were significantly correlated (p = 0.05) with a subset of connectivity.

These findings suggest that PMC and M1 play a crucial role during motor-imagery as well as during motor-execution task. In addition, M1 causes more exchange of causal information among motor areas during a motor-execution task than during a motor-imagery task due to its interaction with SM A. This study expands our understanding of motor network involved during two different tasks, which are commonly used during rehabilitation following stroke. A clear understanding of the effective connectivity networks leads to a better treatment in helping stroke survivors regain motor ability.

Abbreviations

  • DCM, dynamical causal modeling
  • BMS, Bayesian model selection
  • BMA, Bayesian model averaging; IU, imagine unaffected
  • IA, imagine affected
  • PU, pinch unaffected
  • PA, pinch affected
  • MI, motor imagery
  • ME, motor-execution.

 

Continue —> Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation.

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