Posts Tagged MRI

[BLOG POST] 9 promising advances in the management of traumatic brain injury – The Neurology Lounge

 

Traumatic brain injury (TBI) is simply disheartening. It is particularly devastating because it usually affects young people in their prime, with the consequent personal, social, and economic consequences. This blog has previously touched a little on TBI with the post titled Will Smith and chronic traumatic encephalopathy? This was a light-hearted take on concussion in sports, but traumatic brain injury is nothing but a serious burden. So what are the big brains in white coats doing to take down this colossus? Quite a lot it seems. Here, for a taster, are 9 promising advances in the management of traumatic brain injury.

Better understanding of pathology

An amyloid PET imaging study by Gregory Scott and colleagues, published in the journal Neurology, reported a rather surprising link between the pathology seen in long-term survivors of traumatic brain injury, with the pathology seen in Alzheimers disease (AD). In both conditions, there is an increased burden of β-amyloid () in the brain, produced by damage to the nerve axons. The paper, titled Amyloid pathology and axonal injury after brain trauma, however notes that the pattern of  deposition in TBI can be distinguished from the one seen in AD. The big question this finding raises is, does TBI eventually result in AD? The answer remains unclear, and this is discussed in the accompanying editorial titled Amyloid plaques in TBI.

Blood tests to detect concussion

The ideal biomarker for any disorder is one which is easy to detect, such as a simple blood test. A headline that screams Blood test may offer new way to detect concussions is therefore bound to attract attention. The benefits of such a test would be legion, especially if the test can reduce the requirement for CT scans which carry the risks of radiation exposure. This is where glial fibrillary acidic protein (GFAP) may be promising. The research is published in the journal, Academic Research Medicine, with a rather convoluted title, Performance of Glial Fibrillary Acidic Protein in Detecting Traumatic Intracranial Lesions on Computed Tomography in Children and Youth With Mild Head Trauma. The premise of the paper is the fact that GFAP is released into the blood stream from the glial cells of the brain soon after brain injury. What the authors therefore did was to take blood samples within 6 hours of TBI in children. And they demonstrated that GFAP levels are significantly higher following head injury, compared to injuries elsewhere in the body. This sounds exciting, but we have to wait and see where it takes us.

Advanced imaging

Brain Scars Detected in Concussions is the attention-grabbing headline for this one, published in MIT Technology Review. Follow the trail and it leads to the actual scientific paper in the journal Radiology, with a fairly straight-forward title, Findings from Structural MR Imaging in Military Traumatic Brain Injury The authors studied >800 subjects in what is the largest trial of traumatic brain injury in the military. Using high resolution 3T brain magnetic resonance imaging (MRI), they demonstrated that even what is reported as mild brain injury leaves its marks on the brain, usually in the form of white matter hyperintense lesions and pituitary abnormalities. It simply goes to show that nothing is mild when it comes to the brain, the most complex entity in the universe.

Implanted monitoring sensors

Current technologies which monitor patients with traumatic brain injury are, to say the least, cumbersome and very invasive. Imagine if all the tubes and wires could be replaced with microsensors, smaller than grains of rice, implanted in the brain. These would enable close monitoring of critical indices such as temperature and intracranial pressure. And imagine that these tiny sensors just dissolve away when they have done their job, leaving no damage. Now imagine that all this is reality. I came across this one from a CBS News piece titled Tiny implanted sensors monitor brain injuries, then dissolve away. Don’t scoff yet, it is grounded in a scientific paper published in the prestigious journal, Nature, under the title Bioresorbable silicon electronic sensors for the brain. But don’t get too exited yet, this is currently only being trialled in mice.

Drugs to reduce brain inflammation

What if the inflammation that is set off following traumatic brain injury could be stopped in its tracks? Then a lot of the damage from brain injury could be avoided. Is there a drug that could do this? Well, it seems there is, and it is the humble blood pressure drug Telmisartan. This one came to my attention in Medical News Today, in a piece titled Hypertension drug reduces inflammation from traumatic brain injury. Telmisartan seemingly blocks the production of a pro-inflammatory protein in the liver. By doing this, Telmisartan may effectively mitigate brain damage, but only if it is administered very early after traumatic brain injury. The original paper is published in the prestigious journal, Brain, and it is titled Neurorestoration after traumatic brain injury through angiotensin II receptor blockage. Again, don’t get too warm and fuzzy about this yet; so far, only mice have seen the benefits.

Treatment of fatigue

Fatigue is a major long-term consequence of traumatic brain injury, impairing the quality of life of affected subjects in a very frustrating way. It therefore goes without saying, (even if it actually has to be said), that any intervention that alleviates the lethargy of TBI will be energising news. And an intervention seems to be looming in the horizon! Researchers writing in the journal, Acta Neurologica Scandinavica, have reported that Methylphenidate significantly improved fatigue in the 20 subjects they studied. Published under the title Long-term treatment with methylphenidate for fatigue after traumatic brain injury, the study is rather small, not enough to make us start dancing the jig yet. The authors have rightly called for larger randomized trials to corroborate their findings, and we are all waiting with bated breaths.

Treatment of behavioural abnormalities

Many survivors of traumatic brain injury are left with behavioural disturbances which are baffling to the victim, and challenging to their families. Unfortunately, many of the drugs used to treat these behaviours are not effective. This is where some brilliant minds come in, with the idea of stimulating blood stem cell production to enhance behavioural recovery. I am not clear what inspired this idea, but the idea has inspired the paper titled Granulocyte colony-stimulating factor promotes behavioral recovery in a mouse model of traumatic brain injury. The authors report that the administration of G‐CSF for 3 days after mild TBI improved the performance of mice in a water maze…within 2 weeks. As the water maze is a test of learning and memory, and not of behaviour, I can only imagine the authors thought-surely only well-behaved mice will bother to take the test. It is however fascinating that G‐CSF treatment actually seems to fix brain damage in TBI, and it does so by stimulating astrocytosis and microgliosis, increasing the expression of neurotrophic factors, and generating new neurons in the hippocampus“. The promise, if translated to humans, should therefore go way beyond water mazes, but we have to wait and see.

Drugs to accelerate recovery

The idea behind using Etanercept to promote recovery from brain injury sound logical. A paper published in the journal, Clinical Drug Investigation, explains that brain injury sets off a chronic lingering inflammation which is driven by tumour necrosis factor (TNF). A TNF inhibitor will therefore be aptly placed to stop the inflammation. What better TNF inhibitor than Eternacept to try out, and what better way to deliver it than directly into the nervous system. And this is what the authors of the paper, titled Immediate neurological recovery following perispinal etanercept years after brain injury, did. And based on their findings, they made some very powerful claims: “a single dose of perispinal etanercept produced an immediate, profound, and sustained improvementin expressive aphasia, speech apraxia, and left hemiparesis in a patient with chronic, intractable, debilitating neurological dysfunction present for more than 3 years after acute brain injury”. A single patient, mind you. Not that I am sceptical by nature, but a larger study confirming this will be very reassuring.

Neuroprotection

And finally, that elusive holy grail of neurological therapeutics, neuroprotection. Well, does it exist? A review of the subject published in the journal, International Journal of Molecular Sciences, paints a rather gloomy picture of the current state of play. Titled Neuroprotective Strategies After Traumatic Brain Injury, it said “despite strong experimental data, more than 30 clinical trials of neuroprotection in TBI patients have failed“. But all is not lost. The authors promise that “recent changes in experimental approach and advances in clinical trial methodologyhave raised the potential for successful clinical translation”. Another review article, this time in the journal Critical Care, doesn’t offer any more cheery news about the current state of affairs when it says that the “use of these potential interventions in human randomized controlled studies has generally given disappointing results”. But the review, titled Neuroprotection in acute brain injury: an up-to-date review, goes through promising new strategies for neuroprotection following brain injury: these include hyperbaric oxygensex hormones, volatile anaesthetic agents, and mesenchymal stromal cells. The authors conclude on a positive note: “despite all the disappointments, there are many new therapeutic possibilities still to be explored and tested”.

What an optimistic way to end! We are not quite there yet, but these are encouraging steps.

via 9 promising advances in the management of traumatic brain injury | The Neurology Lounge

, , , , , , , , , , , , , , , , , ,

Leave a comment

[Abstract] Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review

Acquired brain injury (ABI) is associated with a range of cognitive and motor deficits, and poses a significant personal, societal, and economic burden. Rehabilitation programs are available that target motor skills or cognitive functioning. In this review, we summarize the existing evidence that training may enhance structural neuroplasticity in patients with ABI, as assessed using structural magnetic resonance imaging (MRI)–based techniques that probe microstructure or morphology. Twenty-five research articles met key inclusion criteria. Most trials measured relevant outcomes and had treatment benefits that would justify the risk of potential harm. The rehabilitation program included a variety of task-oriented movement exercises (such as facilitation therapy, postural control training), neurorehabilitation techniques (such as constraint-induced movement therapy) or computer-assisted training programs (eg, Cogmed program). The reviewed studies describe regional alterations in white matter architecture and/or gray matter volume with training. Only weak-to-moderate correlations were observed between improved behavioral function and structural changes. While structural MRI is a powerful tool for detection of longitudinal structural changes, specific measures about the underlying biological mechanisms are lacking. Continued work in this field may potentially see structural MRI metrics used as biomarkers to help guide treatment at the individual patient level.

via Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review – Karen Caeyenberghs, Adam Clemente, Phoebe Imms, Gary Egan, Darren R. Hocking, Alexander Leemans, Claudia Metzler-Baddeley, Derek K. Jones, Peter H. Wilson, 2018

, , , , , , , ,

Leave a comment

[WEB SITE] Head MRI: Uses, results, and what to expect – Educational

What to know about head and brain MRI scans

Last reviewed

Doctors use MRI scans to diagnose and monitor head injuries and to check for abnormalities in the head or brain.

Magnetic resonance imaging (MRI) scans provide 3-D images of specific body parts. The scan produces highly detailed images from every angle. Depending on the purpose of the scan, a doctor may recommend contrast, which is a substance that a person takes beforehand. It helps the images to be more clearly defined.

An MRI scan is painless and noninvasive. The length of the procedure varies, depending on the situation.

In this article, we take a close look at head MRI scans in adults and children. We discuss their uses, what to expect during a scan, and how a person receives the results.

Purpose and uses of head MRI scans

Man having head and brain MRI

An MRI scan can provide detailed imagery of soft tissue.

MRI scans allow doctors to see what is happening inside the body. These scans do not produce radiation, unlike CT scans and X-rays.

MRI scans use strong magnetic forces and radio waves to create images. They can scan bone, organs, and tissue, which makes them ideal for a complex body part like the head.

MRI scans show a higher level of detail than other imaging techniques, especially in soft tissue. This is important when examining the brain or brain stem for damage or disease.

A doctor may recommend an MRI head scan if they suspect that a person has:

Procedure and what to expect during a head MRI

A head MRI is noninvasive. When a person arrives at the clinic, a doctor or technician will talk them through the process and tell them what to expect.

Preparation

First, a healthcare professional will ask a series of questions about a person’s medical history.

Radiographers also need to know if a woman is pregnant. Doctors tend not to recommend MRI scans during pregnancy, because it is unclear whether the magnetic force can affect fetal development.

They will also ask if a person has any metallic objects, such as piercings, metal plates, watches, or jewelry. These can interfere with the scan, and a person must remove them before entering the scanner.

Other metallic objects that can interfere with a scan include:

  • brain aneurysm clips
  • cochlear implants
  • dental fillings and bridges
  • eye implants
  • metallic fragments in the eyes or blood vessels
  • metal plates, wires, screws, or rods
  • surgical clips or staples

A healthcare team member will usually ask a person to put on a hospital gown. They will store a person’s clothes and any jewelry in a safe locker until the scan is finished.

During the scan

The technician will bring the person into the room that contains the MRI scanner. The person will lie on a sliding trolley, and the technician may cover them with a sheet.

The technician will then position the trolley so that the person’s head and neck are inside the MRI scanner. They will leave the room and speak to the person through a radio.

People should be aware of the following:

  • Pillows or foam blocks on the trolley will keep the head in the right position.
  • MRI machines make a lot of noise, so expect to hear loud hums, knocking sounds, and general electronic noise. Technicians will usually provide headphones or earplugs.
  • People must stay very still inside the scanner to ensure clear, accurate images. If a person moves, they may have to repeat the scan. If someone, such as a person with Parkinson’s, has trouble lying still, a technician may offer restraints to help.
  • Every MRI machine has a call button. If a person feels anxious or wants to stop the procedure, they can press the call button and talk to the medical staff.
  • Most tattoos are safe in an MRI. However, some inks contain traces of metal, which can cause heat or discomfort during a scan. If a person feels any discomfort, they should tell the radiographer.

The medical team may offer anesthetics or sedatives to people who have extreme claustrophobia.

If a person has taken a sedative, they should avoid driving themselves home. Also, a person needs time to recover from an anesthetic at the medical center. In the event of an allergic reaction, the healthcare team will keep the person under observation.

Types of MRI scanner

MRI scanner machine

MRI machines come in a range of sizes.

Several types of scanners can provide a head MRI. The size of the machine will depend on the purpose of the scan and whether the person has claustrophobia.

Types of scanner include:

  • Closed bore. These look like enormous tubes, which a person enters by lying on a sliding bench.
  • Short bore. In this type of machine, the tubular part is shorter, making it less likely to trigger claustrophobia.
  • Wide bore. The opening of the tubular area can be around 70 centimeters in these machines.
  • Open MRI. These come in a variety of shapes. They can have an open side or top.

The narrower the bore, the more detailed the image will be.

Head MRI scans with contrast vs. no contrast

Contrast is a magnetic substance. If a person drinks or receives an injection of contrast before a scan, it can help to improve the image. The majority of MRI scans do not require contrast.

The doctor and radiologist will decide if contrast is necessary, and a person takes it orally or by injection.

Contrast travels to organs and tissue through the bloodstream. The MRI procedure is the same, whether or not it requires contrast.

Contrast makes tissues and organs stand out on the MRI image. This can illuminate early abnormal tissue growth, including tumors. Receiving an early diagnosis can help improve a person’s outlook.

Scans related to the following issues can require contrast:

There is a small chance that a person may have an allergic reaction to contrast materials. Before administering the contrast, a doctor will ask about:

  • allergies
  • current medications
  • medical history
  • recent illnesses or operations

After taking the contrast, a person should check for any side effects. Report any adverse effects to a healthcare provider.

Results

The radiographer will review and interpret the scans. They will then contact the doctor with the results. This can take several days unless it was an emergency scan.

A person can request to see their scans by asking their doctor. The doctor may need a follow-up scan, and they will explain why.

Costs

The costs of an MRI procedure, and how much insurance will cover, varies.

There may also be associated costs, for contrast, anesthesia, and additional procedures.

Speak to the healthcare provider for an accurate estimate.

Head MRI scans in children

Doctor showing child MRI results

A doctor can explain the MRI process to children before undergoing the procedure.

Medical procedures can be scary. It is important for a caregiver to find out the details and explain them to the child beforehand, to reduce any anxiety. Some hospitals have leaflets that help to explain certain procedures.

Head MRI scans for children are almost identical to those for adults. The main difference is the use of a coil.

An MRI coil fits around the child’s head as they lie or sit in the machine because their heads are smaller.

Young children and babies find it hard to stay still for long, and the healthcare provider may recommend an intravenous sedative. The medical team will monitor them throughout the procedure.

Usually, a caregiver stays with the child during the scan. If this is not possible, the caregiver can often wait in the radiographer’s station.

Summary

Head MRI scans are an important tool for diagnosing and monitoring. They can indicate changes in tissue, which is vital in assessing many conditions, particularly those affecting the brain.

Unlike X-rays and CT scans, MRI scans do not involve radiation. They present no risk, apart from triggering certain anxieties or claustrophobia. There are ways to prevent this from happening.

MRI scanners are being improved all the time. With the new generation of scanners, the aim is to cut down scan times and enhance accuracy.

 

via Head MRI: Uses, results, and what to expect

, , , , , ,

Leave a comment

[BOOK Chapter] Epilepsy Imaging -Abstract+References| SpringerLink

Abstract

Epilepsy is one of the most frequent chronic neurological disorder. The role of neuroimaging is crucial in identifying the causal lesion, as its characterization may play a major role for referring the patients to surgery. This chapter reviews the role of MRI in epilepsy, with a special focus on focal intractable epilepsies. A standard protocol is ineffective for epilepsy imaging. By contrast, an optimized protocol carried out by a neuroradiologist experienced in epilepsy imaging and guided by clinical and electroclinical data on a high field magnet improves the detection of the causal lesion. Advanced sequences such as double inversion recovery, arterial spin labeling, or relaxometry can especially be useful for localizing and characterizing the epileptogenic zone. Hippocampal sclerosis is the most frequent cause of intractable temporal epilepsy, and focal cortical dysplasia is the most frequent extratemporal lesion. Functional MRI and diffusion tensor are crucial when planning a surgical treatment.

References

  1. 1.
    Pitkänen A, Löscher W, Vezzani A, Becker AJ, Simonato M, Lukasiuk K, Gröhn O, Bankstahl JP, Friedman A, Aronica E, Gorter JA, Ravizza T, Sisodiya SM, Kokaia M, Beck H (2016) Advances in the development of biomarkers for epilepsy. Lancet Neurol 15:843–856.  https://doi.org/10.1016/S1474-4422(16)00112-5CrossRefPubMedGoogle Scholar
  2. 2.
    Gaillard WD, Chiron C, Helen Cross J, Simon Harvey A, Kuzniecky R, Hertz-Pannier L, Gilbert Vezina L (2009) Guidelines for imaging infants and children with recent-onset epilepsy. Epilepsia 50:2147–2153CrossRefGoogle Scholar
  3. 3.
    Kwan P, Arzimanoglou A, Berg AT, Brodie MJ, Allen Hauser W, Mathern G, Moshé SL, Perucca E, Wiebe S, French J (2010) Definition of drug resistant epilepsy: consensus proposal by the ad hoc task force of the ILAE commission on therapeutic strategies. Epilepsia 51:1069–1077.  https://doi.org/10.1111/j.1528-1167.2009.02397.xCrossRefPubMedGoogle Scholar
  4. 4.
    de Tisi J, Bell GS, Peacock JL, McEvoy AW, Harkness WF, Sander JW, Duncan JS (2011) The long-term outcome of adult epilepsy surgery, patterns of seizure remission, and relapse: a cohort study. Lancet 378:1388–1395CrossRefGoogle Scholar
  5. 5.
    Téllez-Zenteno JF, Ronquillo LH, Moien-Afshari F, Wiebe S (2010) Surgical outcomes in lesional and non-lesional epilepsy: a systematic review and meta-analysis. Epilepsy Res 89:310–318CrossRefGoogle Scholar
  6. 6.
    von Oertzen J, Urbach H, Jungbluth S, Kurthen M, Reuber M, Fernández G, Elger CE (2002) Standard magnetic resonance imaging is inadequate for patients with refractory focal epilepsy. J Neurol Neurosurg Psychiatry 73:643–647.  https://doi.org/10.1136/jnnp.73.6.643CrossRefGoogle Scholar
  7. 7.
    Cendes F (2013) Neuroimaging in investigation of patients with epilepsy. Continuum Lifelong Learning Neurol 19:623–642CrossRefGoogle Scholar
  8. 8.
    Wellmer J, Quesada CM, Rothe L, Elger CE, Bien CG, Urbach H (2013) Proposal for a magnetic resonance imaging protocol for the detection of epileptogenic lesions at early outpatient stages. Epilepsia 54:1977–1987CrossRefGoogle Scholar
  9. 9.
    Saini J, Singh A, Kesavadas C, Thomas B, Rathore C, Bahuleyan B, Radhakrishnan A, Radhakrishnan K (2010) Role of three-dimensional fluid-attenuated inversion recovery (3D FLAIR) and proton density magnetic resonance imaging for the detection and evaluation of lesion extent of focal cortical dysplasia in patients with refractory epilepsy. Acta Radiol 51:218–225CrossRefGoogle Scholar
  10. 10.
    Li Q, Zhang Q, Sun H, Zhang Y, Bai R (2011) Double inversion recovery magnetic resonance imaging at 3 T: diagnostic value in hippocampal sclerosis. J Comput Assist Tomogr 35:290–293CrossRefGoogle Scholar
  11. 11.
    Rugg-Gunn FJ, Boulby PA, Symms MR, Barker GJ, Duncan JS (2006) Imaging the neocortex in epilepsy with double inversion recovery imaging. NeuroImage 31:39–50CrossRefGoogle Scholar
  12. 12.
    Mellerio C, Labeyrie M-A, Chassoux F, Roca P, Alami O, Plat M, Naggara O, Devaux B, Meder J-F, Oppenheim C (2014) 3T MRI improves the detection of transmantle sign in type 2 focal cortical dysplasia. Epilepsia 55:117–122.  https://doi.org/10.1111/epi.12464CrossRefPubMedGoogle Scholar
  13. 13.
    Phal PM, Usmanov A, Nesbit GM, Anderson JC, Spencer D, Wang P, Helwig JA, Roberts C, Hamilton BE (2008) Qualitative comparison of 3-T and 1.5-T MRI in the evaluation of epilepsy. Am J Roentgenol 191:890–895CrossRefGoogle Scholar
  14. 14.
    Rubinger L, Chan C, D’Arco F, Moineddin R, Muthaffar O, Rutka JT, Snead OC, Smith ML, Widjaja E (2016) Change in presurgical diagnostic imaging evaluation affects subsequent pediatric epilepsy surgery outcome. Epilepsia 57:32–40.  https://doi.org/10.1111/epi.13229CrossRefPubMedGoogle Scholar
  15. 15.
    Zijlmans M, de Kort GA, Witkamp TD, Huiskamp GM, Seppenwoolde J-H, van Huffelen AC, Leijten FS (2009) 3T versus 1.5 T phased-array MRI in the presurgical work-up of patients with partial epilepsy of uncertain focus. J Magn Reson Imaging 30:256–262CrossRefGoogle Scholar
  16. 16.
    Coras R, de Boer OJ, Armstrong D, Becker A, Jacques TS, Miyata H, Thom M, Vinters HV, Spreafico R, Oz B et al (2012) Good interobserver and intraobserver agreement in the evaluation of the new ILAE classification of focal cortical dysplasias. Epilepsia 53:1341–1348CrossRefGoogle Scholar
  17. 17.
    Wiggins GC, Polimeni JR, Potthast A, Schmitt M, Alagappan V, Wald LL (2009) 96-channel receive-only head coil for 3 tesla: design optimization and evaluation. Magn Reson Med 62:754–762.  https://doi.org/10.1002/mrm.22028CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Oppenheim C, Dormont D, Biondi A, Lehéricy S, Hasboun D, Clémenceau S, Baulac M, Marsault C (1998) Loss of digitations of the hippocampal head on high-resolution fast spin-echo MR: a sign of mesial temporal sclerosis. Am J Neuroradiol 19:457–463PubMedGoogle Scholar
  19. 19.
    Kim DW, Lee SK, Nam H, Chu K, Chung CK, Lee S-Y, Choe G, Kim HK (2010) Epilepsy with dual pathology: surgical treatment of cortical dysplasia accompanied by hippocampal sclerosis. Epilepsia 51:1429–1435.  https://doi.org/10.1111/j.1528-1167.2009.02403.xCrossRefPubMedGoogle Scholar
  20. 20.
    Spencer S, Huh L (2008) Outcomes of epilepsy surgery in adults and children. Lancet Neurol 7:525–537CrossRefGoogle Scholar
  21. 21.
    Maccotta L, Moseley ED, Benzinger TL, Hogan RE (2015) Beyond the CA1 subfield: local hippocampal shape changes in MRI-negative temporal lobe epilepsy. Epilepsia 56:780–788.  https://doi.org/10.1111/epi.12955CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Kosior RK, Lauzon ML, Frayne R, Federico P (2009) Single-subject voxel-based relaxometry for clinical assessment of temporal lobe epilepsy. Epilepsy Res 86:23–31.  https://doi.org/10.1016/j.eplepsyres.2009.04.001CrossRefPubMedGoogle Scholar
  23. 23.
    Lim Y-M, Cho Y-W, Shamim S, Solomon J, Birn R, Luh WM, Gaillard WD, Ritzl EK, Theodore WH (2008) Usefulness of pulsed arterial spin labeling MR imaging in mesial temporal lobe epilepsy. Epilepsy Res 82:183–189CrossRefGoogle Scholar
  24. 24.
    Pendse N, Wissmeyer M, Altrichter S, Vargas M, Delavelle J, Viallon M, Federspiel A, Seeck M, Schaller K, Lövblad KO (2010) Interictal arterial spin-labeling MRI perfusion in intractable epilepsy. J Neuroradiol 37:60–63.  https://doi.org/10.1016/j.neurad.2009.05.006CrossRefPubMedGoogle Scholar
  25. 25.
    Wolf RL, Alsop DC, Levy-Reis I, Meyer PT, Maldjian JA, Gonzalez-Atavales J, French JA, Alavi A, Detre JA (2001) Detection of mesial temporal lobe hypoperfusion in patients with temporal lobe epilepsy by use of arterial spin labeled perfusion MR imaging. AJNR Am J Neuroradiol 22:1334–1341PubMedGoogle Scholar
  26. 26.
    Eberhardt KE, Stefan H, Buchfelder M, Pauli E, Hopp P, Huk W, Tomandl BF (2000) The significance of bilateral CSI changes for the postoperative outcome in temporal lobe epilepsy. J Comput Assist Tomogr 24:919–926CrossRefGoogle Scholar
  27. 27.
    Blumcke I, Vinters HV, Armstrong D (2009) Malformations of cortical development and epilepsies: neuropathological findings with emphasis on focal cortical dysplasia. Epileptic Disord 11:181–193PubMedGoogle Scholar
  28. 28.
    Colombo N, Salamon N, Raybaud C (2009) Imaging of malformations of cortical development. Epileptic Disord 11:194–205PubMedGoogle Scholar
  29. 29.
    Mellerio C, Labeyrie M-A, Chassoux F, Daumas-Duport C, Landre E, Turak B, Roux F-X, Meder J-F, Devaux B, Oppenheim C (2012) Optimizing MR imaging detection of type 2 focal cortical dysplasia: best criteria for clinical practice. Am J Neuroradiol 33:1932–1938CrossRefGoogle Scholar
  30. 30.
    Widdess-Walsh P, Kellinghaus C, Jeha L (2005) Electro-clinical and imaging characteristics of focal cortical dysplasia: correlation with pathological subtypes. Epilepsy Res 67:25CrossRefGoogle Scholar
  31. 31.
    Wang DD, Deans AE, Barkovich AJ, Tihan T, Barbaro NM, Garcia PA, Chang EF (2013) Transmantle sign in focal cortical dysplasia: a unique radiological entity with excellent prognosis for seizure control: clinical article. J Neurosurg 118:337–344.  https://doi.org/10.3171/2012.10.JNS12119CrossRefPubMedGoogle Scholar
  32. 32.
    Besson P, Andermann F, Dubeau F, Bernasconi A (2008) Small focal cortical dysplasia lesions are located at the bottom of a deep sulcus. Brain 131:3246–3255.  https://doi.org/10.1093/brain/awn224CrossRefPubMedGoogle Scholar
  33. 33.
    Mellerio C, Roca P, Chassoux F, Danière F, Cachia A, Lion S, Naggara O, Devaux B, Meder J-F, Oppenheim C (2015) The power button sign: a newly described central Sulcal pattern on surface rendering MR images of type 2 focal cortical dysplasia. Radiology 274:500–507.  https://doi.org/10.1148/radiol.14140773CrossRefPubMedGoogle Scholar
  34. 34.
    Colombo N, Tassi L, Deleo F, Citterio A, Bramerio M, Mai R, Sartori I, Cardinale F, Russo GL, Spreafico R (2012) Focal cortical dysplasia type IIa and IIb: MRI aspects in 118 cases proven by histopathology. Neuroradiology 54:1065–1077.  https://doi.org/10.1007/s00234-012-1049-1CrossRefPubMedGoogle Scholar
  35. 35.
    Kim DW, Lee SK, Chu K, Park KI, Lee SY, Lee CH, Chung CK, Choe G, Kim JY (2009) Predictors of surgical outcome and pathologic considerations in focal cortical dysplasia. Neurology 72:211–216CrossRefGoogle Scholar
  36. 36.
    Chapman K, Wyllie E, Najm I, Ruggieri P, Bingaman W, Lüders J, Kotagal P, Lachhwani D, Dinner D, Lüders HO (2005) Seizure outcome after epilepsy surgery in patients with normal preoperative MRI. J Neurol Neurosurg Psychiatry 76:710–713CrossRefGoogle Scholar
  37. 37.
    So EL, Lee RW (2014) Epilepsy surgery in MRI-negative epilepsies. Curr Opin Neurol 27:206–212CrossRefGoogle Scholar
  38. 38.
    Kim H, Harrison A, Kankirawatana P, Rozzelle C, Blount J, Torgerson C, Knowlton R (2013) Major white matter fiber changes in medically intractable neocortical epilepsy in children: a diffusion tensor imaging study. Epilepsy Res 103:211–220CrossRefGoogle Scholar
  39. 39.
    Altrichter S, Pendse N, Wissmeyer M, Jägersberg M, Federspiel A, Viallon M, Seeck M, Lövblad K-O (2009) Arterial spin-labeling demonstrates ictal cortical hyperperfusion in epilepsy secondary to hemimegalencephaly. J Neuroradiol 36:303–305.  https://doi.org/10.1016/j.neurad.2009.04.001CrossRefPubMedGoogle Scholar
  40. 40.
    Storti SF, Galazzo IB, Del Felice A, Pizzini FB, Arcaro C, Formaggio E, Mai R, Manganotti P (2014) Combining ESI, ASL and PET for quantitative assessment of drug-resistant focal epilepsy. NeuroImage 102:49–59.  https://doi.org/10.1016/j.neuroimage.2013.06.028CrossRefGoogle Scholar
  41. 41.
    Colliot O, Bernasconi N, Khalili N, Antel SB, Naessens V, Bernasconi A (2006) Individual voxel-based analysis of gray matter in focal cortical dysplasia. NeuroImage 29:162–171CrossRefGoogle Scholar
  42. 42.
    Wagner J, Weber B, Urbach H, Elger CE, Huppertz H-J (2011) Morphometric MRI analysis improves detection of focal cortical dysplasia type II. Brain 134:2844–2854CrossRefGoogle Scholar
  43. 43.
    Rugg-Gunn FJ, Boulby PA, Symms MR, Barker GJ, Duncan JS (2005) Whole-brain T2 mapping demonstrates occult abnormalities in focal epilepsy. Neurology 64:318–325CrossRefGoogle Scholar
  44. 44.
    Roca P, Mellerio C, Chassoux F, Rivière D, Cachia A, Charron S, Lion S, Mangin J-F, Devaux B, Meder J-F, Oppenheim C (2015) Sulcus-based MR analysis of focal cortical dysplasia located in the central region. PLoS One 10:e0122252.  https://doi.org/10.1371/journal.pone.0122252CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Devaux B, Chassoux F, Landré E, Turak B, Laurent A, Zanello M, Mellerio C, Varlet P (2017) Surgery for dysembryoplastic neuroepithelial tumors and gangliogliomas in eloquent areas. Functional results and seizure control. Neurochirurgie 63:227–234.  https://doi.org/10.1016/j.neuchi.2016.10.009CrossRefPubMedGoogle Scholar
  46. 46.
    Zhang D, Henning TD, Zou L-G, Hu L-B, Wen L, Feng X-Y, Dai S-H, Wang W-X, Sun Q-R, Zhang Z-G (2008) Intracranial ganglioglioma: clinicopathological and MRI findings in 16 patients. Clin Radiol 63:80–91.  https://doi.org/10.1016/j.crad.2007.06.010CrossRefPubMedGoogle Scholar
  47. 47.
    Kikuchi T, Kumabe T, Higano S, Watanabe M, Tominaga T (2009) Minimum apparent diffusion coefficient for the differential diagnosis of ganglioglioma. Neurol Res 31:1102–1107.  https://doi.org/10.1179/174313209X382539CrossRefPubMedGoogle Scholar
  48. 48.
    Law M, Meltzer DE, Wetzel SG, Yang S, Knopp EA, Golfinos J, Johnson G (2004) Conventional MR imaging with simultaneous measurements of cerebral blood volume and vascular permeability in ganglioglioma. Magn Reson Imaging 22:599–606.  https://doi.org/10.1016/j.mri.2004.01.031CrossRefPubMedGoogle Scholar
  49. 49.
    Chassoux F, Rodrigo S, Mellerio C, Landré E, Miquel C, Turak B, Laschet J, Meder J-F, Roux F-X, Daumas-Duport C (2012) Dysembryoplastic neuroepithelial tumors an MRI-based scheme for epilepsy surgery. Neurology 79:1699–1707CrossRefGoogle Scholar
  50. 50.
    Campos AR, Clusmann H, von Lehe M, Niehusmann P, Becker AJ, Schramm J, Urbach H (2009) Simple and complex dysembryoplastic neuroepithelial tumors (DNT) variants: clinical profile, MRI, and histopathology. Neuroradiology 51:433–443.  https://doi.org/10.1007/s00234-009-0511-1CrossRefPubMedGoogle Scholar
  51. 51.
    Chassoux F, Daumas-Duport C (2013) Dysembryoplastic neuroepithelial tumors: where are we now? Epilepsia 54:129–134.  https://doi.org/10.1111/epi.12457CrossRefPubMedGoogle Scholar
  52. 52.
    Bulakbasi N, Kocaoglu M, Sanal TH, Tayfun C (2007) Dysembryoplastic neuroepithelial tumors: proton MR spectroscopy, diffusion and perfusion characteristics. Neuroradiology 49:805–812.  https://doi.org/10.1007/s00234-007-0263-8CrossRefPubMedGoogle Scholar
  53. 53.
    Barkovich AJ, Guerrini R, Kuzniecky RI, Jackson GD, Dobyns WB (2012) A developmental and genetic classification for malformations of cortical development: update 2012. Brain 135:1348–1369.  https://doi.org/10.1093/brain/aws019CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Woodward KE, Gaxiola-Valdez I, Mainprize D, Grossi M, Goodyear BG, Federico P (2014) Recent seizure activity alters motor organization in frontal lobe epilepsy as revealed by task-based fMRI. Epilepsy Res 108:1286–1298CrossRefGoogle Scholar
  55. 55.
    Janszky J, Ebner A, Kruse B, Mertens M, Jokeit H, Seitz RJ, Witte OW, Tuxhorn I, Woermann FG (2003) Functional organization of the brain with malformations of cortical development. Ann Neurol 53:759–767.  https://doi.org/10.1002/ana.10545CrossRefPubMedGoogle Scholar
  56. 56.
    Nikolova S, Bartha R, Parrent AG, Steven DA, Diosy D, Burneo JG (2015) Functional MRI of neuronal activation in epilepsy patients with malformations of cortical development. Epilepsy Res 116:1–7.  https://doi.org/10.1016/j.eplepsyres.2015.06.012CrossRefPubMedGoogle Scholar
  57. 57.
    Vitali P, Minati L, D’Incerti L, Maccagnano E, Mavilio N, Capello D, Dylgjeri S, Rodriguez G, Franceschetti S, Spreafico R, Villani F (2008) Functional MRI in malformations of cortical development: activation of dysplastic tissue and functional reorganization. J Neuroimaging 18:296–305.  https://doi.org/10.1111/j.1552-6569.2007.00164.xCrossRefPubMedGoogle Scholar
  58. 58.
    Achten E, Jackson GD, Cameron JA, Abbott DF, Stella DL, Fabinyi GCA (1999) Presurgical evaluation of the motor hand area with functional MR imaging in patients with tumors and dysplastic lesions. Radiology 210:529–538.  https://doi.org/10.1148/radiology.210.2.r99ja31529CrossRefPubMedGoogle Scholar
  59. 59.
    Lenge M, Barba C, Montanaro D, Aghakhanyan G, Frijia F, Guerrini R (2018) Relationships between morphologic and functional patterns in the polymicrogyric cortex. Cereb Cortex 28:1076–1086.  https://doi.org/10.1093/cercor/bhx036CrossRefGoogle Scholar
  60. 60.
    Christodoulou JA, Barnard ME, Del Tufo SN, Katzir T, Whitfield-Gabrieli S, Gabrieli JD, Chang BS (2013) Integration of gray matter nodules into functional cortical circuits in periventricular heterotopia. Epilepsy Behav 29:400–406CrossRefGoogle Scholar
  61. 61.
    Dumoulin SO, Jirsch JD, Bernasconi A (2007) Functional organization of human visual cortex in occipital polymicrogyria. Hum Brain Mapp 28:1302–1312CrossRefGoogle Scholar
  62. 62.
    Binder JR (2011) Functional MRI is a valid noninvasive alternative to Wada testing. Epilepsy Behav 20:214–222CrossRefGoogle Scholar
  63. 63.
    Sabbah P, Chassoux F, Leveque C, Landre E, Baudoin-Chial S, Devaux B, Mann M, Godon-Hardy S, Nioche C, Aït-Ameur A, Sarrazin JL, Chodkiewicz JP, Cordoliani YS (2003) Functional MR imaging in assessment of language dominance in epileptic patients. NeuroImage 18:460–467.  https://doi.org/10.1016/S1053-8119(03)00025-9CrossRefPubMedGoogle Scholar
  64. 64.
    Thivard L, Hombrouck J, du Montcel ST, Delmaire C, Cohen L, Samson S, Dupont S, Chiras J, Baulac M, Lehéricy S (2005) Productive and perceptive language reorganization in temporal lobe epilepsy. NeuroImage 24:841–851CrossRefGoogle Scholar
  65. 65.
    Wang A, Peters TM, de Ribaupierre S, Mirsattari SM (2012) Functional magnetic resonance imaging for language mapping in temporal lobe epilepsy. Epilepsy Res Treat 2012:198183PubMedPubMedCentralGoogle Scholar
  66. 66.
    Benke T, Köylü B, Visani P, Karner E, Brenneis C, Bartha L, Trinka E, Trieb T, Felber S, Bauer G et al (2006) Language lateralization in temporal lobe epilepsy: a comparison between fMRI and the Wada test. Epilepsia 47:1308–1319CrossRefGoogle Scholar
  67. 67.
    Janecek JK, Swanson SJ, Sabsevitz DS, Hammeke TA, Raghavan M, E Rozman M, Binder JR (2013) Language lateralization by fMRI and Wada testing in 229 patients with epilepsy: rates and predictors of discordance. Epilepsia 54:314–322.  https://doi.org/10.1111/epi.12068CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Fernandez G, Specht K, Weis S, Tendolkar I, Reuber M, Fell J, Klaver P, Ruhlmann J, Reul J, Elger CE (2003) Intrasubject reproducibility of presurgical language lateralization and mapping using fMRI. Neurology 60:969–975CrossRefGoogle Scholar
  69. 69.
    Berl MM, Zimmaro LA, Khan OI, Dustin I, Ritzl E, Duke ES, Sepeta LN, Sato S, Theodore WH, Gaillard WD (2014) Characterization of atypical language activation patterns in focal epilepsy. Ann Neurol 75:33–42CrossRefGoogle Scholar
  70. 70.
    Duke ES, Tesfaye M, Berl MM, Walker JE, Ritzl EK, Fasano RE, Conry JA, Pearl PL, Sato S, Theodore WH et al (2012) The effect of seizure focus on regional language processing areas. Epilepsia 53:1044–1050CrossRefGoogle Scholar
  71. 71.
    Jensen EJ, Hargreaves IS, Pexman PM, Bass A, Goodyear BG, Federico P (2011) Abnormalities of lexical and semantic processing in left temporal lobe epilepsy: an fMRI study. Epilepsia 52:2013–2021CrossRefGoogle Scholar
  72. 72.
    Rosazza C, Ghielmetti F, Minati L, Vitali P, Giovagnoli AR, Deleo F, Didato G, Parente A, Marras C, Bruzzone MG et al (2013) Preoperative language lateralization in temporal lobe epilepsy (TLE) predicts peri-ictal, pre-and post-operative language performance: an fMRI study. NeuroImage Clin 3:73–83CrossRefGoogle Scholar
  73. 73.
    Austermuehle A, Cocjin J, Reynolds R, Agrawal S, Sepeta L, Gaillard WD, Zaghloul KA, Inati S, Theodore WH (2017) Language functional MRI and direct cortical stimulation in epilepsy preoperative planning. Ann Neurol 81:526–537.  https://doi.org/10.1002/ana.24899CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    de Vanssay-Maigne A, Noulhiane M, Devauchelle AD, Rodrigo S, Baudoin-Chial S, Meder JF, Oppenheim C, Chiron C, Chassoux F (2011) Modulation of encoding and retrieval by recollection and familiarity: mapping the medial temporal lobe networks. NeuroImage 58:1131–1138CrossRefGoogle Scholar
  75. 75.
    Towgood K, Barker GJ, Caceres A, Crum WR, Elwes RDC, Costafreda SG, Mehta MA, Morris RG, von Oertzen TJ, Richardson MP (2015) Bringing memory fMRI to the clinic: comparison of seven memory fMRI protocols in temporal lobe epilepsy. Hum Brain Mapp 36:1595–1608.  https://doi.org/10.1002/hbm.22726CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Bonelli SB, Powell RHW, Yogarajah M, Samson RS, Symms MR, Thompson PJ, Koepp MJ, Duncan JS (2010) Imaging memory in temporal lobe epilepsy: predicting the effects of temporal lobe resection. Brain 133:1186–1199.  https://doi.org/10.1093/brain/awq006CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Duncan JS, Winston GP, Koepp MJ, Ourselin S (2016) Brain imaging in the assessment for epilepsy surgery. Lancet Neurol 15:420–433CrossRefGoogle Scholar
  78. 78.
    Dupont S, Duron E, Samson S, Denos M, Volle E, Delmaire C, Navarro V, Chiras J, Lehéricy S, Samson Y et al (2010) Functional MR imaging or Wada test: which is the better predictor of individual postoperative memory outcome? 1. Radiology 255:128–134CrossRefGoogle Scholar
  79. 79.
    Sidhu MK, Stretton J, Winston GP, Symms M, Thompson PJ, Koepp MJ, Duncan JS (2015) Memory fMRI predicts verbal memory decline after anterior temporal lobe resection. Neurology 84:1512–1519.  https://doi.org/10.1212/WNL.0000000000001461CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Binder JR, Sabsevitz DS, Swanson SJ, Hammeke TA, Raghavan M, Mueller WM (2008) Use of preoperative functional MRI to predict verbal memory decline after temporal lobe epilepsy surgery. Epilepsia 49:1377–1394CrossRefGoogle Scholar
  81. 81.
    Sidhu MK, Stretton J, Winston GP, Bonelli S, Centeno M, Vollmar C, Symms M, Thompson PJ, Koepp MJ, Duncan JS (2013) A functional magnetic resonance imaging study mapping the episodic memory encoding network in temporal lobe epilepsy. Brain 136:1868–1888CrossRefGoogle Scholar
  82. 82.
    Englot DJ, Konrad PE, Morgan VL (2016) Regional and global connectivity disturbances in focal epilepsy, related neurocognitive sequelae, and potential mechanistic underpinnings. Epilepsia 57:1546–1557CrossRefGoogle Scholar
  83. 83.
    Yang Z, Choupan J, Reutens D, Hocking J (2015) Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning. Front Neurol 6:184CrossRefGoogle Scholar
  84. 84.
    Haneef Z, Lenartowicz A, Yeh HJ, Levin HS, Engel J, Stern JM (2014) Functional connectivity of hippocampal networks in temporal lobe epilepsy. Epilepsia 55:137–145CrossRefGoogle Scholar
  85. 85.
    Maccotta L, He BJ, Snyder AZ, Eisenman LN, Benzinger TL, Ances BM, Corbetta M, Hogan RE (2013) Impaired and facilitated functional networks in temporal lobe epilepsy. Neuroimage Clin 2:862–872.  https://doi.org/10.1016/j.nicl.2013.06.011CrossRefPubMedPubMedCentralGoogle Scholar
  86. 86.
    Luo C, An D, Yao D, Gotman J (2014) Patient-specific connectivity pattern of epileptic network in frontal lobe epilepsy. Neuroimage Clin 4:668–675.  https://doi.org/10.1016/j.nicl.2014.04.006CrossRefPubMedPubMedCentralGoogle Scholar
  87. 87.
    Pedersen M, Curwood EK, Vaughan DN, Omidvarnia AH, Jackson GD (2016) Abnormal brain areas common to the focal epilepsies: multivariate pattern analysis of fMRI. Brain Connect 6:208–215.  https://doi.org/10.1089/brain.2015.0367CrossRefPubMedGoogle Scholar
  88. 88.
    Englot DJ, Hinkley LB, Kort NS, Imber BS, Mizuiri D, Honma SM, Findlay AM, Garrett C, Cheung PL, Mantle M, Tarapore PE, Knowlton RC, Chang EF, Kirsch HE, Nagarajan SS (2015) Global and regional functional connectivity maps of neural oscillations in focal epilepsy. Brain 138:2249–2262.  https://doi.org/10.1093/brain/awv130CrossRefPubMedPubMedCentralGoogle Scholar
  89. 89.
    Doucet GE, Rider R, Taylor N, Skidmore C, Sharan A, Sperling M, Tracy JI (2015) Presurgery resting-state local graph-theory measures predict neurocognitive outcomes after brain surgery in temporal lobe epilepsy. Epilepsia 56:517–526.  https://doi.org/10.1111/epi.12936CrossRefPubMedGoogle Scholar
  90. 90.
    Ohue S, Kohno S, Inoue A, Yamashita D, Harada H, Kumon Y, Kikuchi K, Miki H, Ohnishi T (2012) Accuracy of diffusion tensor magnetic resonance imaging-based tractography for surgery of gliomas near the pyramidal tract: a significant correlation between subcortical electrical stimulation and postoperative tractography. Neurosurgery 70:283–294.  https://doi.org/10.1227/NEU.0b013e31823020e6CrossRefPubMedGoogle Scholar
  91. 91.
    Jeong J-W, Asano E, Juhász C, Chugani HT (2014) Quantification of primary motor pathways using diffusion MRI tractography and its application to predict postoperative motor deficits in children with focal epilepsy. Hum Brain Mapp 35:3216–3226CrossRefGoogle Scholar
  92. 92.
    Rodrigo S, Oppenheim C, Chassoux F, Hodel J, De Vanssay A, Baudoin-Chial S, Devaux B, Meder J-F (2008) Language lateralization in temporal lobe epilepsy using functional MRI and probabilistic tractography. Epilepsia 49:1367–1376.  https://doi.org/10.1111/j.1528-1167.2008.01607.xCrossRefPubMedGoogle Scholar
  93. 94.
    Winston GP, Daga P, Stretton J, Modat M, Symms MR, McEvoy AW, Ourselin S, Duncan JS (2012) Optic radiation tractography and vision in anterior temporal lobe resection. Ann Neurol 71:334–341.  https://doi.org/10.1002/ana.22619CrossRefPubMedPubMedCentralGoogle Scholar
  94. 93.
    Yogarajah M, Focke NK, Bonelli S, Cercignani M, Acheson J, Parker GJM, Alexander DC, McEvoy AW, Symms MR, Koepp MJ et al (2009) Defining Meyer’s loop–temporal lobe resections, visual field deficits and diffusion tensor tractography. Brain 132:1656–1668CrossRefGoogle Scholar
  95. 95.
    Piper RJ, Yoong MM, Kandasamy J, Chin RF (2014) Application of diffusion tensor imaging and tractography of the optic radiation in anterior temporal lobe resection for epilepsy: a systematic review. Clin Neurol Neurosurg 124:59–65CrossRefGoogle Scholar

via Epilepsy Imaging | SpringerLink

, , , , , , , , , ,

Leave a comment

[Abstract] Big data sharing and analysis to advance research in post-traumatic epilepsy – Review

Highlights

  • We have created the infrastructure for a centralized data repository for multi-modal data.
  • Innovative image and electrophysiology processing methods have been applied.
  • Novel analytic tools are described to study epileptogenesis after traumatic brain injury.

Abstract

We describe the infrastructure and functionality for a centralized preclinical and clinical data repository and analytic platform to support importing heterogeneous multi-modal data, automatically and manually linking data across modalities and sites, and searching content. We have developed and applied innovative image and electrophysiology processing methods to identify candidate biomarkers from MRI, EEG, and multi-modal data. Based on heterogeneous biomarkers, we present novel analytic tools designed to study epileptogenesis in animal model and human with the goal of tracking the probability of developing epilepsy over time.

 

via Big data sharing and analysis to advance research in post-traumatic epilepsy

, , , , , ,

Leave a comment

[WEB SITE] One Woman, 10 MRI Scans, 10 Different Diagnoses

Cultura RM Exclusive/Sigrid Gombert/Getty Images

An MRI scan is a lot more like a Rorschach test to your radiologist than you’d probably like to imagine.

That’s the summary of a study recently published in The Spine Journal. Researchers sent a 63-year-old woman with lower back pain and a specific set of other symptoms to MRI appointments with ten different radiologists. The radiologists collectively made 49 distinct findings. Zero, however, made it into all ten diagnoses, and only one was reported in nine out of the ten.

Even more alarming: The average report contained between nine and 16 errors, both false-positives and missed diagnoses (which were later found by experts in her specific spinal problem, the comparison points for the study’s researchers). Overall, the study found “poor overall agreement” in radiologists’ opinions of the woman’s condition.

The study differs from past ones in which radiologists viewed MRI results in a research setting and made diagnoses, says co-author Daniel Elgort, vice president of healthcare data analytics and research at the Spreemo Quality Research Institute. “[In those studies] they knew they were being studied, so they made a more careful diagnosis.” Radiologists seeing an average patient are apparently less thorough.

The point of the exercise was to disprove a common misconception among medical consumers. “There is this notion that there are no differences in quality in radiology services,” Elgort says, “that [one] should always decide by price and convenience.”

Radiologists, however, are not the oil change technicians or dry cleaners of the medical world— professions where there is not much difference in performance once one achieves professional-level competency. Instead, the results suggest that some radiology offices are in fact better than others.

While they do not have enough data to prove it, Elgort theorizes that the difference is in cost. Cheaper radiology offices probably employ less experienced staff, use older equipment, cram in appointments, and cut other corners.

“The takeaway should not be, ‘go get the most expensive MRI possible,'” Elgort says. “Healthcare in general isn’t a necessarily a correlation between price and quality. It should definitely be that not every healthcare provider is equally suited to give you the most accurate diagnosis.” He added that patients should seek out radiology labs with specialists in their specific issues.

As for where they found a middle-aged woman willing to get MRI after MRI for weeks, Elgort says they recruited the subject from contacts at the Hospital for Special Surgery in New York City, adding, “She’s a former nurse, so she knows the value of this kind of science.

 

via One Woman, 10 MRI Scans, 10 Different Diagnoses – Tonic

, , , , , ,

Leave a comment

[WEB SITE] Can MRI Brain Scans Help Us Understand Epilepsy?

epilepsy

A massive meta-analysis of global MRI imaging data on epilepsy patients seeks to clarify a complicated and mysterious neurological disorder.

Epilepsy is a neurological disorder characterized by seizures, which can vary from mild and almost undetectable to severe, featuring vigorous shaking. Almost 40 million people worldwide are affected by epilepsy. Epileptic seizures are caused by an abnormally high level of activity in nerve cells in the brain. A small number of cases have been tied to a genetic defect, and major trauma to the brain (such as an injury or stroke) can also induce seizures. However, for the majority of cases, the underlying cause of epilepsy is not known. In many instances, epilepsy can be treated with the use of anti-convulsant medication. Some people will experience an improvement in their symptoms to the point of no longer requiring medication, while others will not respond to medication at all. The variability of the disease with regards to physiology and progression makes it difficult to accurately diagnose.

How Does Epilepsy Affect the Brain?

There are multiple types of epilepsies, some more common than others, which affect different parts of the brain cortex. The disorder has been studied by using techniques such as magnetic resonance imaging (MRI), and analyses of brain tissue. The latter requires post-mortem collection of tissue, as biopsies are not routinely performed on living patients’ brains. A brain scan via MRI imaging can provide detail about pathological markers of epilepsy, but the massive amount of data collected worldwide by imaging has not yet been consolidated and analyzed in a robust manner. Gaining an understanding of distinct or shared disease markers for different forms of epilepsy could help clinicians identify targets for therapy and increase the personalization of treatment.

The ENIGMA Study

A recent study published in the journal BRAIN represents the largest neuroimaging analysis of epilepsy conducted to date.This study, called ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis)summarizes contributions from 24 research centers across 14 countries in Europe, North and South America, Asia, and Australia. Similar wide-ranging studies have revealed structural brain abnormalities in other neurological conditions such as schizophrenia, depression, and obsessive-compulsive disorder. The researchers had several goals in putting this meta-analysis together:

  1. To look at distinct types of epilepsy to see whether they share similar structural abnormalities of the brain.
  2. To analyze a well-known specific type of epilepsy, mesial temporal lobe epilepsy (MTLE) for differences between people afflicted with this disorder on different sides of the brain.
  3. To analyze idiopathic generalized epilepsies (IGE), which are thought to have a genetic component to their cause and aren’t often detectable via MRI.

The researchers compiled imaging data from 2,149 people with epilepsy and 1,727 healthy control subjects. The large sample size allowed them to perform high-powered statistical analysis of the data.

For analysis (1), the results showed that a diverse array of epilepsies showed common structural anomalies across several different regions of the brain. This suggested that distinct disease types share a common neuroanatomical signature.

For analysis (2), they found that people with mesial temporal lobe epilepsy on the right side of the hippocampus did not experience damage to the left side, and vice-versa. However, somewhat unexpectedly, they saw that damage extended to areas outside the hippocampus, suggesting that even a region-specific disorder like mesial temporal lobe epilepsy may be a network disease.

In analysis (3), the researchers found that contrary to many reports of a “normal” MRI for patients with idiopathic generalized epilepsy, several structural irregularities were observable over a large number of samples. These included reduced brain volume and thickness in several regions.

One Step Closer to Understanding Epilepsy

The authors noted some limitations to their study, such as the fact that all results were derived from cross-sectional data, meaning that it was not possible to determine whether certain features were the cause of severe brain damage at one point in time, or whether they were the product of progressive trauma. In addition, this study could not account for the possible contribution of other factors, such as medications, seizure type and frequency, and disease severity. However, this wide-scale meta-analysis represents an important step towards understanding how different types of epilepsies affect the brain, and hopefully can lead to more personalized and effective medical interventions.

Written by Adriano Vissa, PhD

Reference: Whelan CD, et al. Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study. Brain. 2018; 141(2):391-408

 

via Can MRI Brain Scans Help Us Understand Epilepsy? – Medical News Bulletin | Health News and Medical Research

, , , , , , , , , ,

Leave a comment

[WEB SITE] MRI brain scans may help clinicians decide between CBT and drug treatment for depression

Researchers from Emory University have found that specific patterns of activity on brain scans may help clinicians identify whether psychotherapy or antidepressant medication is more likely to help individual patients recover from depression.

The study, called PReDICT, randomly assigned patients to 12 weeks of treatment with one of two antidepressant medications or with cognitive behavioral therapy (CBT). At the start of the study, patients underwent a functional MRI brain scan, which was then analyzed to see whether the outcome from CBT or medication depended on the state of the brain prior to starting treatment. The study results are published as two papers in the March 24 online issue of the American Journal of Psychiatry.

The MRI scans identified that the degree of functional connectivity between an important emotion processing center (the subcallosal cingulate cortex) and three other areas of the brain was associated with the treatment outcomes. Specifically, patients with positive connectivity between the brain regions were significantly more likely to achieve remission with CBT, whereas patients with negative or absent connectivity were more likely to remit with antidepressant medication.

“All depressions are not equal and like different types of cancer, different types of depression will require specific treatments. Using these scans, we may be able to match a patient to the treatment that is most likely to help them, while avoiding treatments unlikely to provide benefit,” says Helen Mayberg, MD, who led the imaging study. Mayberg is a Professor of Psychiatry, Neurology and Radiology and the Dorothy C. Fuqua Chair in Psychiatric Imaging and Therapeutics at Emory University School of Medicine.

Mayberg and co- investigators Boadie Dunlop, MD, Director of the Emory Mood and Anxiety Disorders Program, and W. Edward Craighead, PhD, J. Rex Fuqua Professor of Psychiatry and Behavioral Sciences, sought to develop methods for a more personalized approach to treating depression.

Current treatment guidelines for major depression recommend that a patient’s preference for psychotherapy or medication be considered in selecting the initial treatment approach. However, in the PReDICT study patients’ preferences were only weakly associated with outcomes; preferences predicted treatment drop-out but not improvement. These results are consistent with prior studies, suggesting that achieving personalized treatment for depressed patients will depend more on identifying specific biological characteristics in patients rather than relying on their symptoms or treatment preferences. The results from PReDICT suggest that brain scans may offer the best approach for personalizing treatment going forward.

In recruiting 344 patients for the study from across the metro Atlanta area, researchers were able to convene a more diverse group of patients than other previous studies, with roughly half of the participants self-identified as African-American or Hispanic.

“Our diverse sample demonstrated that the evidence-based psychotherapy and medication treatments recommended as first line treatments for depression can be extended with confidence beyond a white, non-Hispanic population,” says Dunlop.

“Ultimately our studies show that clinical characteristics, such as age, gender, etc., and even patients’ preferences regarding treatment, are not as good at identifying likely treatment outcomes as the brain measurement,” adds Mayberg.

Source: MRI brain scans may help clinicians decide between CBT and drug treatment for depression

, , , , , ,

Leave a comment

[WEB SITE] Understanding the Human Brain – Neuroscience News

Functional magnetic resonance images reflect input signals of nerve cells.

The development of magnetic resonance imaging (MRI) is a success story for basic research. Today medical diagnostics would be inconceivable without it. But the research took time to reach fruition: it has been nearly half a century since physicists first began their investigations that ultimately led to what became known as nuclear magnetic resonance. In 2001, Nikos K. Logothetis and his colleagues at the Max Planck Institute for Biological Cybernetics in Tübingen devised a new methodological approach that greatly deepened our understanding of the principles of functional MRI.

The great advantage of functional magnetic resonance imaging (fMRI) is that it requires no major interventions in the body. In fMRI, the human body is exposed to the action of electromagnetic waves. As far as we know today, the process is completely harmless, despite the fact that fMRI equipment generates magnetic fields that are about a million times stronger than the natural magnetic field of the earth.

The physical phenomenon underlying fMRI is known as nuclear magnetic resonance, and the path to its discovery was paved with several Nobel prizes. The story begins in the first half of the 20th century with the description of the properties of atoms. The idea of using nuclear magnetic resonance as a diagnostic tool was mooted as early as the 1950s. But the method had to be refined before finally being realised in the form of magnetic resonance imaging.

Today, MRI not only produces images of the inside of our bodies; it also provides information on the functional state of certain tissues. The breakthrough for fMRI came in the 1980s when researchers discovered that MRI can also be used to detect changes in the oxygen saturation of blood, a principle known as BOLD (blood oxygen level dependent) imaging. There is a 20 percent difference between the magnetic sensitivity of oxygenated arterial blood and that of deoxygenated venous blood. Unlike oxygenated haemoglobin, deoxygenated haemoglobin amplifies the strength of a magnetic field in its vicinity. This difference can be seen on an MRI image.

Resuscitation of the brain after a 15-minute cardiac arrest in fMRI: The pictorial representation provides information about the degree of damage of the brain as well as a detailed analysis of the recovery curve. The top three rows are examples of successful and the bottom row for an unsuccessful resuscitation. The comparison with the concentration images of ATP, glucose and lactate shows that the MR images are in fact closely related to the biochemical changes. Based on such studies, the course of cerebral infarction and the success of various therapeutic measures can be documented. Credit Max Planck Institute.

fMRI has given us new insights into the brain, especially in neurobiology. However, the initial phase of euphoria was followed by a wave of scepticism among scientists, who questioned how informative the “coloured images” really are. Although fMRI can in fact generate huge volumes of data, there is often a lack of background information or basic understanding to permit a meaningful interpretation. As a result, there is a yawning gap between fMRI measurements of brain activity and findings in animals based on electrophysiological recordings.

This is due mainly to technical considerations: interactions between the strong MRI field and currents being measured at the electrodes made it impossible to apply the two methods simultaneously to bridge the gap between animal experiments and findings in humans.

fMRT shows input signals

In 2001, Nikos Logothetis and his colleagues at the Max Planck Institute for Biological Cybernetics in Tübingen were the first to overcome this barrier. With the help of special electrodes and sophisticated data processing, they showed unambiguously that BOLD fMRI actually does measure changes in the activity of nerve cells. They also discovered that BOLD signals correlate to the arrival and local processing of data in an area of the brain rather than to output signals that are transmitted to other areas of the brain. Their paper was a milestone in our understanding of MRI and has been cited over 2500 times worldwide.

Their novel experimental setup enabled the Tübingen scientists to study various aspects of nerve cell activity and to distinguish between action potentials and local field potentials. Action potentials are electrical signals that originate from single nerve cells or a relatively small group of nerve cells. They are all-or-nothing signals that occur only if the triggering stimulus exceeds a certain threshold. Action potentials therefore reflect output signals. These signals are detected by electrodes located in the immediate vicinity of the nerve cells. By contrast, local field potentials generate slowly varying electrical potentials that reflect signals entering and being processed in a larger group of nerve cells.

Applying these three methods simultaneously, the Max Planck researchers examined the responses to a visual stimulus in the visual cortex of anaesthetized monkeys. Comparison of the measurements showed that fMRI data relate more to local field potentials than to single-cell and multi-unit potentials. This means that changes in blood oxygen saturation are not necessarily associated with output signals from nerve cells; instead, they reflect the arrival and processing of signals received from other areas of the brain.

Another important discovery the Tübingen researchers made was that, because of the large variability of vascular reactions, BOLD fMRI data have a much lower signal-to-noise ratio than electrophysiological recordings. Because of this, conventional statistical analyses of human fMRI data underestimate the extent of activity in the brain. In other words, the absence of an fMRI signal in an area of the brain does not necessarily mean that no information is being processed there. Doctors need to take this into account when interpreting fMRI data.

NOTES ABOUT THIS NEUROIMAGING RESEARCH

Contact: Christina Beck – Max Planck Institute
Source: Max Planck Institute press release
Image Source: The image is credited to Max Planck Institute and is adapted from the press release

Source: Understanding the Human Brain – Neuroscience News

, , , , ,

Leave a comment

[ARTICLE] Comparison of Magnetic Resonance Imaging and Stress Radiographs in the Evaluation of Chronic Lateral Ankle Instability – Full Text

In patients who develop chronic ankle instability, clinicians often obtain magnetic resonance imaging (MRI) as part of the evaluation prior to operative referral. The purpose of this study was to analyze the diagnostic efficacy of MRI in the diagnosis of chronic lateral ankle instability. Our hypothesis was that magnetic resonance imaging would not be a specific diagnostic tool in the evaluation of chronic lateral ankle instability.

A retrospective chart review of 187 consecutive patients (190 ankles) was performed. Inclusion criteria for the study group required a primary complaint of instability that required operative repair or reconstruction, a documented clinical evaluation consistent with instability, stress radiographs, and MRI. Stress radiographs and clinical examinations for the study group and a control group were reviewed independently by both a musculoskeletal radiologist and a board-certified orthopaedic foot and ankle surgeon. Predictive values in terms of sensitivity, specificity, and prevalence were performed. In total, 112 patients (115 ankles) were identified who underwent an operative reconstruction of their lateral ligaments with a history, physical examination, and stress radiographs consistent with lateral ankle instability. A control group was selected consisting of 75 patients seen in the foot and ankle clinic with a diagnosis other than lateral ankle instability. Thirty-seven of the patients in the control group had stress radiographs performed in the clinic to rule out instability as part of their evaluation, and this allowed for an evaluation of the efficacy of stress radiographs in addition to MRI. Statistical analysis was performed using predictive values from sensitivity, specificity, and prevalence.

The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in regards to MRI in the evaluation of patients found to have clinical lateral ankle instability and those who did not had statistical significance. Sensitivity of MRI was 82.6%, specificity was 53.3%, NPV was 66.7%, and PPV was 73%. Since 37 patients in the control group also had stress radiographs, a subanalysis was performed to identify the same values with stress radiographs. Sensitivity, specificity, NPV, and PPV were 66%, 97%, 48%, and 98.7%, respectively. The overall accuracy within this study was 71% for MRI and 74% for stress radiographs.

This study demonstrated that MRI has high sensitivity but low specificity in the evaluation of clinical ankle instability. While MRI has value as a screening tool for concomitant ankle pathology, it should not be considered diagnostic in terms of lateral ankle instability.

Figure 1. In conducting the anterolateral drawer, the examiner applies slight internal rotation force and stabilizes the tibia with one hand while grasping the heel with the other hand and applying anteriorly directed force on the heel.

Download PDF

Continue —> Comparison of Magnetic Resonance Imaging and Stress Radiographs in the Evaluation of Chronic Lateral Ankle Instability – Jan 06, 2017

, , ,

Leave a comment

%d bloggers like this: