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type=\u0022text\/css\u0022 rel=\u0022stylesheet\u0022 href=\u0022\/\/d282kpwvnogo5m.cloudfront.net\/sites\/default\/files\/advagg_css\/css__ce2QY63WIanKyr8eSq7eavr1XQRRmFD6ZSmwpyJi8lM__zXwFqpqmxrZOXXcd_TpBQpjuELbmIP9wBR5UuTDWAO4__YJWWMMdfCJuAFm5cUEp88OsodhO3ZA-2lzRfoBsSlk4.css\u0022 media=\u0022all\u0022 \/\u003E\n\u003Clink rel=\u0027stylesheet\u0027 type=\u0027text\/css\u0027 href=\u0027\/sites\/all\/modules\/contrib\/panels\/plugins\/layouts\/onecol\/onecol.css\u0027 \/\u003E\u003C\/head\u003E\u003Cbody\u003E\u003Cdiv class=\u0022panels-ajax-tab-panel panels-ajax-tab-panel-sageoa-tab-art\u0022\u003E\u003Cdiv class=\u0022panel-display panel-1col clearfix\u0022 \u003E\n  \u003Cdiv class=\u0022panel-panel panel-col\u0022\u003E\n    \u003Cdiv\u003E\u003Cdiv class=\u0022panel-pane pane-highwire-markup\u0022 \u003E\n  \n      \n  \n  \u003Cdiv class=\u0022pane-content\u0022\u003E\n    \u003Cdiv class=\u0022highwire-markup\u0022\u003E\u003Cdiv xmlns=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 id=\u0022content-block-markup\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cdiv class=\u0022article fulltext-view \u0022\u003E\u003Cspan class=\u0022highwire-journal-article-marker-start\u0022\u003E\u003C\/span\u003E\u003Cdiv class=\u0022section abstract\u0022 id=\u0022abstract-1\u0022\u003E\u003Ch2\u003ESummary\u003C\/h2\u003E\n            \u003Cp id=\u0022p-1\u0022\u003EThis article addresses biomarkers in patients with migraine, the association of hypertension with recurrent intracerebral hemorrhage, and genetic research in autism. Regional cortical thickness measurements may be a useful biomarker for migraine. Inadequate blood pressure control increases risk of recurrent intracerebral hemorrhage. Genetic study of autism can be facilitated by identifying convergent molecular pathways.\u003C\/p\u003E\n         \u003C\/div\u003E\u003Cul class=\u0022kwd-group\u0022\u003E\u003Cli class=\u0022kwd\u0022\u003Eautism\u003C\/li\u003E\u003Cli class=\u0022kwd\u0022\u003Ebiomarker\u003C\/li\u003E\u003Cli class=\u0022kwd\u0022\u003Eblood pressure\u003C\/li\u003E\u003Cli class=\u0022kwd\u0022\u003Econvergence\u003C\/li\u003E\u003Cli class=\u0022kwd\u0022\u003Ecortical thickness\u003C\/li\u003E\u003Cli class=\u0022kwd\u0022\u003Egenetics\u003C\/li\u003E\u003Cli class=\u0022kwd\u0022\u003Eintracerebral hemorrhage\u003C\/li\u003E\u003Cli class=\u0022kwd\u0022\u003Emigraine\u003C\/li\u003E\u003Cli class=\u0022kwd\u0022\u003Eneurology clinical trials\u003C\/li\u003E\u003Cli class=\u0022kwd\u0022\u003Eneurology genomics\u003C\/li\u003E\u003C\/ul\u003E\u003Cdiv class=\u0022section\u0022 id=\u0022sec-1\u0022\u003E\n         \n         \u003Cp id=\u0022p-2\u0022\u003EPracticing neurologists are confronted with many challenges in the diagnosis and treatment of neurologic disorders. Among the contemporary clinical issues explored at the American Academy of Neurology (AAN) 2015 Annual Meeting were the neurobiology of pain in adults with migraine, blood pressure (BP) control and the risk of recurrent intracerebral hemorrhage (ICH), and the development of targeted treatments for autism.\u003C\/p\u003E\n      \u003C\/div\u003E\u003Cdiv class=\u0022section\u0022 id=\u0022sec-2\u0022\u003E\n         \u003Ch2 class=\u0022\u0022\u003ECortical Thickness Measurements Promising as a Biomarker for Migraine\u003C\/h2\u003E\n         \u003Cp id=\u0022p-3\u0022\u003EMigraine is typically diagnosed and subclassified according to the International Classification of Headache Disorders (ICHD) symptom-based criteria, which are derived from the opinions of an expert panel. The lack of an objective biomarker makes it difficult to test the validity of the ICHD criteria and optimize the diagnosis. Todd J. Schwedt, MD, Mayo Clinic, Phoenix, Arizona, USA, presented evidence showing that structural imaging might provide an objective biomarker for migraine.\u003C\/p\u003E\n         \u003Cp id=\u0022p-4\u0022\u003EFunctional magnetic resonance imaging (MRI) in patients with migraine has demonstrated hyperexcitability of brain regions that facilitate sensory processing and atypical functional connectivity of sensory processing regions [Schwedt TJ et al. \u003Cem\u003ELancet Neurol\u003C\/em\u003E. 2015]. Structural MRI of cortical volume, surface, and thickness is a practical way to obtain data for a potential biomarker for migraine. A study in healthy participants found correlations between cortical thickness and pain sensitivity [Erpelding N et al. \u003Cem\u003EPain\u003C\/em\u003E. 2012]. Another study found that healthy controls had a significant negative correlation between cortical thickness and pain thresholds, while patients with migraine had a nonsignificant positive correlation [Schwedt TJ, Chong CD. \u003Cem\u003EPLoS One\u003C\/em\u003E. 2014].\u003C\/p\u003E\n         \u003Cp id=\u0022p-5\u0022\u003EDr Schwedt and colleagues conducted a study to identify the brain regions with interregional cortical thickness correlations that differed the most between patients with migraine (n\u2005=\u200564) and healthy controls (n\u2005=\u200539) [Schwedt TJ et al. \u003Cem\u003EPLoS One\u003C\/em\u003E. 2015]. The participants underwent structural 3T MRI imaging. Cortical thickness was measured for 35 regions within each hemisphere, and cortical thickness correlations among these regions were identified. A multivariate model comprising 15 interregional cortical thickness correlations accurately differentiated patients with migraine from controls, with 84.9% accuracy (\u003Ca id=\u0022xref-fig-1-1\u0022 class=\u0022xref-fig\u0022 href=\u0022#F1\u0022\u003EFigure 1\u003C\/a\u003E). The right temporal pole was involved in 13 of the 15 correlations.\u003C\/p\u003E\n         \u003Cdiv id=\u0022F1\u0022 class=\u0022fig pos-float  odd\u0022\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/15\/9\/26\/F1.large.jpg?width=800\u0026amp;height=600\u0026amp;carousel=1\u0022 title=\u0022Henze-Penrose Divergence Between Patients With Migraine and Healthy ControlsValues range from 0.5 (subject groups cannot be separated) to 1 (subject groups are completely separable).Adapted from Schwedt TJ et al. Temporal lobe cortical thickness correlations differentiate the migraine brain from the healthy brain. PLoS One. 2015;10:e0116687. doi:10.1371\/journal.pone.0116687\u0022 class=\u0022fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images-1542055726\u0022 data-figure-caption=\u0022\u0026amp;lt;div xmlns=\u0026amp;quot;http:\/\/www.w3.org\/1999\/xhtml\u0026amp;quot;\u0026amp;gt;Henze-Penrose Divergence Between Patients With Migraine and Healthy ControlsValues range from 0.5 (subject groups cannot be separated) to 1 (subject groups are completely separable).Adapted from Schwedt TJ et al. Temporal lobe cortical thickness correlations differentiate the migraine brain from the healthy brain. \u0026amp;lt;em\u0026amp;gt;PLoS One\u0026amp;lt;\/em\u0026amp;gt;. 2015;10:e0116687. doi:10.1371\/journal.pone.0116687\u0026amp;lt;\/div\u0026amp;gt;\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003E\u003Cimg class=\u0022fragment-image\u0022 alt=\u0022Figure 1.\u0022 src=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/15\/9\/26\/F1.medium.gif\u0022\/\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links inline\u0022\u003E\u003Cli class=\u00220 first\u0022\u003E\u003Ca href=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/15\/9\/26\/F1.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure 1.\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u00221\u0022\u003E\u003Ca href=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/15\/9\/26\/F1.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u00222 last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/16574\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cspan class=\u0022fig-label\u0022\u003EFigure 1.\u003C\/span\u003E \n               \u003Cp id=\u0022p-6\u0022 class=\u0022first-child\u0022\u003EHenze-Penrose Divergence Between Patients With Migraine and Healthy Controls\u003C\/p\u003E\n               \u003Cp id=\u0022p-7\u0022\u003EValues range from 0.5 (subject groups cannot be separated) to 1 (subject groups are completely separable).\u003C\/p\u003E\n               \u003Cp id=\u0022p-8\u0022\u003EAdapted from Schwedt TJ et al. Temporal lobe cortical thickness correlations differentiate the migraine brain from the healthy brain. \u003Cem\u003EPLoS One\u003C\/em\u003E. 2015;10:e0116687. doi:10.1371\/journal.pone.0116687\u003C\/p\u003E\n            \u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\n         \u003Cp id=\u0022p-9\u0022\u003EDr Schwedt concluded that measurements of cortical thickness are promising as a biomarker for migraine. Ongoing studies are assessing combinations of different structural measures, migraine subclassification, differentiating migraine from other headache disorders, and addition of functional data.\u003C\/p\u003E\n      \u003C\/div\u003E\u003Cdiv class=\u0022section\u0022 id=\u0022sec-3\u0022\u003E\n         \u003Ch2 class=\u0022\u0022\u003EHigher Risk of Recurrent ICH With Inadequate BP Control After Lobar and Nonlobar ICH\u003C\/h2\u003E\n         \u003Cp id=\u0022p-10\u0022\u003EICH survivors have a high risk for recurrent ICH of even greater severity. Elevated BP is associated with increased risk of recurrent nonlobar ICH. Available evidence suggests that BP reduction reduces the risk of recurrent ICH but the optimal degree of reduction is not clear. BP reduction in elderly ICH survivors may increase risk of ischemic stroke, ischemic heart disease, renal failure, and syncope. The aim of this study, presented by Christopher D. Anderson, MD, Massachusetts General Hospital, Boston, Massachusetts, USA, was to characterize the relationship between elevated BP after index lobar and nonlobar ICH and risk of ICH recurrence.\u003C\/p\u003E\n         \u003Cp id=\u0022p-11\u0022\u003EThis single-center observational study included 1145 ICH survivors (505 lobar, 640 nonlobar). Ambulatory BP was measured at 3, 6, 9, and 12 months after ICH and every 6 months thereafter. The end point was recurrent ICH (lobar vs nonlobar). During follow-up, recurrent ICH occurred in 102 patients with initial lobar ICH and 42 patients with initial nonlobar ICH. Inadequate BP control was significantly associated with ICH recurrence in lobar ICH (HR, 3.53; 95% CI, 1.65 to 7.54; \u003Cem\u003EP\u003C\/em\u003E\u2005=\u2005.001) and nonlobar ICH (HR, 4.23; 95% CI, 1.02 to 17.52; \u003Cem\u003EP\u003C\/em\u003E\u2005=\u2005.048) survivors. All stages of hypertension, including prehypertension, were significantly (\u003Cem\u003EP\u003C\/em\u003E\u2005\u0026lt;\u2005.05) associated with ICH recurrence in both lobar and nonlobar ICH survivors, except for stage 2 hypertension in nonlobar ICH survivors.\u003C\/p\u003E\n         \u003Cp id=\u0022p-12\u0022\u003EThere was evidence of a dose-response relationship, with a linear association between BP values and recurrent ICH.\u003C\/p\u003E\n         \u003Cp id=\u0022p-13\u0022\u003EElevated BP after both lobar and nonlobar ICH is associated with increased risk of recurrent ICH. If these results are confirmed, current recommendations for BP control after ICH may be inadequate. Clinical trials are needed to determine the optimal degree of BP reduction for prevention of recurrent ICH and minimization of side effects.\u003C\/p\u003E\n      \u003C\/div\u003E\u003Cdiv class=\u0022section\u0022 id=\u0022sec-4\u0022\u003E\n         \u003Ch2 class=\u0022\u0022\u003EConvergent Molecular Pathways May Lead to Targeted Therapies for Autism\u003C\/h2\u003E\n         \u003Cp id=\u0022p-14\u0022\u003EAutism is a neuropsychiatric syndrome characterized by deficits in social communication and interactions and restrictive, repetitive behavior. Daniel H. Geschwind, MD, PhD, University of California, Los Angeles School of Medicine, Los Angeles, California, USA, discussed progress in the past 2 decades of genetic research in autism spectrum disorders (ASDs) and explored the potential for using autism risk genes to develop targeted treatments.\u003C\/p\u003E\n         \u003Cp id=\u0022p-15\u0022\u003ETwo large, open resources for ASD genetics are available. The Autism Genetic Resource Exchange, founded in 1998, includes \u0026gt;\u20051500 families and 6000 individuals. The Simons Simplex Collection comprises 2700 families and \u0026gt;\u200510\u2005000 individuals. Both resources have large-scale genetic and phenotypic data. Twelve major linkage studies (1998-2009) and genome-wide association studies have revealed the genetic heterogeneity of ASDs [Abrahams BS, Geschwind DH. \u003Cem\u003ENat Rev Genet\u003C\/em\u003E. 2008].\u003C\/p\u003E\n         \u003Cp id=\u0022p-16\u0022\u003EMajor advances were made in 2012, said Dr Geschwind, when 4 groups published results of exome sequencing in almost 1000 patients with ASDs. Several dozen mutations were identified, including several observed more than once; however, most appear to increase ASDs risk rather than cause ASDs. The most frequent genes (\u003Cem\u003ECHD8, DYRK1A, GRIN2B, TBR1, PTEN\u003C\/em\u003E, and \u003Cem\u003ETBL1XR1\u003C\/em\u003E) account for \u0026lt;\u20051% of cases. Based on these findings, Dr Geschwind estimated that \u0026gt;\u2005500 genes contribute to ASDs. These results were confirmed in a study on \u0026gt;\u20052500 families [Iossifov I et al. \u003Cem\u003ENature\u003C\/em\u003E. 2014].\u003C\/p\u003E\n         \u003Cp id=\u0022p-17\u0022\u003EMore than 100 genes contributing to ASDs have been identified. Development of targeted treatments for this extremely heterogeneic disorder might depend on using integrative genomics to find points of convergence among these genes at molecular and cellular levels and across brain circuits [Parikshak NN et al. \u003Cem\u003ECell\u003C\/em\u003E. 2013; Berg JM, Geschwind DH. \u003Cem\u003EGenome Biol\u003C\/em\u003E. 2012]. A working model shows how major ASD risk genes might converge and lead to autism (\u003Ca id=\u0022xref-fig-2-1\u0022 class=\u0022xref-fig\u0022 href=\u0022#F2\u0022\u003EFigure 2\u003C\/a\u003E) [Geschwind DH. \u003Cem\u003ETrends Cogn Sci\u003C\/em\u003E. 2011].\u003C\/p\u003E\n         \u003Cdiv id=\u0022F2\u0022 class=\u0022fig pos-float  odd\u0022\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/15\/9\/26\/F2.large.jpg?width=800\u0026amp;height=600\u0026amp;carousel=1\u0022 title=\u0022Levels of Convergence in ASDASD, autism spectrum disorder.Reprinted from Geschwind DH. Genetics of autism spectrum disorders. Trends Cogn Sci. 2011;15:409-416. With permission from Elsevier Ltd.\u0022 class=\u0022fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images-1542055726\u0022 data-figure-caption=\u0022\u0026amp;lt;div xmlns=\u0026amp;quot;http:\/\/www.w3.org\/1999\/xhtml\u0026amp;quot;\u0026amp;gt;Levels of Convergence in ASDASD, autism spectrum disorder.Reprinted from Geschwind DH. Genetics of autism spectrum disorders. \u0026amp;lt;em\u0026amp;gt;Trends Cogn Sci\u0026amp;lt;\/em\u0026amp;gt;. 2011;15:409-416. With permission from Elsevier Ltd.\u0026amp;lt;\/div\u0026amp;gt;\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003E\u003Cimg class=\u0022fragment-image\u0022 alt=\u0022Figure 2.\u0022 src=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/15\/9\/26\/F2.medium.gif\u0022\/\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links inline\u0022\u003E\u003Cli class=\u00220 first\u0022\u003E\u003Ca href=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/15\/9\/26\/F2.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure 2.\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u00221\u0022\u003E\u003Ca href=\u0022http:\/\/d282kpwvnogo5m.cloudfront.net\/content\/spmdc\/15\/9\/26\/F2.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u00222 last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/16575\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022\u003E\u003Cspan class=\u0022fig-label\u0022\u003EFigure 2.\u003C\/span\u003E \n               \u003Cp id=\u0022p-18\u0022 class=\u0022first-child\u0022\u003ELevels of Convergence in ASD\u003C\/p\u003E\n               \u003Cp id=\u0022p-19\u0022\u003EASD, autism spectrum disorder.\u003C\/p\u003E\n               \u003Cp id=\u0022p-20\u0022\u003EReprinted from Geschwind DH. Genetics of autism spectrum disorders. \u003Cem\u003ETrends Cogn Sci\u003C\/em\u003E. 2011;15:409-416. With permission from Elsevier Ltd.\u003C\/p\u003E\n            \u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\n         \u003Cp id=\u0022p-21\u0022\u003EWhile ASD risk is largely genetic, its etiology is multifactorial and heterogeneous. Even so, convergent evidence from transcriptional networks can be used to examine the neural systems basis of ASDs. Genetic findings are a starting point for creating in vitro and in vivo models, and systems biology approaches can be used to identify potential convergent molecular pathways.\u003C\/p\u003E\n      \u003C\/div\u003E\u003Cul class=\u0022copyright-statement\u0022\u003E\u003Cli class=\u0022fn\u0022 id=\u0022copyright-statement-1\u0022\u003E\u00a9 2015 SAGE Publications\u003C\/li\u003E\u003C\/ul\u003E\u003Cspan class=\u0022highwire-journal-article-marker-end\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Cspan id=\u0022related-urls\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Ca href=\u0022http:\/\/mdc.sagepub.com\/content\/15\/9\/26.abstract\u0022 class=\u0022hw-link hw-link-article-abstract\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EView Summary\u003C\/a\u003E\u003C\/div\u003E  \u003C\/div\u003E\n\n  \n  \u003C\/div\u003E\n\u003C\/div\u003E\n  \u003C\/div\u003E\n\u003C\/div\u003E\n\u003C\/div\u003E\u003Cscript type=\u0022text\/javascript\u0022 src=\u0022http:\/\/mdc.sagepub.com\/sites\/all\/modules\/highwire\/highwire\/plugins\/highwire_markup_process\/js\/highwire_figures.js?nzlom2\u0022\u003E\u003C\/script\u003E\n\u003Cscript type=\u0022text\/javascript\u0022 src=\u0022http:\/\/mdc.sagepub.com\/sites\/all\/modules\/highwire\/highwire\/plugins\/highwire_markup_process\/js\/highwire_openurl.js?nzlom2\u0022\u003E\u003C\/script\u003E\n\u003C\/body\u003E\u003C\/html\u003E"}