Posts Tagged neuroimaging

[WEB SITE] The Whole Brain Atlas

  • Keith A. Johnson, M.D. (keith@bwh.harvard.edu)
  • J.Alex Becker (jabecker@mit.edu)

Neuroimaging of specific neurological conditions.

The Whole Brain Atlas 

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[WEB SITE] Epileptic Disorders – How to diagnose and treat post-stroke seizures and epilepsy – Educational

Résumé

Stroke is one of the commonest causes of seizures and epilepsy, mainly among the elderly and adults. This seminar paper aims to provide an updated overview of post-stroke seizures and post-stroke epilepsy (PSE) and offers clinical guidance to anyone involved in the treatment of patients with seizures and stroke. The distinction between acute symptomatic seizures occurring within seven days from stroke (early seizures) and unprovoked seizures occurring afterwards (late seizures) is crucial regarding their different risks of recurrence. A single late post-stroke seizure carries a risk of recurrence as high as 71.5% (95% confidence interval: 59.7-81.9) at ten years and is diagnostic of PSE. Several clinical and stroke characteristics are associated with increased risk of post-stroke seizures and PSE. So far, there is no evidence supporting the administration of antiepileptic drugs as primary prevention, and evidence regarding their use in PSE is scarce.

 

Neurologists frequently encounter seizures related to stroke. Given that post-stroke epilepsy (PSE) is the most common form of acquired epilepsy, it is quite surprising that it has attracted little academic interest and that, so far, only scarce evidence is available to guide clinical practice. The scenario is, however, changing and both basic science researchers and clinical investigators have started to address highly relevant issues, including pathophysiology, prevalence and incidence, diagnosis, prevention, treatment, and prognosis.

When should PSE be diagnosed? When should treatment start? Which is the most effective treatment? Which antiepileptic drug (AED) should be preferred? Not all seizures occurring after stroke are necessarily stroke-related. So, when should PSE be diagnosed? Only an adequate knowledge and correct diagnosis may spare patients the anxiety, stigma, and side effects of unnecessary treatments.

In recent years, interesting developments in the field of epileptogenesis also suggests that the risk of PSE may be modified through pharmacological intervention. In addition, the association found between PSE and risk of vascular events highlights the importance of secondary stroke prophylaxis.

This seminar paper aims to provide an updated overview of PSE and offers clinical guidance to professionals involved in the treatment of patients with post-stroke seizures.

EARLY AND LATE POST-STROKE SEIZURES

DEFINITION

A distinction between early and late post-stroke seizures is mandatory in the field of seizures and stroke since it underscores different pathophysiological mechanisms. Early post-stroke seizures reflect an acute, and perhaps reversible cerebral injury (i.e. acute symptomatic, provoked) whereas late seizures arise from long-lasting changes in the post-stroke brain (i.e. remote symptomatic, unprovoked). In this article, we will use the terms “early and late post-stroke seizures” throughout. The distinction between early and late seizures is closely linked to the theoretical concept of epileptogenesis -the hereto incompletely characterized process by which the brain acquires an enduring predisposition to seizures. Epileptogenesis does not simply represent a process that starts at stroke onset and manifests with seizures at a later stage, but should be considered within the frame of a threshold model in which individual predisposition, stroke characteristics, and subsequent reactions to the primary injury converge with PSE.

The temporal limit to consider a seizure as a “late-seizure” ranges mostly between one and two weeks after stroke, in analogy with the concept of early and late post-traumatic seizures. A pivotal study in Rochester, Minnesota demonstrated that a seizure within seven days of a stroke carries a ten-year risk of a subsequent unprovoked seizure of 33% (95% confidence interval [CI]: 20.7-49.9), whereas a seizure occurring seven days after a stroke carries a ten-year risk of 71.5% (95% CI: 59.7-81.9) (Hesdorffer et al., 2009). Based on such differences in patient prognosis, seven days is currently the recommended cut-off for considering a post-stroke seizure as early or late (Beghi et al., 2010). According to the most recent diagnostic criteria, epilepsy can be diagnosed after a single seizure and with a recurrence risk >60% within the next ten years (Fisher et al., 2014). As shown by the Rochester study, patients presenting with a single late seizure after a stroke carry such a risk. Therefore, one late unprovoked post-stroke seizure can be diagnosed as PSE.

However, it is important to recognize the pitfalls of the seven-day cut-off in order to distinguish between early and late seizures (see below under: “When and how to clinically diagnose post-stroke epilepsy”). The risk of PSE is substantially higher in patients who have presented with an early post-stroke seizure than in patients who have not had any seizure. The occurrence of early seizures is, indeed, an independent risk factor for PSE and weighs heavily in the SeLECT score. The SeLECT score is a recently developed and validated clinical tool to predict late seizures/epilepsy after ischaemic stroke. In addition to the occurrence of an early seizure, it takes into account the severity of stroke, aetiology of stroke, and cortical and arterial territory involved (Ferlazzo et al., 2016; Galovic et al., 2018). An early seizure, not considered epilepsy, should therefore not convey the message that the patient is at no risk of PSE. Clinical risk models and biomarkers must be incorporated in the future to help identify the mechanisms of PSE and refine the diagnosis of PSE in some patients with early seizures and reassure those at low risk of recurrence.

PATHOPHYSIOLOGY

Much of the pathophysiology underlying seizures after stroke remains elusive. Experimental stroke studies have mapped a number of reactions following brain injury, which are common to other models of acquired epilepsy and include inflammatory response, changes in the expression of proteins involved in neuronal signalling, and remodelling of cytoskeleton, but causal links have not been clearly established. Increased blood-brain barrier permeability could also play a pathogenic role (Pitkänen et al., 2016).

Early post-stroke seizures should be regarded as a reaction of the neuronal cells to the acute cerebrovascular injury. They reflect transient cellular biochemical dysfunctions, including -among others- the increased release of excitatory neurotransmitter glutamate, ionic imbalance, breakdown of membrane phospholipids, and release of free fatty acids with oxidative stress (Tanaka and Ihara, 2017). Homeostatic or systemic disturbances, such as electrolyte imbalance, acid-base disturbances and hyperglycaemia, may also play a role in the development of early post-stroke seizures (Tanaka and Ihara, 2017). Conversely, late post-stroke seizures reflect a structural change of neuronal networks following the cerebrovascular injury to the brain (Trinka and Brigo, 2014). They are usually attributed to epileptogenic gliotic scarring with changes in membrane properties, neuronal deafferentation, selective neuronal loss or collateral sprouting (Tanaka and Ihara, 2017). Late post-stroke seizures after a primary cerebral haemorrhage or secondary haemorrhagic transformation of an ischemic stroke are thought to be the consequence of haemosiderin deposits leading to increased neuronal excitability. During post-stroke epileptogenesis, the brain undergoes molecular and cellular alterations, which increase its excitability and eventually lead to the occurrence of recurrent spontaneous seizures. These progressive neuronal changes include selective neuronal cell death and apoptosis, changes in membrane properties, mitochondrial and receptor changes (e.g. loss of GABAergic receptors), deafferentation, and collateral sprouting (Pitkänen et al., 2016). Disruption of the brain-blood barrier following endothelial damage causes extravasation of albumin which in turns activates astrocytes and microglial cells; this leads to changes in the extracellular milieu with increased glutamate levels, release of inflammatory cytokines, and further increase in brain-blood barrier permeability (Tanaka and Ihara, 2017). Thrombin, a major component of the coagulation cascade, and its protease-activated receptor 1 (PAR1), may further contribute to maladaptive plasticity leading to permanent structural changes in the brain with altered neuronal firing and circuit dysfunctions (Altman et al., 2019). This complex cascade of events directly enhances neuronal excitability and could explain epileptogenesis after a stroke. Alterations in gene expression after a stroke can also play a role in epileptogenesis, as they can be associated with impaired neuroprotection, aberrant synaptic plasticity, upregulation of neuronal excitability, and enhanced gliotic scarring formation (Pitkänen et al., 2016). Of note, these pathophysiological mechanisms interact with each other and eventually lead to structural and functional alterations of neuronal networks, leading to recurrent spontaneous seizures (Tanaka and Ihara, 2017).

Remarkably, the current pathophysiological perspective of acquired epilepsy favours a threshold model, which also involves individual predisposition. For instance, individuals with a first-degree relative suffering with epilepsy are at higher risk of developing PSE (hazard ratio: 1.18; 95% CI: 1.09-1.28) although this was associated with a small effect size (Eriksson et al., 2019). Lesion characteristics may be more important in most cases such as size of the lesion, cortical involvement and presence of intralesional blood products (see below under “Neuroimaging of post-stroke seizures: pitfalls and differential diagnosis”).

Until now, the early treatment of stroke patients with AEDs during the acute phase has not been effective in reducing the risk of developing PSE (Gilad et al., 2011; Sheth et al., 2015). On the other hand, statins appear to be the only medication to decrease the risk of PSE (Etminan et al., 2010), and to a greater extent in patients who present with early seizures and are considered a high-risk group (Guo et al., 2015). However, causality and mechanisms of the effect of statins are not yet well-established.

EPIDEMIOLOGY AND RISK FACTORS

The rates of early post-stroke seizures and PSE vary across stroke populations. For ischaemic stroke, the prevalence of early seizures is generally 3-6% (Beghi et al., 2011; Labovitz et al., 2001; Guo et al., 2015; Serafini et al., 2015) but can be up to 15% in selected cohorts (Labovitz et al., 2001; Lamy et al., 2003; Bentes et al., 2017). There is no converging evidence about the risk of early seizures in patients treated with reperfusion therapies, either intravenous thrombolysis or endovascular thrombectomy (Belcastro et al., 2020; Brigo et al., 2020a; Feher et al., 2019). The risk of intracerebral haemorrhage is somewhat higher (Qian et al., 2014), with early seizures occurring in approximately 10-16% of patients (Naess et al., 2004; Beghi et al., 2011; Procaccianti et al., 2012). However, the methodology adopted to ascertain and diagnose early post-stroke seizures can greatly affect the results. For instance, a study using video-EEG recording performed in the first 72 hours following an acute anterior circulation ischaemic stroke revealed early seizures in 14.6% and non-convulsive status epilepticus (SE) in 2.6% of patients; of note, almost a quarter (22.7%) of early seizures were exclusively electrographic (Bentes et al., 2017).

Data on PSE prevalence also depend on the study population and methodology used to collect data. Based on nationwide registers in Sweden, the cumulative incidence of PSE was 6.4% following ischaemic stroke and 12.4% following haemorrhagic stroke after a follow-up of almost five years (Zelano et al., 2016); the latter finding has been replicated in a population-based investigation in a Finnish region (Lahti et al., 2017). In a video-EEG study, 15.2% of patients suffering with an anterior ischemic stroke met the diagnostic criteria for epilepsy at 12 months (Bentes et al., 2017).

A diagnosis of PSE (after ischaemic and haemorrhagic stroke) increases the risk of mortality after adjusting for stroke severity (Zelano et al., 2016) and, unsurprisingly, vascular disease is the major cause of death. These findings call for concerted efforts to prioritise and optimize secondary vascular prophylaxis (Hansen et al., 2017), and AEDs that do not interfere with concomitant medications, such as anti-hypertensives and anticoagulants, should be preferentially chosen.

The main risk factors for early post-ischaemic stroke seizures are cortical involvement, severe stroke, haemorrhagic transformation, age younger than 65 years, a large lesion and atrial fibrillation (Feher et al., 2019). The main risk factors for PSE following ischaemic stroke are cortical involvement, haemorrhage, and early seizures (Ferlazzo et al., 2016).

WHEN AND HOW TO CLINICALLY DIAGNOSE POST-STROKE EPILEPSY

The concept of early and late seizures and PSE is straightforward to apply in clinical practice in most cases. If a patient has a seizure within a week of stroke, it is an early seizure and considered acute symptomatic. Although such a seizure carries a risk of subsequent epilepsy, this risk does not warrant the diagnosis of PSE. In contrast, a seizure occurring more than one week after stroke is considered an unprovoked late seizure. This infers a >60% risk of seizure recurrence and the patient meets the diagnostic criteria for epilepsy.

In some circumstances, the distinction between early and late seizures may not be unequivocal. The clinical situation may have been unstable, and the exact time of the latest cerebral insult may not be clear. As per the definition of epilepsy recommended by the International League Against Epilepsy, the diagnosis requires a risk of seizure recurrence exceeding 60%, however, the exact risk in each case is hard to estimate with precision. If there is doubt whether a seizure has occurred within the acute symptomatic phase, then there is no clear evidence of a >60% recurrence risk. In this scenario, the diagnosis of PSE should not be made. A similar approach can be suggested if there is doubt whether a paroxysmal post-stroke event is actually a seizure. In the presence of uncertainty, it is probably better not to diagnose a late seizure/PSE, but rather adopt a wait-and-watch approach. It is important to emphasize, however, that whether a patient is or is not diagnosed with PSE, the decision to initiate treatment with AEDs will depend on clinical characteristics of individual patients.

THE ROLE OF THE ELECTROENCEPHALOGRAM IN THE DIAGNOSIS AND PREDICTION OF POST-STROKE SEIZURES

In the early phase following an ischaemic or haemorrhagic stroke, electroencephalogram (EEG) is an essential diagnostic tool that aims to detect purely electrographic seizures. It can also detect specific patterns, such as lateralized periodic discharges (LPDs), that are independently associated with early seizures (Mecarelli et al., 2011).

Interestingly, brain single-photon emission computed tomography (SPECT) imaging can reveal focal hypermetabolism with increased cerebral blood flow in association with LPDs in patients with post-stroke seizures; such findings support the view that – at least in some patients – this EEG pattern may correspond to an ictal phenomenon (Ergün et al., 2006; Hughes, 2010).

The lack of a systematic electrophysiological assessment with video-EEG can lead to an underestimation of seizures, particularly in the case of focal unaware or non-convulsive seizures (Belcastro et al., 2014; Bentes et al., 2017; Brigo et al., 2020a, 2020b). Neurologists and health personnel working in stroke units should promptly request an EEG recording for patients with sudden onset of unexplained behavioural changes or impairment of consciousness. A continuous EEG lasting ≥24 hours should be recorded as soon as possible in patients with acute supratentorial brain injury presenting with altered mental status or with clinical paroxysmal events suspected to be seizures. In addition, in comatose patients, patients with periodic discharges, or patients who are pharmacologically sedated, a more prolonged EEG (≥48 hours) may lead to the detection of non-convulsive seizures (Herman et al., 2015). The main indications for continuous EEG in patients with acute stroke, to identify non-convulsive seizures and non-convulsive status epilepticus, are presented in table 1.

EEG recordings may also have implications in the prediction of functional outcome, mortality and post-stroke cognitive decline, with different levels of evidence (Doerrfuss et al., 2020).

Only few studies have, so far, assessed EEG as a predictive tool for post-stroke seizures and epilepsy. Abnormalities on EEG can predict the development of epilepsy in the first year after stroke, independently of clinical and imaging-based infarct severity. A retrospective study of 110 patients with ischaemic stroke-related seizures found LPDs in 5.8% of patients, whereas the 275 stroke patients who did not suffer an early and/or a late seizure did not present with LPDs (De Reuck et al., 2006). Diffuse EEG slowing and frontal intermittent rhythmic delta activities also occurred more frequently among patients with post-stroke seizures compared to controls (21.7% versus 5.1% and 24.6% versus 1.1%, respectively) (De Reuck et al., 2006). A prospective video-EEG study enrolled 151 patients with anterior circulation ischaemic stroke and no previous seizures. Asymmetric background activity and interictal epileptiform activity detected on EEG performed during the first 72 hours after stroke were independent predictors of PSE during the first year following the index event (Bentes et al., 2018).

These findings suggest how EEG recorded in the acute stroke phase may not only detect subclinical seizures, but also may provide useful information to predict the development of PSE. Further studies are warranted to assess whether the inclusion of EEG findings with existing scores (e.g. SeLECT [Galovic et al., 2018]) could improve their predictive accuracy (Doerrfuss et al., 2020).

Most studies available in the literature refer to the use of EEG in the acute phase as a predictor of unprovoked late post-stroke seizures. In patients with PSE, EEG usually shows multifocal or focal slowing, typically with a normal background alpha rhythm (Mecarelli and Vicenzini, 2019). Epileptiform abnormalities can be detected, usually as sharp waves, sometimes with a quasiperiodic pattern of recurrence, particularly in PSE associated with large cortical cerebrovascular lesions (Brigo and Mecarelli, 2019; Mecarelli and Vicenzini, 2019).

NEUROIMAGING OF POST-STROKE SEIZURES: PITFALLS AND DIFFERENTIAL DIAGNOSIS

Seizures are an expression of sudden depolarization of neurons that transiently disrupts ionic and metabolic homeostasis. There are different proposed pathophysiological mechanisms for early and late seizures, which include critically reduced local blood flow, abnormal release of neurotransmitters, metabolic dysfunction, presence of gliotic scarring and aberrant synaptic connectivity (Pitkänen et al., 2016). Of note, only a minority of stroke patients will develop seizures and there is still scarce understanding of magnetic resonance imaging (MRI) signatures that can identify the patients at higher risk. Some studies have identified the following MRI predictors: watershed infarctions, middle cerebral arterial territory strokes, cortical involvement, haemorrhagic strokes and haemorrhagic transformation of ischaemic stroke (Ferlazzo et al., 2016; Galovic et al., 2018).

Caution is, however, necessary in considering a post-stroke seizure as stroke related. In such cases, neuroimaging is fundamental in providing a differential diagnosis.

In acute settings, cranial computed tomography (CCT) is the gold standard to rapidly image patients presenting with seizures. It also represents the only available neuroimaging tool in patients who cannot undergo MRI. Besides standard CCT, perfusion CT (PCT) can be helpful in differentiating between stroke, stroke mimics and status epilepticus (Strambo et al., 2018). Ongoing seizure activity or SE are characterized by regions of hyperperfusion that usually involve atypical vascular territories, whereas strokes typically correspond to hypoperfused areas in a precise arterial territory (Payabvash et al., 2015). PCT can also be helpful to differentiate postictal versus stroke related focal neurological deficits: the former are characterized by transient iso- to hyperperfusion and the latter by areas of hypoperfusion in a vascular territory (Brigo and Lattanzi, 2020). Notably, PCT must be performed within a strict interval from seizure onset (< three hours) to improve its sensitivity (Payabvash et al., 2015).

MRI remains the most sensitive non-invasive diagnostic tool to image the brain, and conventional MR sequences suffice in most cases of suspected post-stroke seizures. The most common conventional sequences are listed in table 2. Diffusion-weighted imaging (DWI) is an informative sequence and is now routinely used in clinical settings. It is fast (acquisition requires less than a minute) and demonstrates high sensitivity for areas of water restriction, making it a very commonly used sequence to differentiate stroke from stroke mimics. Nonetheless, timing of acquisition is extremely important to avoid pitfalls. Lesions can be falsely positive (those containing a very high water content, also known as “T2 shine-through” artefact) or falsely negatively (MRI scans acquired too late after symptom onset) (Agarwal et al., 2017; Shono et al., 2017). Restricted signal on DWI due to seizure activity is mostly reversible (figure 1), whereas it may last three to four days in stroke, unless cerebral tissue has been reperfused earlier. Other lesions that may present with DWI positive signal include subacute haematomas, hypercellular tumours and abscesses (figure 2). Recently, Koksel et al. proposed the acronym “CRUMPLED” as a helpful way to remember the most important DWI-restricted lesions that are cortically based and have an atypical vascular distribution. The acronym stands for C = Creutzfeldt-Jakob disease; R = reversible cerebral vasoconstriction syndrome; U = urea cycle disorders and uraemia; M = mitochondrial disorders; P = prolonged seizures and posterior reversible encephalopathy syndrome (PRES); L = laminar necrosis (hypoxic-ischaemic encephalopathy) and liver-related (acute hepatic or hyperammonaemic encephalopathy); E=encephalitis (infectious meningoencephalitis) and D = diabetes mellitus (hypoglycaemia) (Koksel et al., 2018) (figure 2).

Intravenous gadolinium-based imaging can be particularly useful to: a) identify areas of disrupted blood-brain barrier; b) provide evidence of reperfusion or presence of collateral flow; and c) identify stroke mimics.

Advanced imaging can provide additional and more accurate information for the differential diagnosis.

Perfusion weighted imaging (PWI) can reliably identify tissue at risk of infarct, defined as an area with a blood flow of less than 50 mL per 100 mL of brain tissue per minute (Jahng et al., 2014). Signal changes in PWI are related to electrographic ictal activity: hyperperfusion is likely to be seen in pre-ictal and ictal phases, whereas hypoperfusion is more common in the post-ictal phase (Takahara et al., 2018). Early seizures are more likely to present as areas of hyperperfusion due to the underlying pathophysiological mechanisms, including metabolic dysfunction and abnormal release of neurotransmitters, whereas late seizures are likely to show a mixed pattern of perfusion as they are more related to gliotic changes and loss of neuronal tissue (Yoo et al., 2017). Hyperperfusion may also precede DWI signal changes as a compensatory mechanism to support the abnormally increased depolarization of neurons (Takahara et al., 2018). Susceptibility weighted imaging (SWI) and gradient-echo (GRE) sequences can be highly informative and detect punctuate microbleeds and areas of iron-laden products in patients with subarachnoid haemorrhage, chronic subdural haematoma, cerebral amyloid angiopathy or superficial siderosis. Extracellular haemosiderin is considered epileptogenic and may cause focal cerebral irritation and initiate seizures, even though the mechanisms are not yet well-established (O’Connor et al., 2014).

MANAGEMENT OF ACUTE-SYMPTOMATIC POST-STROKE SEIZURES

Due to the rather low risk of early, acute-symptomatic post-stroke seizures, ranging from 3-6% in cases of cerebral ischaemia to 16% in primary cerebral haemorrhage (Labovitz et al., 2001; Naess et al., 2004; Beghi et al., 2011; Procaccianti et al., 2012; Guo et al., 2015; Serafini et al., 2015), primary prophylaxis with an AED is not recommended. This is also true for those patients who have cerebral haemorrhage involving cortical structures and a risk of early post-stroke seizures of around 35%. If physicians decide to introduce primary AED prophylaxis despite the evidence-based recommendations, an AED that can be titrated very quickly, administered intravenously, and which lacks significant drug-drug interactions should be preferred. One of the most commonly prescribed AEDs that meets these characteristics is levetiracetam (LEV). One randomized controlled trial compared valproate to placebo in 36 patients, both of which were administered directly after intracerebral haemorrhage (Gilad et al., 2011). The groups did not differ with respect to prevention of early post-stroke seizures (defined in that study as occurring within the first 14 days), but the trial was underpowered, and prevention of early seizures was not the primary endpoint.

After the occurrence of one early post-stroke seizure, the risk of developing a second acute symptomatic seizure within the acute phase is only 10-20% (De Herdt et al., 2011; Leung et al., 2017). Due to the low risk of recurrence, guidelines generally do not recommend secondary AED prophylaxis after an early post-stroke seizure (Holtkamp et al., 2017). However, many clinicians prefer to administer an AED to reduce the likelihood of clinical worsening in the acute setting. Conceptually, this approach likely relies on pathophysiological considerations including increased neuronal excitotoxicity, peri-infarct depolarisations, and inflammatory responses in the first hours and days after stroke (Dirnagl et al., 1999), all of which can be risk factors for acute recurrence of epileptic seizures. The criteria used to choose the AED for acute secondary prophylaxis are similar to those for primary prophylaxis.

If patients without or after an early post-stroke seizure have been administered an AED, physicians are encouraged to withdraw it after the acute phase – at best, at discharge from the stroke unit – as the vast majority of these patients will not experience any future seizures (Holtkamp et al., 2017). The risk of a first unprovoked post-stroke seizure within eight years (which would define epilepsy) after cerebral infarct is 8% and 15% after cerebral haemorrhage (Merkler et al., 2018), and the risk of an unprovoked seizure after one early post-stroke seizure with 10 years is 30 to 35% (Hesdorffer et al., 2009; Galovic et al., 2018). Two studies developed scores to estimate the long-term risk of unprovoked seizures after acute cerebrovascular events. The CAVE score indicates a five-year seizure risk of 46.2% in patients after intracerebral haemorrhage based on the following four variables: early post-stroke seizure(s), cortical involvement, bleeding volume of more than 10 mL, and age of less than 65 years (Haapaniemi et al., 2014). The SeLECT score indicates a five-year seizure risk of more than 50% in patients after ischaemic stroke based on the following four or five criteria: early post-stroke seizure(s), severe stroke (NIHSS ≥11), cortical involvement, and large-artery atherosclerosis and/or involvement of the middle cerebral artery territory (Galovic et al., 2018). In these individual risk constellations, long-term secondary AED prophylaxis may be indicated.

MANAGEMENT OF POST-STROKE EPILEPSY

AED treatment is advised based on guidelines when PSE is diagnosed (Holtkamp et al., 2017). As always, there may be individual reasons not to start treatment -for instance, in cases with very mild semiology. Regarding the selection of drugs, two underpowered randomized, open-label studies compared controlled-release carbamazepine (CBZ-CR) to lamotrigine (LTG) (Gilad et al., 2007) and LEV (Consoli et al., 2012). The 12-month seizure freedom rates were 44% and 85% for CBZ-CR and 72% and 94% for LTG and LEV, without significant differences. LTG and LEV were better tolerated than CBZ-CR. A network meta-analysis of these trials showed no difference between LEV and LTG for seizure freedom (OR: 0.86; 95% CI: 0.15-4.89), but demonstrated greater occurrence of adverse events for LEV than LTG (OR: 6.87; 95% CI: 1.15-41.1) (Brigo et al., 2018). A randomized double-blinded trial on AEDs in epileptic patients, aged 60 years and older (two thirds had cerebrovascular aetiology), demonstrated higher one-year retention rates for LEV (62%) compared to CBZ-CR (46%; p=0.02), while LTG (56%) was intermediate (Werhahn et al., 2015). The SANAD trial, a non-blinded randomized controlled study comparing five standard and new AEDs in focal epilepsy, found LTG to have the best retention rate as compared to carbamazepine (CBZ), gabapentin, oxcarbazepine, and topiramate (Marson et al., 2007). Although data were not stratified according to the underlying aetiology, the findings can likely be extrapolated to PSE. The non-blinded, randomized, 52-week KOMET study compared the effectiveness of LEV as monotherapy to extended-release sodium valproate (VPA-ER) or CBZ-CR after the physician had decided which of the two AEDs best suited the individual patient (Trinka et al., 2013). In a post-hoc subgroup analysis of patients aged ≥60 years with newly diagnosed epilepsy (most of which were likely to have cerebrovascular aetiology), the 12-month retention rates in the VPA-ER stratum were 90% in the LEV group and 77% in the VPA-ER group; the corresponding rates in the CBZ-CR stratum were 75% and 53% in the LEV and CBZ-CR treatment arms, respectively (Pohlmann-Eden et al., 2016). In summary, the findings from clinical studies argue in favour of the newer AEDs for PSE due to their better tolerability profiles.

In focal epilepsy, the underlying aetiology does not usually determine the choice of AED. The decision regarding the most suitable compound has to be individualized according to the patient’s age, sex, comorbidities and comedications. Patients with PSE likely carry some burden of cardiovascular risk factors. Accordingly, AEDs such as CBZ, phenytoin, phenobarbital and primidone, which can increase biochemical markers of vascular disease, including total cholesterol, lipoprotein, C-reactive protein and homocysteine (Mintzer et al., 2009), should be avoided. Being strong enzyme-inducers, these AEDs may also increase the metabolism, and thus decrease serum concentrations, of drugs that are concomitantly administered for stroke management, such as warfarin. Post-stroke depression is common, and the detrimental effects of LEV on behaviour (Josephson et al., 2019) may further fuel psychiatric comorbidity, rendering this AED less appropriate in patients with post-stroke depression.

The question to withdrawal the antiepileptic treatment at some time point after the onset of PSE is difficult to address. The overall risk of seizure recurrence within five years after AED tapering is roughly 50%. A meta-analysis on seizure recurrence rate after AED withdrawal, based on 10 retrospective, prospective and randomized-controlled trials involving more than 1,700 patients, allowed the development of a prediction tool for seizure relapse (Lamberink et al., 2017). This tool can be accessed online (Epilepsy Prediction and Tool., 2019) and can assist physicians, but the decision to withdraw the treatment needs to be tailored to each patient individually.

FUTURE PERSPECTIVES

Several issues of PSE remain open to further research and investigation. Studies are warranted to elucidate the mechanisms of epileptogenesis after stroke and identify reliable biomarkers associated with the development of PSE. The role of EEG in predicting the occurrence of post-stroke seizures and epilepsy requires additional evaluation. The duration of EEG recording should be further evaluated in order to establish whether prolonged video-EEG monitoring during the first 72 hours after stroke is cost-effective and can offer advantages over routine, short-lasting EEG to identify post-stroke seizures (Grillo, 2015). The association of systemic thrombolysis and mechanical revascularization procedures with the development of early and late post-stroke seizures is still a matter of debate (Bentes et al., 2020). Similarly, there remain uncertainties about the most efficacious and safe AED to manage PSE.

Long-term, prospective, multicentric, high-quality studies with large cohorts of patients and stroke registries are needed to elaborate a practice guideline on diagnosis and treatment of PSE.

SUPPLEMENTARY DATA

Summary didactic slides are available on the www.epilepticdisorders.com website.

Illustrations

Tableaux

DISCLOSURES

Dr. Brigo received travel support from Eisai; acted as consultant for Eisai, LivaNova, and UCB Pharma; and was one of the organizers of the “Seizures & Stroke” Congress, held in Gothenburg from 20th to 22nd February 2019.

Dr Zelano has received consultancy fees from the Swedish Medial Product agency; speaker honoraria from UCB, was one of the organizers of the “Seizures & Stroke” Congress, held in Gothenburg from 20th to 22nd February 2019; and as an employee of Sahlgrenska university hospital (no personal compensation) is, and has been, an investigator in clinical trials sponsored by GW Pharma, SK life science, UCB, and Bial.

Dr. Holtkamp received speaker’s honoraria and/or consultancy fees from Bial, Desitin, Eisai, GW Pharmaceuticals, LivaNova, Novartis, and UCB (within the last three years).

Dr. Trinka received speaker honoraria from Eisai, UCB Pharma, LivaNova, Sandoz, Novartis, Biogen, Everpharma, BIAL-Portela &C, Newbridge, GL Pharma, Boehringer; grants from Biogen, UCB Pharma, Bayer, Novarti, Eisai, Merck, and Red Bull; grants from the European Union, FWF Österreichischer Fond zur Wissenschaftsforderung, Bundesministerium für Wissenschaft und Forschung, and Jubiläumsfond der Österreichischen Nationalbank outside the submitted work; and is a member of the following ILAE Task forces: Medical Therapies, Nosology, Terminology, Congresses, Driving, Regulatory affairs, and Telemedicine.

Dr. Agarwal and Dr. Lattanzi have no conflicts of interest to disclose.

via John Libbey Eurotext – Epileptic Disorders – How to diagnose and treat post-stroke seizures and epilepsy

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[Abstract] Withdrawal seizures: possible risk factors

Abstract

Introduction

Most of the patients usually achieve seizure freedom under treatment with antiseizure medications (ASMs). Drug withdrawal in seizure-free patients is still one of the most challenging issues in the management of epilepsy. The decision-making process of whether the treatment should be discontinued must be based on the evaluation of possible long-term side effects of chronic treatment and, on the other hand, the risk of seizure relapse.

Areas Covered

This review aims to describe and discuss possible predictors and risk factors for seizure relapse during and after discontinuation, according to the available literature evidence.

The most important risk factors for withdrawal failure are the etiology of the epilepsy syndrome and epilepsy-related factors, worsening or persistence of epileptiform abnormalities on EEG recordings at the time of discontinuation or during drug tapering, and brain MRI abnormalities.

Each single risk factor should be considered together with possible other concurrent predictors.

Expert Opinion

The decision to withdrawal antiseizure medication in seizure-free patients should be carefully planned and based on the evaluation of predictors. A discontinuation program should include tailored discussion with patients and family members and individualized decision, the taper schedule, and plans for monitoring during and after drug tapering.

Links: Antiseizure medicationsantiepileptic drugswithdrawaldiscontinuationseizure relapseepilepsy syndromeetiologyelectroencephalogramneuroimaging

Source: https://www.tandfonline.com/doi/abs/10.1080/14737175.2020.1780917?journalCode=iern20

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[Abstract] How brain imaging provides predictive biomarkers for therapeutic success in the context of virtual reality cognitive training

Highlight

VR environments help improve rehabilitation of impaired complex cognitive functions

Combining neuroimaging and VR boosts ecological validity, generates practical gains

These are the first neurofunctional predictive biomarkers of VR cognitive training

Abstract

As Virtual reality (VR) is increasingly used in neurological disorders such as stroke, traumatic brain injury, or attention deficit disorder, the question of how it impacts the brain’s neuronal activity and function becomes essential. VR can be combined with neuroimaging to offer invaluable insight into how the targeted brain areas respond to stimulation during neurorehabilitation training. That, in turn, could eventually serve as a predictive marker for therapeutic success. Functional magnetic resonance imaging (fMRI) identified neuronal activity related to blood flow to reveal with a high spatial resolution how activation patterns change, and restructuring occurs after VR training. Portable and quiet, electroencephalography (EEG) conveniently allows the clinician to track spontaneous electrical brain activity in high temporal resolution. Then, functional near-infrared spectroscopy (fNIRS) combines the spatial precision level of fMRIs with the portability and high temporal resolution of EEG to constitute an ideal measuring tool in virtual environments (VEs). This narrative review explores the role of VR and concurrent neuroimaging in cognitive rehabilitation.

Source: https://www.sciencedirect.com/science/article/abs/pii/S0149763420304218?dgcid=rss_sd_all&utm_campaign=RESR_MRKT_Researcher_inbound&utm_medium=referral&utm_source=researcher_app

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[ARTICLE] Increased gait variability during robot-assisted walking is accompanied by increased sensorimotor brain activity in healthy people – Full Text

Abstract

Background

Gait disorders are major symptoms of neurological diseases affecting the quality of life. Interventions that restore walking and allow patients to maintain safe and independent mobility are essential. Robot-assisted gait training (RAGT) proved to be a promising treatment for restoring and improving the ability to walk. Due to heterogenuous study designs and fragmentary knowlegde about the neural correlates associated with RAGT and the relation to motor recovery, guidelines for an individually optimized therapy can hardly be derived. To optimize robotic rehabilitation, it is crucial to understand how robotic assistance affect locomotor control and its underlying brain activity. Thus, this study aimed to investigate the effects of robotic assistance (RA) during treadmill walking (TW) on cortical activity and the relationship between RA-related changes of cortical activity and biomechanical gait characteristics.

Methods

Twelve healthy, right-handed volunteers (9 females; M = 25 ± 4 years) performed unassisted walking (UAW) and robot-assisted walking (RAW) trials on a treadmill, at 2.8 km/h, in a randomized, within-subject design. Ground reaction forces (GRFs) provided information regarding the individual gait patterns, while brain activity was examined by measuring cerebral hemodynamic changes in brain regions associated with the cortical locomotor network, including the sensorimotor cortex (SMC), premotor cortex (PMC) and supplementary motor area (SMA), using functional near-infrared spectroscopy (fNIRS).

Results

A statistically significant increase in brain activity was observed in the SMC compared with the PMC and SMA (p < 0.05), and a classical double bump in the vertical GRF was observed during both UAW and RAW throughout the stance phase. However, intraindividual gait variability increased significantly with RA and was correlated with increased brain activity in the SMC (p = 0.05; r = 0.57).

Conclusions

On the one hand, robotic guidance could generate sensory feedback that promotes active participation, leading to increased gait variability and somatosensory brain activity. On the other hand, changes in brain activity and biomechanical gait characteristics may also be due to the sensory feedback of the robot, which disrupts the cortical network of automated walking in healthy individuals. More comprehensive neurophysiological studies both in laboratory and in clinical settings are necessary to investigate the entire brain network associated with RAW.

Background

Safe and independent locomotion represents a fundamental motor function for humans that is essential for self-contained living and good quality of life [1,2,3,4,5]. Locomotion requires the ability to coordinate a number of different muscles acting on different joints [6,7,8], which are guided by cortical and subcortical brain structures within the locomotor network [9]. Structural and functional changes within the locomotor network are often accompanied by gait and balance impairments which are frequently considered to be the most significant concerns in individuals suffering from brain injuries or neurological diseases [51011]. Reduced walking speeds and step lengths [12] as well as non-optimal amount of gait variability [13,14,15] are common symptoms associated with gait impairments that increase the risk of falling [16].

In addition to manual-assisted therapy, robotic neurorehabilitation has often been applied in recent years [1718] because it provides early, intensive, task-specific and multi-sensory training which is thought to be effective for balance and gait recovery [171920]. Depending on the severity of the disease, movements can be completely guided or assisted, tailored to individual needs [17], using either stationary robotic systems or wearable powered exoskeletons.

Previous studies investigated the effectiveness of robot-assisted gait training (RAGT) in patients suffering from stroke [2122], multiple sclerosis [23,24,25,26], Parkinson’s disease [2728], traumatic brain injury [29] or spinal cord injury [30,31,32]. Positive effects of RAGT on walking speed [3334], leg muscle force [23] step length, and gait symmetry [2935] were reported. However, the results of different studies are difficult to summarize due to the lack of consistency in protocols and settings of robotic-assisted treatments (e.g., amount and frequency of training sessions, amount and type of provided robotic support) as well as fragmentary knowledge of the effects on functional brain reorganization, motor recovery and their relation [3637]. Therefore, it is currently a huge challenge to draw guidelines for robotic rehabilitation protocols [2236,37,38]. To design prologned personalized training protocols in robotic rehabilitation to maximize individual treatment effects [37], it is crucial to increase the understanding of changes in locomotor patterns [39] and brain signals [40] underlying RAGT and how they are related [3641].

A series of studies investigated the effects of robotic assistance (RA) on biomechanical gait patterns in healthy people [3942,43,44]. On one side, altered gait patterns were reported during robot-assisted walking (RAW) compared to unassisted walking (UAW), in particular, substantially higher muscle activity in the quadriceps, gluteus and adductor longus leg muscles and lower muscle activity in the gastrocnemius and tibialis anterior ankle muscles [3942] as well as reduced  lower-body joint angles due to the little medial-lateral hip movements [45,46,47]. On the other side, similar muscle activation patterns were observed during RAW compared to UAW [444849], indicating that robotic devices allow physiological muscle activation patterns during gait [48]. However, it is hypothesized that the ability to execute a physiological gait pattern depends on how the training parameters such as body weight support (BWS), guidance force (GF) or kinematic restrictions in the robotic devices are set [444850]. For example, Aurich-Schuler et al. [48] reported that the movements of the trunk and pelvis are more similar to UAW on a treadmill when the pelvis is not fixed during RAW, indicating that differences in musle activity and kinematic gait characteristics between RAW and UAW are due to the reduction in degrees of freedom that user’s experience while walking in the robotic device [45]. In line with this, a clinical concern that is often raised with respect to RAW is the lack of gait variability [454850]. It is assumed that since the robotic systems are often operated with 100% GF, which means that the devices attempt to force a particular gait pattern regardless of the user’s intentions, the user lacks the ability to vary and adapt his gait patterns [45]. Contrary to this, Hidler et al. [45] observed differences in kinematic gait patterns between subsequent steps during RAW, as demonstrated by variability in relative knee and hip movements. Nevertheless, Gizzi et al. [49] showed that the muscular activity during RAW was clearly more stereotyped and similar among individuals compared to UAW. They concluded that RAW provides a therapeutic approach to restore and improve walking that is more repeatable and standardized than approaches based on exercising during UAW [49].

In addition to biomechanical gait changes, insights into brain activity and intervention-related changes in brain activity that relate to gait responses, will contribute to the optimization of therapy interventions [4151]. Whereas the application of functional magnetic resonance imaging (fMRI), considered as gold standard for the assessment of activity in cortical and subcortical structures, is restricted due to the vulnerability for movement artifacts and the range of motion in the scanner [52], functional near infrared spectroscopy (fNIRS) is affordable and easily implementable in a portable system, less susceptible to motion artifacts, thus facilitation a wider range of application with special cohorts (e.g., children, patients) and in everyday environments (e.g., during a therapeutic session of RAW or UAW) [5354]. Although with lower resolution compared to fMRI [55], fNIRS also relies on the principle of neurovascular coupling and allows the indirect evaluation of cortical activation [5657] based on hemodynamic changes which are analogous to the blood-oxygenation-level-dependent responses measured by fMRI [56]. Despite limited depth sensitivity, which restricts the measurement of brain activity to cortical layers, it is a promising tool to investigate the contribution of cortical areas to the neuromotor control of gross motor skills, such as walking [53]. Regarding the cortical correlates of walking, numerous studies identified either increaesed oxygenated hemoglobin (Hboxy) concentration changes in the sensorimotor cortex (SMC) by using fNIRS [5357,58,59] or suppressed alpha and beta power in sensorimotor areas by using electroencephalography (EEG) [60,61,62] demonstrating that motor cortex and corticospinal tract contribute directly to the muscle activity of locomotion [63]. However, brain activity during RAW [366164,65,66,67,68], especially in patients [6970] or by using fNIRS [6869], is rarely studied [71].

Analyzing the effects of RA on brain activity in healthy volunteers, Knaepen et al. [36] reported significantly suppressed alpha and beta rhythms in the right sensory cortex during UAW compared to RAW with 100% GF and 0% BWS. Thus, significantly larger involvement of the SMC during UAW compared to RAW were concluded [36]. In contrast, increases of Hboxy were observed in motor areas during RAW compared UAW, leading to the conclusion that RA facilitated increased cortical activation within locomotor control systems [68]. Furthermore, Simis et al. [69] demonstrated the feasibility of fNIRS to evaluate the real-time activation of the primary motor cortex (M1) in both hemispheres during RAW in patients suffering from spinal cord injury. Two out of three patients exhibited enhanced M1 activation during RAW compared with standing which indicate the enhanced involvement of motor cortical areas in walking with RA [69].

To summarize, previous studies mostly focused the effects of RA on either gait characteristics or brain activity. Combined measurements investigating the effects of RA on both biomechanical and hemodynamic patterns might help for a better understanding of the neurophysiological mechanisms underlying gait and gait disorders as well as the effectiveness of robotic rehabilitation on motor recovery [3771]. Up to now, no consensus exists regarding how robotic devices should be designed, controlled or adjusted (i.e., device settings, such as the level of support) for synergistic interactions with the human body to achieve optimal neurorehabilitation [3772]. Therefore, further research concerning behavioral and neurophysiological mechanisms underlying RAW as well as the modulatory effect of RAGT on neuroplasticy and gait recovery are required giving the fact that such knowledge is of clinical relevance for the development of gait rehabilitation strategies.

Consequently, the central purpose of this study was to investigate both gait characteristics and hemodynamic activity during RAW to identify RAW-related changes in brain activity and their relationship to gait responses. Assuming that sensorimotor areas play a pivotal role within the cortical network of automatic gait [953] and that RA affects gait and brain patterns in young, healthy volunteers [39424568], we hypothesized that RA result in both altered gait and brain activity patterns. Based on previous studies, more stereotypical gait characteristics with less inter- and intraindividual variability are expected during RAW due to 100% GF and the fixed pelvis compared to UAW [4548], wheares brain activity in SMC can be either decreased [36] or increased [68].

Methods

This study was performed in accordance with the Declaration of Helsinki. Experimental procedures were performed in accordance with the recommendations of the Deutsche Gesellschaft für Psychologie and were approved by the ethical committee of the Medical Association Hessen in Frankfurt (Germany). The participants were informed about all relevant study-related contents and gave their written consent prior to the initiation of the experiment.

Participants

Twelve healthy subjects (9 female, 3 male; aged 25 ± 4 years), without any gait pathologies and free of extremity injuries, were recruited to participate in this study. All participants were right-handed, according to the Edinburg handedness-scale [73], without any neurological or psychological disorders and with normal or corrected-to-normal vision. All participants were requested to disclose pre-existing neurological and psychological conditions, medical conditions, drug intake, and alcohol or caffeine intake during the preceding week.

Experimental equipment

The Lokomat (Hocoma AG, Volketswil, Switzerland) is a robotic gait-orthosis, consisting of a motorized treadmill and a BWS system. Two robotic actuators can guide the knee and hip joints of participants to match pre-programmed gait patterns, which were derived from average joint trajectories of healthy walkers, using a GF ranging from 0 to 100% [7475] (Fig. 1a). Kinematic trajectories can be adjusted to each individual’s size and step preferences [45]. The BWS was adjusted to 30% body weight for each participant, and the control mode was set to provide 100% guidance [64].

figure1

Montage and Setup. a Participant during robot-assisted walking (RAW), with functional near-infrared spectroscopy (fNIRS) montage. b fNIRS montage; S = Sources; D = Detectors c Classification of regions of interest (ROI): supplementary motor area/premotor cortex  (SMA/PMC) and sensorimotor cortex (SMC) 

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[ARTICLE] Effects of virtual reality therapy on upper limb function after stroke and the role of neuroimaging as a predictor of a better response – Full Text

ABSTRACT

Background:

Virtual reality therapy (VRT) is an interactive intervention that induces neuroplasticity. The aim was to evaluate the effects of VRT associated with conventional rehabilitation for an upper limb after stroke, and the neuroimaging predictors of a better response to VRT.

Methods:

Patients with stroke were selected, and clinical neurological, upper limb function, and quality of life were evaluated. Statistical analysis was performed using a linear model comparing pre- and post-VRT. Lesions were segmented in the post-stroke computed tomography. A voxel-based lesion-symptom mapping approach was used to investigate the relationship between the lesion and upper limb function.

Results:

Eighteen patients were studied (55.5 ± 13.9 years of age). Quality of life, functional independence, and dexterity of the upper limb showed improvement after VRT (p < 0.001). Neuroimaging analysis showed negative correlations between the internal capsule lesion and functional recovery.

Conclusion:

VRT showed benefits for patients with stroke, but when there was an internal capsule lesion, a worse response was observed.

 

Stroke can be defined as a neurological deficit resulting from focal and acute central nervous system injury. It is considered a major cause of mortality and disability worldwide1. Stroke is the second leading cause of death in Brazil and the leading cause of chronic disability in adults, resulting in socioeconomic consequences and reduced quality of life. Therefore, it is an important public health problem, particularly because of long-term dependence on public health services2,3.

Cerebrovascular injury may damage the cells of the cortex and emerging axons, generating dysfunction of the upper motor neurons. Motor function can be impaired, reducing functional capacity, particularly that of the upper limbs4. Approximately 85% of individuals experience hemiparesis immediately after the stroke, particularly in an upper limb, and 55%–75% of these individuals have persistence of motor deficits, making it difficult to return to work and leisure, consequently worsening their quality of life5.

Epidemiological clinical studies have suggested that 33%–66% of stroke patients had no motor recovery after six months. Several techniques aiming to improve upper limb function are still being developed. However, the implementation of these techniques requires great team work, a high degree of specialization, and requires more time5,6. Currently, there are new approaches to rehabilitation, and virtual reality is still developing, with the objective of restoring the functional capacity of individuals after stroke as an easy, interactive, and low-cost intervention7,8.

The objective of stroke rehabilitation is to provide maximal physical, functional, and psychosocial recovery for the patients9. Comprehensive rehabilitation initiated early after stroke (within the first 24 hours) is generally accepted as being associated with better motor outcomes for these patients9. Strength training is an important part of the therapeutic process for upper-limb motor impairment after stroke10.

Virtual reality is defined as any hardware or software system that provides a simulated environment with real or imagined conditions that allow participating individuals to interact with the environment. The interaction is made by body movements using motion capture technology or by manipulating a device11. This interaction generates information necessary for proper understanding of the movement with particular emphasis on the upper limbs12. The technique consists of an avatar (graphic representation of the person) generated by the video game, where the individual manages a wireless control, directing the movement during the practice of different activities5. This is a good option for rehabilitation for individuals with stroke due to the variety of nonimmersive video game systems developed by the entertainment industry for home use.

This wide availability makes virtual reality an accessible and inexpensive rehabilitation method for rehabilitation centers13. Despite the ease of application, virtual reality therapy (VRT), added to conventional physical therapy has not been associated with a better outcome than recreational activity13. Adverse events usually are mild and the main effects described are transient dizziness and headache, pain and numbness13,14. Time since the onset of stroke, severity of impairment, and the type of device (commercial or customized) usually do not influence the outcome14. However, the variable methodology is an important bias for these investigations14. Therefore, virtual reality is still a promising tool. Some authors have reported that VRT can be combined with conventional rehabilitation to improve upper limb function after stroke15,16. The clinical situations wherein VRT may best be used have not yet been established in the literature. Also, the exact mechanism of action of this treatment modality is not yet fully understood.

The objectives of this study were to evaluate the effects of VRT combined with conventional rehabilitation for upper limb function in the recovery of individuals after stroke. Neuroimaging characteristics that could be used as predictors of a better response to VRT were also investigated.[…]

Continue —>  Effects of virtual reality therapy on upper limb function after stroke and the role of neuroimaging as a predictor of a better response

Figure Brain areas affected in nine patients with stroke and upper limb impairment underwent virtual reality therapy (A). Findings are overlaid in a template of axial magnetic resonance image slices and in a three-dimensional model of the brain (B). The color code bar in the inferior portion of the figure indicates the number of patients with the area injured. 

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[WEB SITE] New method based on artificial intelligence may help predict epilepsy outcomes

 

Medical University of South Carolina (MUSC) neurologists have developed a new method based on artificial intelligence that may eventually help both patients and doctors weigh the pros and cons of using brain surgery to treat debilitating seizures caused by epilepsy. This study, which focused on mesial temporal lobe epilepsy (TLE), was published in the September 2018 issue of Epilepsia. Beyond the clinical implications of incorporating this analytical method into clinicians’ decision making processes, this work also highlights how artificial intelligence is driving change in the medical field.

Despite the increase in the number of epilepsy medications available, as many as one-third of patients are refractory, or non-responders, to the medication. Uncontrolled epilepsy has many dangers associated with seizures, including injury from falls, breathing problems, and even sudden death. Debilitating seizures from epilepsy also greatly reduce quality of life, as normal activities are impaired.

Epilepsy surgery is often recommended to patients who do not respond to medications. Many patients are hesitant to undergo brain surgery, in part, due to fear of operative risks and the fact that only about two-thirds of patients are seizure-free one year after surgery. To tackle this critical gap in the treatment of this epilepsy population, Dr. Leonardo Bonilha and his team in the Department of Neurology at MUSC looked to predict which patients are likely to have success in being seizure free after the surgery.

Neurology Department Chief Resident Dr. Gleichgerrcht explains that they tried “to incorporate advanced neuroimaging and computational techniques to anticipate surgical outcomes in treating seizures that occur with loss of consciousness in order to eventually enhance quality of life”. In order to do this, the team turned to a computational technique, called deep learning, due to the massive amount of data analysis required for this project.

The whole-brain connectome, the key component of this study, is a map of all physical connections in a person’s brain. The brain map is created by in-depth analysis of diffusion magnetic resonance imaging (dMRI), which patients receive as standard-of-care in the clinic. The brains of epilepsy patients were imaged by dMRI prior to having surgery.

Deep learning is a statistical computational approach, within the realm of artificial intelligence, where patterns in data are automatically learned. The physical connections in the brain are very individualized and thus it is challenging to find patterns across multiple patients. Fortunately, the deep learning method is able to isolate the patterns in a more statistically reliable method in order to provide a highly accurate prediction.

Currently, the decision to perform brain surgery on a refractory epilepsy patient is made based on a set of clinical variables including visual interpretation of radiologic studies. Unfortunately, the current classification model is 50 to 70 percent accurate in predicting patient outcomes post-surgery. The deep learning method that the MUSC neurologists developed was 79 to 88 percent accurate. This gives the doctors a more reliable tool for deciding whether the benefits of surgery outweigh the risks for the patient.

A further benefit of this new technique is that no extra diagnostic tests are required for the patients, since dMRIs are routinely performed with epilepsy patients at most centers.

This first study was retrospective in nature, meaning that the clinicians looked at past data. The researchers propose that an ideal next step would include a multi-site prospective study. In a prospective study, they would analyze the dMRI scans of patients prior to surgery and follow-up with the patients for at least one year after surgery. The MUSC neurologists also believe that integrating the brain’s functional connectome, which is a map of simultaneously occurring neural activity across different brain regions, could enhance the prediction of outcomes.

Dr. Gleichgerrcht says that the novelty in the development of this study lies in the fact that this “is not a question of human versus machine, as is often the fear when we hear about artificial intelligence. In this case, we are using artificial intelligence as an extra tool to eventually make better informed decisions regarding a surgical intervention that holds the hope for a cure of epilepsy in a large number of patients.”

 

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[WEB SITE] Largest-ever study to examine anatomical alterations in the brains of epilepsy patients

Largest-ever study to examine anatomical alterations in the brains of epilepsy patients 

An international research consortium used neuroimaging techniques to analyze the brains of more than 3,800 volunteers in different countries. The largest study of its kind ever conducted set out to investigate anatomical similarities and differences in the brains of individuals with different types of epilepsy and to seek markers that could help with prognosis and treatment.

Epilepsy’s seizure frequency and severity, as well as the patient’s response to drug therapy, vary with the part of the brain affected and other poorly understood factors. Data from the scientific literature suggests that roughly one-third of patients do not respond well to anti-epileptic drugs. Research has shown that these individuals are more likely to develop cognitive and behavioral impairments over the years.

The new study was conducted by a specific working group within an international consortium called ENIGMA, short for Enhancing NeuroImaging Genetics through Meta-Analysis, established to investigate several neurological and psychiatric diseases. Twenty-four cross-sectional samples from 14 countries were included in the epilepsy study.

Altogether, the study included data for 2,149 people with epilepsy and 1,727 healthy control subjects (with no neurological or psychiatric disorders). The Brazilian Research Institute for Neuroscience and Neurotechnology (BRAINN), which participated in the multicenter study, was the center with the largest sample, comprising 291 patients and 398 controls. Hosted in Brazil, at the State University of Campinas (UNICAMP), BRAINN is a Research, Innovation and Dissemination Center (RIDC http://cepid.fapesp.br/en/home/) supported by the Sao Paulo Research Foundation – FAPESP.

“Each center was responsible for collecting and analyzing data on its own patients. All the material was then sent to the University of Southern California’s Imaging Genetics Center in the US, which consolidated the results and performed a meta-analysis,” said Fernando Cendes, a professor at UNICAMP and coordinator of BRAINN.

A differential study

All volunteers were subjected to MRI scans. According to Cendes, a specific protocol was used to acquire three-dimensional images. “This permitted image post-processing with the aid of computer software, which segmented the images into thousands of anatomical points for individual assessment and comparison,” he said.

According to the researcher, advances in neuroimaging techniques have enabled the detection of structural alterations in the brains of people with epilepsy that hadn’t been noticed previously.

Cendes also highlighted that this is the first epilepsy study built on a really large number of patients, which allowed researchers to obtain more robust data. “There were many discrepancies in earlier studies, which comprised a few dozen or hundred volunteers.”

The patients included in the study were divided into four subgroups: mesial temporal lobe epilepsy (MTLE) with left hippocampal sclerosis, MTLE with right hippocampal sclerosis, idiopathic (genetic) generalized epilepsy, and a fourth group comprising various less common subtypes of the disease.

The analysis covered both patients who had had epilepsy for years and patients who had been diagnosed recently. According to Cendes, the analysis – whose results were published in the international journal Brain – aimed at the identification of atrophied brain regions in which the cortical thickness was smaller than in the control group.

First analysis

The researchers first analyzed data from the four patient subgroups as a whole and compared them with the controls to determine whether there were anatomical alterations common to all forms of epilepsy. “We found that all four subgroups displayed atrophy in areas of the sensitive-motor cortex and also in some parts of the frontal lobe,” Cendes said.

“Ordinary MRI scans don’t show anatomical alterations in cases of genetic generalized epilepsy,” Cendes said. “One of the goals of this study was to confirm whether areas of atrophy also occur in these patients. We found that they do.”

This finding, he added, shows that in the case of MTLE, there are alterations in regions other than those in which seizures are produced (the hippocampus, parahippocampus, and amygdala). Brain impairment is, therefore, more extensive than previously thought.

Cendes also noted that a larger proportion of the brain was compromised in patients who had had the disease for longer. “This reinforces the hypothesis that more brain regions atrophy and more cognitive impairment occurs as the disease progresses.”

The next step was a separate analysis of each patient subgroup in search of alterations that characterize each form of the disease. The findings confirmed, for example, that MTLE with left hippocampal sclerosis is associated with alterations in different neuronal circuits from those associated with MTLE with right hippocampal sclerosis.

“Temporal lobe epilepsy occurs in a specific brain region and is therefore termed a focal form of the disease. It’s also the most common treatment-refractory subtype of epilepsy in adults,” Cendes said. “We know it has different and more severe effects when it involves the left hemisphere than the right. They’re different diseases.”

“These two forms of the disease are not mere mirror-images of each other,” he said. “When the left hemisphere is involved, the seizures are more intense and diffuse. It used to be thought that this happened because the left hemisphere is dominant for language, but this doesn’t appear to be the only reason. Somehow, it’s more vulnerable than the right hemisphere.”

In the GGE group, the researchers observed atrophy in the thalamus, a central deep-lying brain region above the hypothalamus, and in the motor cortex. “These are subtle alterations but were observed in patients with epilepsy and not in the controls,” Cendes said.

Genetic generalized epilepsies (GGEs) may involve all brain regions but can usually be controlled by drugs and are less damaging to patients.

Future developments

From the vantage point of the coordinator for the FAPESP-funded center, the findings published in the article will benefit research in the area and will also have future implications for the diagnosis of the disease. In parallel with their anatomical analysis, the group is also evaluating genetic alterations that may explain certain hereditary patterns in brain atrophy. The results of this genetic analysis will be published soon.

“If we know there are more or less specific signatures of the different epileptic subtypes, instead of looking for alterations everywhere in the brain, we can focus on suspect regions, reducing cost, saving time and bolstering the statistical power of the analysis. Next, we’ll be able to correlate these alterations with cognitive and behavioral dysfunction,” Cendes said.

 

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[WEB PAGE] Excitatory magnetic brain stimulation reduces emotional arousal to fearful faces, study shows

February 6, 2018

A new study in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging looks at the modulation of emotion in the brain

A new study published in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging reports that processing of negative emotion can be strengthened or weakened by tuning the excitability of the right frontal part of the brain.

Using magnetic stimulation outside the brain, a technique called repetitive transcranial magnetic stimulation (rTMS), researchers at University of Münster, Germany, show that, despite the use of inhibitory stimulation currently used to treat depression, excitatory stimulation better reduced a person’s response to fearful images.

The findings provide the first support for an idea that clinicians use to guide treatment in depression, but has never been verified in a lab. “This study confirms that modulating the frontal region of the brain, in the right hemisphere, directly effects the regulation of processing of emotional information in the brain in a ‘top-down’ manner,” said Cameron Carter, M.D., Editor of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, referring to the function of this region as a control center for the emotion-generating structures of the brain. “These results highlight and expand the scope of the potential therapeutic applications of rTMS,” said Dr. Carter.

In depression, processing of emotion is disrupted in the frontal region of both the left and right brain hemispheres (known as the dorsolateral prefrontal cortices, dlPFC). The disruptions are thought to be at the root of increased negative emotion and diminished positive emotion in the disorder. Reducing excitability of the right dlPFC using inhibitory magnetic stimulation has been shown to have antidepressant effects, even though it’s based on an idea-that this might reduce processing of negative emotion in depression-that has yet to be fully tested in humans.

Co-first authors Swantje Notzon, M.D., and Christian Steinberg, Ph.D, and colleagues divided 41 healthy participants into two groups to compare the effects of a single-session of excitatory or inhibitory magnetic stimulation of the right dlPFC. They performed rTMS while the participants viewed images of fearful faces to evoke negative emotion, or neutral faces for a comparison.

Excitatory and inhibitory rTMS had opposite effects-excitatory reduced visual sensory processing of fearful faces, whereas inhibitory increased visual sensory processing. Similarly, excitatory rTMS reduced participants’ reaction times to respond to fearful faces and reduced feelings of emotional arousal to fearful faces, which were both increased by inhibitory rTMS.

Although the study was limited to healthy participants, senior author Markus Junghöfer, Ph.D., notes that “…these results should encourage more research on the mechanisms of excitatory and inhibitory magnetic stimulation of the right dlPFC in the treatment of depression.”

 

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[WEB SITE] New method uses advanced noninvasive neuroimaging to localize and identify epileptic lesions

Epilepsy affects more than 65 million people worldwide. One-third of these patients have seizures that are not controlled by medications. In addition, one-third have brain lesions, the hallmark of the disease, which cannot be located by conventional imaging methods. Researchers at the Perelman School of Medicine at the University of Pennsylvania have piloted a new method using advanced noninvasive neuroimaging to recognize the neurotransmitter glutamate, thought to be the culprit in the most common form of medication-resistant epilepsy. Their work is published today in Science Translational Medicine.

Glutamate is an amino acid which transmits signals from neuron to neuron, telling them when to fire. Glutamate normally docks with the neuron, gives it the signal to fire and is swiftly cleared. In patients with epilepsy, stroke and possibly ALS, the glutamate is not cleared, leaving the neuron overwhelmed with messages and in a toxic state of prolonged excitation.

In localization-related epilepsy, the most common form of medication-resistant epilepsy, seizures are generated in a focused section of the brain; in 65 percent of patients, this occurs in the temporal lobe. Removal of the seizure-generating region of the temporal lobe, guided by preoperative MRI, can offer a cure. However, a third of these patients have no identified abnormality on conventional imaging studies and, therefore, more limited surgical options.

“Identification of the brain region generating seizures in location-related epilepsy is associated with significantly increased chance of seizure freedom after surgery,” said the new study’s lead author, Kathryn Davis, MD, MSTR, an assistant professor of Neurology at Penn. “The aim of the study was to investigate whether a novel imaging method, developed at Penn, could use glutamate to localize and identify the epileptic lesions and map epileptic networks in these most challenging patients.”

“We theorized that if we could develop a technique which allows us to track the path of and make noninvasive measurements of glutamate in the brain, we would be able to better identify the brain lesions and epileptic foci that current methods miss,” said senior author Ravinder Reddy, PhD, a professor of Radiology and director of Penn’s Center for Magnetic Resonance and Optical Imaging.

Reddy’s lab developed the glutamate chemical exchange saturation transfer (GluCEST) imaging method, a very high resolution magnetic resonance imaging contrast method not available before now, to measure how much glutamate was in different regions of the brain including the hippocampi, two structures within the left and right temporal lobes responsible for short- and long-term memory and spatial navigation and the most frequent seizure onset region in adult epilepsy patients.

The study tested four patients with medication-resistant epilepsy and 11 controls. In all four patients, concentrations of glutamate were found to be higher in one of the hippocampi, and confirmatory methods (electroencephalography and magnetic resonance spectra) verified independently that the hippocampus with the elevated glutamate was located in the same hemisphere as the epileptic focus/lesion. Consistent lateralization to one side was not seen in the control group.

While preliminary, this work indicates the ability of GluCEST to detect asymmetrical hippocampal glutamate levels in patients thought to have nonlesional temporal lobe epilepsy. The authors say this approach could reduce the need for invasive intracranial monitoring, which is often associated with complications, morbidity risk, and added expense.

“This demonstration that GluCEST can localize small brain hot spots of high glutamate levels is a promising first step in our research,” Davis said. “By finding the epileptic foci in more patients, this approach could guide clinicians toward the best therapy for these patients, which could translate to a higher rate of successful surgeries and improved outcomes from surgery or other therapies in this difficult disease.”

Source: Penn Medicine

Source: New method uses advanced noninvasive neuroimaging to localize and identify epileptic lesions

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