We thank Erin Douglas, Anna Evans, and Carolyn Bellion for their contributions to participant recruitment and clinical assessment. We also thank Michael Casp, Zoe Englander, Justin Woodlief, and James Carter for their contributions to data collection and analysis, and Robert M. Hamer for consultation on statistical analysis and editing of the manuscript. Finally, we thank the individuals and their families who participated in this study. 
This study was supported by Conte center grant P50 MH064065 from the National Institute of Mental Health. Dr. Hart was supported by T32 HD040127 from the National Institute of Child Health and Human Development.

Chapel Hill - Bu çalışma sırasında kullanılan araştırma teknikleri kurumsal inceleme kurulları Duke Üniversitesi (KİK) ve Kuzey Carolina Üniversitesi tarafından onaylanmıştır. 1. Görüntüleme Görev Tasarımı <ol><li> Daha fazla sıklıkta standart uyaranlara (şifreli görüntüler) bir dizi içinde nadir hedef uyaranlar (daire) sunan bir olay tabanlı davranışsal bir görev oluşturun. Görevin bir şeması, Şekil 1 &#39;de gösterilmiştir. CIGAL yazılımı 10 kullanılarak görevi sunulması. </li></ol><img alt="figür 1" src="/files/ftp_upload/51484/51484fig1.jpg" /> Görev Tasarımı Şekil 1. şematik. Bu rakam izni ile, Hart ve ark., 20 modifiye edilmiştir. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51484/51484fig1large.jpg" target="_blank">Bu rakamın büyük halini görmek için lütfen buraya tıklayınız.</a> <ol style="margin-left: 40px;"><li> Olarak Seçcaydırıcı stimuli et ve Uluslararası Duyuşsal Resim Sistem veritabanı (IAPS) nötr uyaranlar bir dizi. IAPS görüntüleri uyarılma ve değerlik 11 seviyelerini yansıtmak amacıyla 1-9 arasında bir ölçekte derecelendirilir. Yüksek sayılar daha olumlu değerlik ve uyarılma gösterir. Bu tür yılan, örümcek, ya da diğer hayvanların resimleri gibi yaşına uygun çalışma grubuna görüntülerin bir dizi seçin. Not: Bu çalışma için kullanılan görev ilgisiz caydırıcı uyaranlar görüntüler 3.38 (SD = 1.78) ve 6.14 (SD = 2.08), ortalama uyarılma oylaması ortalama değerlik puanı vardı. Nötr uyaranlar görüntüler 6.21 (SD = 0.26) ve 3.72 (SD = 2.15), ortalama uyarılma oylaması ortalama değerlik vardı. </li><li> Görüntüleri 500 milisaniye ile 1500 milisaniye boyunca bir sözde rastgele sırayla sunulmuştur şekilde program görev komut arası uyaran aralığı anlamına gelir. Mevcut hedef uyaranlara ve görev-ilgisiz nötr görüntüler yok daha sık her 15 sn ve uyaranların her biri yaklaşık% 4 yapmak. Jihemodinamik tepki fonksiyonu daha iyi çözünürlük sağlamak amacıyla tter olay başlangıçlı kez. </li></ol><ol start="2"><li> Görüntülerin 8 setleri, katılımcılar 8 ishal boyunca 40 hedefler ve 40 görev ilgisiz nötr görüntüleri toplam sunulmaktadır şekilde 8 fonksiyonel çalışır her biri için bir tane oluşturun. </li></ol> 2. Katılımcı Kurulum ve Tarama <ol><li> Psikoz ailesel yüksek risk altındadır sağlıklı kontrol birey ya ya olan 9 yaş ve 18 arası çocuklar ve gençler askere. <ol><li> Sağlıklı bireyler psikiyatrik hastalığı olan herhangi bir psikiyatrik hastalıkları veya herhangi birinci dereceden aile üyeleri olduğundan emin olun. Ailesel risk katılımcılar şizofreni, en az bir birinci derece akrabası (ebeveyn ya da kardeş) sahip olduğundan emin olun. Birinci derece akrabalarda diğer psikiyatrik hastalıkların varlığı için onları dışlamak etmeyin. </li><li> Ailesel risk grubu katılımcı ile yaş ve cinsiyet maç sağlıklı katılımcılars. </li></ol></li><li> , Küçükler için 18 yaşın üzerindeki katılımcıların bilgilendirilmiş onam Edinme ebeveyn / yasal vasileri bilgilendirilmiş onam kazanır. Ayrıca, çalışmada yer alıyor küçüklerin yazılı onayı kazanır. </li><li> Çevre ile onları tanımak için bir sahte MRI tarayıcı katılımcıları yerleştirin. Tarayıcı gürültü bir ses kaydı oynayın ve onlara görev talimatları anlamak sağlamak amacıyla davranışsal görevin uygulama çalıştırmak tamamlamak zorunda. </li><li> MRG Tarayıcı katılımcıyı yerleştirin ve gerekli beyin taramaları yerelleştirme ve / veya anatomik görüntüler elde. </li><li> MRI güvenli giriş kutusu kullanarak, tüm hedef uyaranlar ve diğer tüm uyaranlara kendi orta parmak ile başka bir düğme yanıt kendi parmağı ile tek bir düğmeye basın katılımcıların söyleyin. </li><li> FMRI taraması sonrasında, katılımcıların bir alt gelen çalışmada kullanılan görüntüler için uyarılma ve değerlik öznel derecelendirme toplamak. Current çalışması 15 kontroller ve ailesel yüksek riskli 13&#39;ten derecelendirme aldı. </li></ol> 3. Görüntü Alma <ol><li> 3,0 Tesla MR tarayıcıya yerleştirin katılımcılar. TE; 5,16 msn: 2.04 msn; FOV: Birincisi, (TR bir şımarık degrade hatırlattı edinme darbe dizisi kullanarak anatomik T1-kontrast görüntünün coplanar bir 3D de dahil olmak üzere yapısal görüntüleri bir dizi elde 24 cm; görüntü matrisi: 256 × 256; Ayaklı Açısı: 20; Voksel boyutu: 0.94 mm x 0.94 mm mm 1.9 x, 68 eksenel dilim). </li><li> Tam beyin kapsama ile bir gradient eko eko-planar görüntüleme sekansı kullanılarak fonksiyonel görüntüleme veri almak (TR: 2000 msn; TE: 27 ms; FOV: 24 cm; görüntü matrisi: 64 × 64; Angle flip: 60; Voksel boyutu: 3.75 mm 3.8 mm × 3.75 mm ×; 34 kesitler) böylece beyin aktivitesi davranışsal görevin yerine getirilmesi sırasında ölçülebilir. Davranışsal görevin her çalışması için bu görüntüleme sırasını çalıştırın. Her çalışma 120 görüntüleme zaman noktalarında oluşmalıdır. </li><li> Ta Günümüzesk 8 fonksiyonel çalıştırır, her biri yaklaşık 4 dakika süren. </li></ol> 4. Analiz <ol><li> Görüntü Önişleme: FSL 12 Open fMRI Uzman Analiz Aracı (FEAT). Birinci düzey analiz ve Ön istatistikleri seçin. <ol><li> &quot;Veri&quot; sekmesinde, giriş görüntülerin sayısını seçin ve işlemek için gidiyoruz MR görüntülerinin her yolunu girin. Çıktı dizini ayarlayın. Toplam hacimleri, atılan satın alma sayısını ve TR giriniz. </li><li> &quot;Pre-stats&quot; sekmesinde, 5 mms için MCFLIRT, FWHM yumuşatma Spatial Hareket düzeltme ayarlayın ve &quot;Dilim zamanlama düzeltme&quot;. &quot;BAHİS beyin çıkarma&quot; ve &quot;Highpass&quot; zamansal filtreleme seçin, ancak B0 unwarping seçmeyin (bir degrade alan harita var uness) ya da &quot;Ben normaization ntensitiy.&quot; 12,14. </li><li> &quot;Kayıt&quot; sekmesinde, &quot;Ana STRU seçinctural görüntü &quot;.. deneğin kafatası elimden T1-ağırlıklı görüntüde yolunu girin, en az 6 DOF ile doğrusal, normal aramayı kullanın. Standart uzay onay kutusunu işaretleyin. MNI atlas yansımasının yolunu girin. Normal, doğrusal kullanın 12 serbestlik dereceli. Basın Git arayın. </li><li> X, Y fazla 3 mm kafa hareketi, ya da Z yönlerinin katılımcılara hariç. </li></ol></li><li> Seviye 1: Tek bir çalışma içinde görev koşulları arasında veri karşılaştırın. Açık FEAT. &quot;Birinci düzey analizi&quot; ve &quot;Stats + Post-istatistikleri&quot; ni seçin. <ol><li> Veri sekmesinde, girişlerin sayısını ayarlamak ve MR görüntülerinin her yolunu girin. . &quot;Çıktı dizini&quot; için bir yol girin &quot;Toplam hacimleri&quot;, atılan satın almaların sayısı ve TR giriniz. </li><li> &quot;Stats&quot; sekmesinde, &quot;Use FİLM prewhitening&quot; onay kutusunu seçin 16. &quot;Tam bir model basın setup &quot;düğmesine basın. Görev koşullarına sayısı &quot;orijinal EVs Sayısı&quot; olarak ayarlayın. Her durum için, Konvolusyon &quot;açılır menüsünden 17,18&quot; Çift-Gama İHÖ&#39;ye &quot;temel şekli açılır menüsünden ve gelen&quot; Özel (3 sütun biçimini) &quot;seçeneğini seçin ve görev zamanlamasını içeren bir metin dosyasını seçin. <ol><li> Verilen bir tür her &quot;olay&quot; için bir giriş ile 3 sütunlarda Bu metin dosyasını biçimlendirin. İlk sütun, ikinci (saniye cinsinden) içermelidir (saniye olarak) başlama zamanı içermelidir ve üçüncü olgu, ağırlık içermelidir. Zıtlıklar &amp; F-testleri sekmesinde, birer görev durumu için kontrast ve her bir karşılaştırma için bir tane oluşturun. </li></ol></li><li> &quot;Post-stats&quot; sekmesinde, &quot;Eşikleme&quot; açılır menüsünden &quot;Küme&quot; ve &quot;Z eşiği&quot; ve Küme P t setsırasıyla, 2.3 ve 0.05 8,19 kadar hreshold. </li><li> &quot;Kayıt&quot; sekmesinde,. &quot;Ana yapısal görüntü&quot; seçeneğini kişinin kafatası elimden T1-ağırlıklı görüntüde yolunu girin. En az 6 DOF ile doğrusal, normal aramayı kullanın. &quot;Standart alan&quot; onay kutusunu işaretleyin. MNI atlas resmin yolunu girin. 12 DOF ile normal, doğrusal aramayı kullanın. Basın &quot;git&quot;. </li></ol></li><li> Seviye 2: her görev koşulu için çalışır arasında veri karşılaştırın. Açık FEAT. Açılır menüden &quot;Yükseköğretim düzeyinde analiz&quot; ve &quot;Stats + Post-istatistikleri&quot; ni seçin. <ol><li> Veri sekmesinde, &quot;Girişler alt düzey FEAT dizinleri&quot; seçeneğini seçin. Girişlerin sayısını belirleyin ve MR görüntülerinin her yolunu girin. &quot;Çıkış dizini&quot; için bir yol girin. </li><li> &quot;Stats&quot; sekmesinde, &quot;Karışık Etkileri: FLAME1&quot; değiştirmek seçim kutusunu &quot;E Sabit etmekfektleri &quot;. Basın&quot; Model kurulum sihirbazı, tek grup ortalama &quot;düğmesine basın. Seç&quot; &quot;ve&quot; Süreç &quot;düğmesine basın. </li><li> &quot;Post-stats&quot; sekmesinde, &quot;Eşikleme&quot; açılır menüsünden &quot;küme&quot; öğesini seçin ve sırasıyla 2.3 ve 0.05 8,19 için &quot;Z eşiği&quot; ve &quot;Küme P&quot; eşiğini ayarlamak. Basın &quot;git&quot;. </li></ol></li><li> Seviye 3: Tüm çalışır genelinde her görev durumu için konular arasında veri karşılaştırın. Açık FEAT. Açılır menüden &quot;Yükseköğretim düzeyinde analiz&quot; ve &quot;Stats + Post-istatistikleri&quot; ni seçin. <ol><li> Veri sekmesinde, girişlerin sayısını ayarlayın. &quot;Girişler FEAT dizinleri görüntüleri baş 3D olan&quot; seçeneğini seçin ve MR görüntülerinin her yolunu girin. &quot;Çıkış dizini&quot; için bir yol girin. </li><li> &quot;Stats&quot; tab, basın &quot;Tam bir model setup&quot;. Her EV - örneğin teşhis vs grup, yaş, cinsiyet, her konu için değerleri (Girdi n Input 1) girin, grup değişkenleri ve değişkenlerin sayısına eşit EVs sayısını ayarlayın. Bu değerlerin bir elektronik tablo kopyalamak için &quot;Yapıştır&quot; penceresini kullanabilirsiniz. <ol><li> &quot;Zıtlıklar &amp; F-testi&quot; sekmesinde, her test değişkeni için ve her kontrast (örn tanı grubu) için bir kontrast eklemek. Her test değişkeni için uygun EV kolonda 1 değerini seçerek kontrast ayarlayın. Her kontrast, 1 ilk değer ve -1 ikinci ayarlayın. &quot;Bitti&quot; seçeneğini seçin. </li></ol></li><li> &quot;Post-stats&quot; sekmesinde, &quot;Eşikleme&quot; açılır menüsünden &quot;Küme&quot; ve sırasıyla 2.3 ve 0.05 8,19 için &quot;Z eşiği&quot; ve &quot;Küme P&quot; eşiğini ayarlamak </sup&gt;. Basın &quot;git&quot;. </li></ol></li></ol>

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Dr. Perkins is currently receiving grant support from Janssen, and is a consultant to Dainippon. In the past, Dr. Perkins is or has been on speaker's bureau for Eli Lilly and AstraZeneca. Dr. Perkins has previously received grants from AstraZeneca, Bristol-Myers Squibb, Otsuka, Eli Lilly, Janssen and Pfizer; and consulting and educational fees from AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Janssen, Glaxo Smith Kline, Forest Labs, Pfizer and Shire. All other authors declare no biomedical financial interests or potential conflict of interest.

The modified emotional oddball paradigm in the current study has been shown to elicit differences in neural recruitment during selective attention and emotional processing in children and adolescents at risk for schizophrenia. While existing paradigms using the emotional oddball task have been used to investigate neural changes in adult populations with psychiatric illness8, the current paradigm may be particularly useful for measurement of vulnerability markers in younger age groups. 
There are several challenges inherent in performing fMRI studies with children and adolescents, including ensuring that the task is appropriate in terms of difficulty and content and in minimizing motion artifacts. Critical steps in the protocol include step 2.2, which requires that participants have a mock scan and practice session prior to the fMRI session. This step is particularly helpful for improving participants&#8217; comfort and data quality. Additionally, the design of the behavioral task is critical to ensure that the task has an appropriate level of difficulty and that the selection of aversive images is appropriate for younger age groups. The current task design was successful in eliciting significant behavioral differences between the aversive and neutral conditions and had a moderate difficulty level for both high-risk and control groups. 
A limitation of this protocol was that the task did not elicit a significant difference in amygdala activation between the aversive and neutral conditions. There was significant amygdala activation during the aversive condition, but the difference was not significant when contrasted with neutral stimuli. This finding is likely due to the selection of children-appropriate stimuli. Future modifications of the task could examine effects of emotional facial expressions, which may produce more robust differences in amygdala activation between emotional and neutral conditions21. 
Other future applications of this technique include applying it to populations of children and adolescents who may be at risk for other psychiatric illnesses that may similarly affect executive and emotional processing. Several other psychiatric conditions, such as bipolar disorder and mood disorders, have been found to be associated with alterations in brain structure and function that reflect endophenotypes, or heritable markers associated with a disease22,23. This suggests the possibility that these intermediate changes in brain structure or function may potentially precede the onset of pathological symptoms in at-risk individuals. The use of the described paradigm in longitudinal studies and with children and adolescents may help to identify relevant endophenotypes for a particular condition to help to refine risk estimates.

Schizophrenia is a neurodevelopmental disorder with a known genetic component1,2 and with symptoms including deficits in both executive and emotional processing3,4. First-degree relatives are thought to be at an increased risk of developing schizophrenia, and have been shown to share some of these same neurocognitive deficits in both cognitive and social-emotional domains5. We therefore expect that brain activity in regions associated with executive and emotional processing may be altered in at-risk family members preceding the onset of clinical symptoms. 
Previous studies have indicated that both adults with schizophrenia and adults at familial high risk show aberrant activity within executive and emotional processing networks; however it remains unclear how these changes come about during development. Demonstrating that these changes occur early in life will be a critical first step in understanding the pathophysiology of the disorder. Therefore, this study utilizes an emotional oddball paradigm during functional MRI (fMRI) scanning in order to measure brain activity during the completion of a task that requires both executive and emotional processing in adolescents who are at risk for developing schizophrenia. Oddball paradigms are frequently used to examine the function of fronto-striate circuitry in schizophrenia6 and in individuals with familial high risk7 by measuring selective attention processes allocated to task-relevant target stimuli. Here, a standard oddball task has been modified to include task-irrelevant aversive and neutral stimuli that have been shown to elicit changes in brain activity in patients with schizophrenia8. 
This paper measures functional differences between healthy adolescents and adolescents at high familial risk for schizophrenia using an emotional oddball task. The task design is similar to that used by Fichtenholtz and colleagues9, but the selection of aversive emotional images has been modified to be appropriate for children between the ages of 9-18. The use of this task during functional MRI allowed for the identification of specific brain regions that showed patterns of hyperactivation and hypoactivation in children and adolescents with FHR for schizophrenia, in addition to age-related changes in neural activity during adolescent development.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td class="xl63">3T MRI scanner</td>
			<td class="xl63">GE</td>
			<td class="xl63"></td>
			<td class="xl63">BIAC 3T scanner (replaced)</td>
		</tr>
	</tbody>
</table>

measurement of fronto-limbic activity using an emotional oddball task in children with familial high risk for schizophrenia

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with schizophrenia who are at familial high risk (FHR) may show similar cognitive and emotional changes. However, the neurological changes underlying the emergence of these symptoms remain unclear. This study sought to identify differences in frontal, striatal, and limbic regions in children and adolescents with FHR using functional magnetic resonance imaging. Groups of 21 children and adolescents at FHR and 21 healthy controls completed an emotional oddball task that relied on selective attention and the suppression of task-irrelevant emotional information. The standard oddball task was modified to include aversive and neutral distractors in order to examine potential group differences in both emotional and executive processing. This task was designed specifically to allow for children and adolescents to complete by keeping the difficulty and emotional image content age-appropriate. Furthermore, we demonstrate a technique for suitable fMRI registration for children and adolescent participants. This paradigm may also be applied in future studies to measure changes in neural activity in other populations with hypothesized developmental changes in executive and emotional processing.

Demografik özelliklerine göre gruplara 20 arasında fark yoktu. Davranış veriler hedef saptama görevi 9-18 yaş arası çocuklar ve gençler için zorluk uygun bir seviyede olduğunu belirtti. Bu çalışmada, doğru hedeflere (SD = 0.14) ve 82,36% tespit ve ailesel risk grubunun doğru hedeflere (SD = 0.17) ve 76.8% tespit denetimleri. Her iki grup da nötr resimler kıyasla duygusal resimler belirlenmesinde doğruluğu azalma gösterdi (F (1,40) = 5.63, p = 0.03). Görüntüleme verileri deneysel koşullar yürütme ve duygusal işleme sırasında işe beklenen bölgelerde önemli aktivasyonuna neden olduğunu belirtti. Aktivasyon hedef denemeler sırasında ve sağ amigdala, bilateral orbitofrontal korteks, fusiform korteks ve görsel cor de prefrontal, ön kaudat, ada ve posterior parietal bölgelerde görüldüHer iki grupta da caydırıcı denemeler sırasında kortikal alanlar. Tablo 1 her bir durum için kontrollerinde önemli aktivasyon alanları göstermektedir. Bu paradigma da kontroller ve şizofreni için ailesel riski yüksek olan bireyler arasındaki aktivasyon önemli farklılıklar ortaya çıkardı. Ailesel yüksek riskli grup uyaranlara hedef yanıt fronto-striat devresi aktivasyon azalma gösterdi. Kontroller, aksine, orta frontal girus ve insulada daha aktivasyon gösterdi. Koşullar arasında Grup farklılıkları. Tablo 2 ve Şekil 2&#39;de gösterilmiştir ailesel yüksek risk grubu da hedef yanıt denetimleri ve caydırıcı stimuli (Şekil 3) ile karşılaştırıldığında yaşa bağlı aktivasyon farklı desen gösterdi. <img alt="Şekil 2," src="/files/ftp_upload/51484/51484fig2.jpg" /> Arası Grubu&#39;nun Farklılıkların 2. Aktivasyon Maps Şekil. (A) ailesel yüksek risk grubu (n = 21), hedef işleme sırasında kontrollere (n = 21) daha fazla aktivasyon gösterdi Alanları. Caud = Kaudat; BAG = İnferior frontal girus; ITG = İnferior temporal girus. (B) denetimleri hedef işleme sırasında ailesel yüksek risk grubunda daha fazla aktivasyon gösterdi Alanları. INS = Insula; MFG = Orta frontal girus; MTG = Orta temporal girus. (C) Ailesel yüksek risk grubu Caydırıcı&gt; Nötr kontrast sırasında kontrollere göre daha aktif Alanları. COC = Orta solungaç korteks. (D) denetimleri Caydırıcı&gt; Nötr kontrast sırasında ailesel yüksek risk grubunda daha fazla aktive Alanları. ACC = Anterior singulat korteks; PC = precuneus. Bu rakam Hart ve ark modifiye edilmiştir. 20, izninizle. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51484/51484fig2large.jpg" target="_blank">Bu f büyük halini görmek için tıklayınız</a>ŞEKIL. <img alt="Şekil 3," src="/files/ftp_upload/51484/51484fig3.jpg" /> Şekil 3. Yaşa Bağlı Grubu Farklılıkların aktivasyonu Maps. Hedef işleme sırasında kontrollere göre ailesel yüksek risk grubunda yaşla birlikte daha pozitif korelasyon (A) Alanlar. ACC = Anterior singulat korteks; INS = Insula; OFC = Orbitofrontal korteks; TH = Thalamus. Caydırıcı&gt; Nötr kontrast sırasında ailesel yüksek risk grubuna göre daha kontrollerde yaşla birlikte daha pozitif korelasyon (B) Alanlar. BAG = İnferior frontal girus; PostCG = postsentral girus; PreCG = presantral gyrus. Bu rakam izni ile, Hart ve ark., 20 modifiye edilmiştir. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51484/51484fig3large.jpg" target="_blank">Bu rakamın büyük halini görmek için lütfen buraya tıklayınız.</a> <table border="0" cellpadding="0" cellspacing="0"> &lt;tbody&gt; <tr><td> ST1. Içinde-Grup Aktivasyon Foci Kontroller (n = 21) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td colspan="3"> MNI koordinatları </td><td></td><td></td></tr><tr><td> Yöre </td><td> Yarımküre </td><td> x </td><td> y </td><td> z </td><td> Max Z-değeri </td><td> Max p-değeri 1 </td></tr><tr><td> Hedef aktivasyonu (p &lt;0.05, yanlış keşif hızı düzeltilmiş) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Orta frontal girus / Frontal kutup </td><td> B </td><td> -30 </td><td> -2 </td><td> 50 </td><td> 5.57 </td><td> &lt;0.0000001 </td></tr><tr><td> Inferior frontal girus </td><td> B </td><td> 46 </td><td> 12 </td><td> 32 </td><td> 5.41 </td><td> &lt;0.0000001 </td></tr><tr><td> Insula </td><td> B </td><td> -32 </td><td> 24 </td&gt; <td> 0 </td><td> 5.4 </td><td> &lt;0.0000001 </td></tr><tr><td> Presantral girus </td><td> B </td><td> -40 </td><td> -22 </td><td> 48 </td><td> 5.53 </td><td> &lt;0.0000001 </td></tr><tr><td> Talamus </td><td> B </td><td> -12 </td><td> -16 </td><td> 12 </td><td> 5.03 </td><td> &lt;0.0000001 </td></tr><tr><td> Kuyruklu </td><td> B </td><td> -12 </td><td> 12 </td><td> 4 </td><td> 4.07 </td><td> 0.000003 </td></tr><tr><td> Putamen </td><td> B </td><td> 18 </td><td> 8 </td><td> 2 </td><td> 4.27 </td><td> 0,00009 </td></tr><tr><td> Anterior singulat / Paracingulate gyrus </td><td> B </td><td> 0 </td><td> 12 </td><td> 46 </td><td> 5.6 </td><td> &lt;0.0000001 </td></tr><tr><td> Posterior singulat girus </td><td> B </td><td> 8 </td><td> -16 </td><td> 28 </td><td> 5.2 </td><td> &lt;0.0000001 </td></tr><tr><td> Üstün / Orta temporal girus </td> &lt;td&gt; B </td><td> 48 </td><td> -46 </td><td> 10 </td><td> 5.88 </td><td> &lt;0.0000001 </td></tr><tr><td> Fusiform / inferior temporal girus </td><td> B </td><td> -30 </td><td> -50 </td><td> -12 </td><td> 5.64 </td><td> &lt;0.0000001 </td></tr><tr><td> Üstün parietal lobül / supramarginal gyrus&#39;u / postsentral girus </td><td> B </td><td> 30 </td><td> -44 </td><td> 44 </td><td> 6 </td><td> &lt;0.0000001 </td></tr><tr><td> Yanal Oksipital korteks </td><td> B </td><td> 48 </td><td> -62 </td><td> 12 </td><td> 6.12 </td><td> &lt;0.0000001 </td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Caydırıcı&gt; Nötr aktivasyon (p &lt;0.05, yanlış keşif hızı düzeltilmiş) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Inferior frontal girus </td><td> L </td><td> -44 </td><td> 14 </td&gt; <td> 14 </td><td> 3.16 </td><td> 0.0004 </td></tr><tr><td> Frontal kutup / Medial frontal korteks </td><td> B </td><td> -2 </td><td> 64 </td><td> 0 </td><td> 3.42 </td><td> 0.0005 </td></tr><tr><td> Postsentral girus </td><td> L </td><td> -62 </td><td> -22 </td><td> 34 </td><td> 3.12 </td><td> 0.0004 </td></tr><tr><td> Anterior singulat korteks </td><td> B </td><td> -4 </td><td> 34 </td><td> 8 </td><td> 3.27 </td><td> 0.0002 </td></tr><tr><td> Posterior singulat girus </td><td> B </td><td> 0 </td><td> -44 </td><td> 28 </td><td> 3.26 </td><td> 0.0002 </td></tr><tr><td> Alt temporal / Fusiform girus </td><td> B </td><td> -44 </td><td> -44 </td><td> -14 </td><td> 3.03 </td><td> 0.0006 </td></tr><tr><td> Eğik girus </td><td> B </td><td> 46 </td><td> -64 </td><td> 8 </td><td> 3.42 </td><td> 0.0001 </td></tr><tr><td> Supramarginal girus </td><td> L </td><td> -40 </td><td> -56 </td><td> 20 </td><td> 3.59 </td><td> 0.00005 </td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Caydırıcı aktivasyon (p &lt;0.05, yanlış keşif hızı düzeltilmiş) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Amygdala </td><td> R </td><td> 22 </td><td> -4 </td><td> -18 </td><td> 2.86 </td><td> 0.001 </td></tr><tr><td> Orbitofrontal kortekste / Insula </td><td> B </td><td> 36 </td><td> 22 </td><td> -4 </td><td> 4.93 </td><td> &lt;0.0000001 </td></tr><tr><td> Orta frontal girus </td><td> B </td><td> 32 </td><td> 4 </td><td> 40 </td><td> 4.7 </td><td> &lt;0.0000001 </td></tr><tr><td> Frontal kutup </td><td> B </td><td> -38 </td><td> 36 </td><td> 10 </td><td> 4.95 </td><td> &lt;0.0000001 </td></tr><tr><td> Anterior singulat /paracingulate girus </td><td> B </td><td> 6 </td><td> 16 </td><td> 50 </td><td> 4.85 </td><td> &lt;0.0000001 </td></tr><tr><td> Posterior singulat girus </td><td> B </td><td> 2 </td><td> -28 </td><td> 24 </td><td> 5.88 </td><td> &lt;0.0000001 </td></tr><tr><td> Talamus </td><td> B </td><td> 18 </td><td> -26 </td><td> 2 </td><td> 5.44 </td><td> &lt;0.0000001 </td></tr><tr><td> Presantral girus </td><td> B </td><td> -44 </td><td> 8 </td><td> 34 </td><td> 4.54 </td><td> &lt;0.0000001 </td></tr><tr><td> Üstün parietal lobül </td><td> B </td><td> -20 </td><td> -56 </td><td> 54 </td><td> 6.05 </td><td> &lt;0.0000001 </td></tr><tr><td> Yanal Oksipital korteks </td><td> B </td><td> -36 </td><td> -82 </td><td> 4 </td><td> 6.05 </td><td> &lt;0.0000001 </td></tr><tr><td> Oksipital kutup </td><td> B </td><td> -16 </td><td> -90 </td><td> 18 </td><td> 5.18 </td&gt; <td> &lt;0.0000001 </td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> B, İkili </td><td rowspan="2"></td><td rowspan="2"></td><td rowspan="2"></td><td rowspan="2"></td><td rowspan="2"></td><td rowspan="2"></td></tr><tr><td> 1 Bildirilen p-değerleri FDR-düzeltilmiş değerinin &lt;0.05 anlamlı, düzeltilmedi </td></tr></tbody></table> Kontroller (n = 21) &#39;de içi Grup Aktivasyon Foci Tablo 1.. Bu tablo izni ile, Hart ve ark., 20 modifiye edilmiştir. <table border="0" cellpadding="0" cellspacing="0"><tbody><tr><td> Tablo 2. arası Grubu&#39;nun Aktivasyonu Farklılıkların </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td colspan="3"> MNI koordinatları </td><td></td><td></td></tr&gt; <tr><td></td><td> Yarımküre </td><td> x </td><td> y </td><td> z </td><td> Max Z-değeri </td><td> Max p-değeri 1 </td></tr><tr><td> Hedefler </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Ailevi Yüksek riskli&gt; Controls (p &lt;0.05, yanlış keşif hızı düzeltilmiş) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Frontal kutup </td><td> B </td><td> 16 </td><td> 76 </td><td> 6 </td><td> 3.52 </td><td> 0,00007 </td></tr><tr><td> Inferior frontal girus </td><td> L </td><td> -58 </td><td> 16 </td><td> 18 </td><td> 3.37 </td><td> 0.0001 </td></tr><tr><td> Kuyruklu </td><td> B </td><td> -14 </td><td> 20 </td><td> 10 </td><td> 3.2 </td><td> 0.0003 </td></tr><tr><td> Inferior temporal girus </td><td> L </td><td> -52 </td><td> -44 </td><td> -20 </td><td> 2.94 </td><td> 0.0009 </td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Kontroller&gt; Ailevi Yüksek Riskli (p &lt;0.05, yanlış keşif hızı düzeltilmiş) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Orta frontal girus / presentral girustan </td><td> R </td><td> 48 </td><td> 8 </td><td> 34 </td><td> 3 </td><td> 0.0007 </td></tr><tr><td> Frontal korteks operkulum </td><td> L </td><td> -46 </td><td> 16 </td><td> -4 </td><td> 2.94 </td><td> 0.0009 </td></tr><tr><td> Ek motor alan </td><td> R </td><td> 18 </td><td> -16 </td><td> 40 </td><td> 3.02 </td><td> 0.0007 </td></tr><tr><td> Insula </td><td> L </td><td> -34 </td><td> -18 </td><td> 4 </td><td> 2.94 </td><td> 0.0009 </td></tr><tr><td> Presantral girus </td><td> B </td><td> 10 </td><td> -26 </td><td> 60 </td&gt; <td> 3.29 </td><td> 0.0002 </td></tr><tr><td> Postsentral girus </td><td> B </td><td> 14 </td><td> -38 </td><td> 54 </td><td> 3.57 </td><td> 0.0001 </td></tr><tr><td> Superior temporal girus </td><td> R </td><td> 54 </td><td> -6 </td><td> -4 </td><td> 3.18 </td><td> 0.0003 </td></tr><tr><td> Orta temporal girus </td><td> R </td><td> 48 </td><td> -46 </td><td> 8 </td><td> 3.65 </td><td> 0,00004 </td></tr><tr><td> Precuneus </td><td> R </td><td> 2 </td><td> -40 </td><td> 46 </td><td> 2.89 </td><td> 0.001 </td></tr><tr><td> Yanal Oksipital korteks </td><td> B </td><td> -20 </td><td> -74 </td><td> 36 </td><td> 3.36 </td><td> 0.0002 </td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Caydırıcı - Nötr </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td&gt; Ailevi Yüksek Riskli&gt; Controls (p &lt;0.05, yanlış keşif hızı düzeltilmiş) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Orta solungaç korteks </td><td> R </td><td> 50 </td><td> -2 </td><td> 6 </td><td> 3.01 </td><td> 0.0007 </td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Kontroller&gt; Ailevi Yüksek Riskli (p &lt;0.05, yanlış keşif hızı düzeltilmiş) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> Anterior singulat korteks </td><td> L </td><td> -6 </td><td> 38 </td><td> 8 </td><td> 2.68 </td><td> 0.002 </td></tr><tr><td> Precuneus </td><td> L </td><td> -10 </td><td> -54 </td><td> 36 </td><td> 2.7 </td><td> 0.002 </td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> B, İkili </td><td rowspan="2"></td><td rowspan="2"></td><td rowspan="2"></td><td rowspan="2"></td><td rowspan="2"></td><td rowspan="2"></td></tr><tr><td> 1 Bildirilen p-değerleri FDR-düzeltilmiş değerinin &lt;0.05 anlamlı, düzeltilmedi </td></tr></tbody></table> Tablo 2. arası Grup Aktivasyon Foci Farklılıkların. Bu tablo izni ile, Hart ve ark., 20 modifiye edilmiştir.

Watch this Scientific Journal Video about Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia at JoVE.com

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

This paper describes how to use the emotional oddball task and fMRI to measure brain activation in children and adolescents at familial high risk for schizophrenia (FHR).&#160;FMRI was used to measure differences in fronto-striato-limbic regions during an emotional oddball task. Children with FHR exhibited abnormal functional activation during adolescence.

Şizofreni ailesel Riskli Çocuklarda Duygusal Oddball Görev Kullanımı Fronto-limbik Faaliyet Ölçümü

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with ...

measurement-fronto-limbic-activity-using-an-emotional-oddball-task

Nanyang Technological University

Research

JoVE Journal

Behavior

8.8K Görüntüleme.  University of North Carolina at Chapel Hill School of Medicine. This paper describes how to use the emotional oddball task and fMRI to measure brain activation in children and adolescents at familial high risk for schizophrenia (FHR). FMRI was used to measure differences in fronto-striato-limbic regions during an emotional oddball task. Children with FHR exhibited abnormal functional activation during adolescence.

Video: Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

There has been a long-standing interest in the assessment of the neurobehavioral integrity of the newborn infant. The NICU Network Neurobehavioral Scale (NNNS) was developed as an assessment for the at-risk infant. These are infants who are at increased risk for poor developmental outcome because of insults during prenatal development, such as substance exposure or prematurity or factors such as poverty, poor nutrition or lack of prenatal care that can have adverse effects on the intrauterine environment and affect the developing fetus. The NNNS assesses the full range of infant neurobehavioral performance including neurological integrity, behavioral functioning, and signs of stress/abstinence. The NNNS is a noninvasive neonatal assessment tool with demonstrated validity as a predictor, not only of medical outcomes such as cerebral palsy diagnosis, neurological abnormalities, and diseases with risks to the brain, but also of developmental outcomes such as mental and motor functioning, behavior problems, school readiness, and IQ. The NNNS can identify infants at high risk for abnormal developmental outcome and is an important clinical tool that enables medical researchers and health practitioners to identify these infants and develop intervention programs to optimize the development of these infants as early as possible. The video shows the NNNS procedures, shows examples of normal and abnormal performance and the various clinical populations in which the exam can be used.

The NICU Network Neurobehavioral Scale (NNNS) was developed as an assessment for the at-risk infant. The purpose of this article is to describe the NNNS, provide video examples of the NNNS procedures and discuss the ways in which the exam has been used.

Değerlendirme ve Yüksek Riskli Yenidoğan Değerlendirilmesi: YYBB Ağ Nörodavranışsal Ölçeği

There has been a long-standing interest in the assessment of the neurobehavioral integrity of the newborn infant.  The NICU Network Neurobehavioral Scale ...

Davranış

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Na&#239;ve Adolescents at High Risk for Psychosis

Growing evidence suggests that movement abnormalities are a core feature of psychosis. One marker of movement abnormality, dyskinesia, is a result of impaired neuromodulation of dopamine in fronto-striatal pathways. The traditional methods for identifying movement abnormalities include observer-based reports and force stability gauges. The drawbacks of these methods are long training times for raters, experimenter bias, large site differences in instrumental apparatus, and suboptimal reliability. Taking these drawbacks into account has guided the development of better&#160;standardized and more efficient procedures to examine movement abnormalities through handwriting analysis software and tablet. Individuals at&#160;risk for psychosis showed significantly more dysfluent pen movements (a proximal measure for dyskinesia) in a handwriting task. Handwriting kinematics offers a great advance over previous methods of assessing dyskinesia, which could clearly be beneficial for understanding the etiology of psychosis.

Handwriting analysis software significantly improves upon existing instruments measuring movement disorders. Individuals at&#160;risk for psychosis and healthy controls completed handwriting tasks to test for dyskinesia. Results suggest that youth at&#160;risk for psychosis exhibit dyskinesia and that handwriting analysis could significantly contribute to wider dissemination of early identification efforts

El Yazısı Analizi Psikoz Yüksek Risk nöroleptiğe Naif Ergenlerde Spontan diskinesiyas gösterir

Growing evidence suggests that movement abnormalities are a core feature of psychosis. One marker of movement abnormality, dyskinesia, is a result of ...

Quantitative Assessment of Cortical Auditory-tactile Processing in Children with Disabilities

Objective and easy measurement of sensory processing is extremely difficult in nonverbal or vulnerable pediatric patients. We developed a new methodology to quantitatively assess children&#39;s cortical processing of light touch, speech sounds and the multisensory processing of the 2 stimuli, without requiring active subject participation or causing children discomfort. To accomplish this we developed a dual channel, time and strength calibrated air puff stimulator that allows both tactile stimulation and sham control. We combined this with the use of event-related potential methodology to allow for high temporal resolution of signals from the primary and secondary somatosensory cortices as well as higher order processing. This methodology also allowed us to measure a multisensory response to auditory-tactile stimulation.

Objective and easy measurement of sensory processing is extremely difficult in nonverbal or vulnerable pediatric patients. We developed a new methodology to quantitatively assess infants and children&#39;s cortical processing of light touch, speech sounds, and the multisensory processing of the 2 stimuli, without requiring active subject participation or causing discomfort in vulnerable patients.

Engelli Çocuklarda Kortikal İşitsel-dokunsal İşleme Kantitatif Değerlendirilmesi

Objective and easy measurement of sensory processing is extremely difficult in nonverbal or vulnerable pediatric patients. We developed a new methodology ...

Cortical Source Analysis of High-Density EEG Recordings in Children

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2, because the composition and spatial configuration of head tissues changes dramatically over development3.&#160;
In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how&#160;EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.&#160;

In recent years, there has been increasing interest in estimating the cortical sources of scalp measured electrical activity for cognitive neuroscience experiments. This article describes how high density EEG is acquired and how recordings are processed for cortical source estimation in children from the age of 2 years at the London Baby Lab.

Çocuklarda Yüksek Yoğunlukta EEG Recordings kortikal Kaynak Analizi

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and ...

Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression

Ruminative brooding is associated with increased vulnerability to major depression. Individuals who regularly ruminate will often try to reduce the frequency of their negative thoughts by actively suppressing them. We aim to identify the neural correlates underlying thought suppression in at-risk and depressed individuals. Three groups of women were studied; a major depressive disorder group, an at-risk group (having a first degree relative with depression) and controls. Participants performed a mixed block-event fMRI paradigm involving thought suppression, free thought and motor control periods. Participants identified the re-emergence of &#8220;to-be-suppressed&#8221; thoughts (&#8220;popping&#8221; back into conscious awareness) with a button press. During thought suppression the control group showed the greatest activation of the dorsolateral prefrontal cortex, followed by the at-risk, then depressed group. During the re-emergence of intrusive thoughts compared to successful re-suppression of those thoughts, the control group showed the greatest activation of the anterior cingulate cortices, followed by the at-risk, then depressed group. At-risk participants displayed anomalies in the neural regulation of thought suppression resembling the dysregulation found in depressed individuals. The predictive value of these changes in the onset of depression remains to be determined.

We aim to identify the neural correlates underlying sustained and transient thought suppression, and thought re-emergence in controls, at-risk and depressed individuals. Activation was greatest for controls compared to the at-risk and the depressed group in the dorsolateral prefrontal cortex during thought suppression and anterior cingulate cortex during thought re-emergence.

FMRI Çalışması Tasarım ve Uygulama Genç Kadınlarda Bastırma Düşünce ve At-risk Depresyon incelenmesi

Ruminative brooding is associated with increased vulnerability to major depression. Individuals who regularly ruminate will often try to reduce the ...

Acquisition of a High-precision Skilled Forelimb Reaching Task in Rats

Movements are the main measurable output of central nervous system function. Developing behavioral paradigms that allow detailed analysis of motor learning and execution is of critical importance in order to understand the principles and processes that underlie motor function. Here we present a paradigm to study movement acquisition within a daily session of training (within-session) representing the fast learning component and primary acquisition as well as skilled motor learning over several training sessions (between-session) representing the slow learning component and consolidation of the learned task. This behavioral paradigm increases the degree of difficulty and complexity of the motor skill task due to two features: First, the animal realigns its body prior to each pellet retrieval forcing renewed orientation and preventing movement execution from the same angle. Second, pellets are grasped from a vertical post that matches the diameter of the pellet and is placed in front of the cage. This requires a precise grasp for successful pellet retrieval and thus prevents simple pulling of the pellet towards the animal. In combination with novel genetics, imaging and electrophysiological technologies, this behavioral method will aid to understand the morphological, anatomical and molecular underpinnings of motor learning and memory.

A paradigm is presented to analyze the acquisition of a high-precision skilled forelimb reaching task in rats.

Nitelikli ön ayakları Sıçanlarda Görev ulaşan bir Yüksek hassasiyet Edinme

Movements are the main measurable output of central nervous system function. Developing behavioral paradigms that allow detailed analysis of motor ...

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Delay discounting refers to a decline in the value of a reward when it is delayed relative to when it is immediately available. Delay discounting tasks are used to identify indifference points, which reflect equal preference for two dichotomous reward alternatives differing in both delay and magnitude. Indifference points are key to assessing the shape of a delay-discounting gradient because they allow us to isolate the effect of delay on value. For example, if at a 1 week delay and a maximum of $1,000, the indifference point is at $700 we know that, for that participant, a 1-week delay corresponds to a 30% reduction in value. This video outlines an adjusting amount delay-discounting task that identifies indifference points relatively quickly and is inexpensive and easy to administer. Once data have been collected, non-linear regression techniques are typically used to generate discounting curves. The steepness of the discounting curve reflects the degree of impulsive choice of a group or individual. These techniques have been used with a wide range of commodities and have identified populations that are relatively impulsive. For example, people with substance abuse problems discount delayed rewards more steeply than control participants. Although degree of discounting varies as a function of the commodity examined, discounting of one commodity correlates with discounting of other commodities, which suggests that discounting may be a persistent pattern of behavior1.

Delay discounting refers to a decline in the value of a reward when it is delayed relative to when it is immediately available. We outline a computer-based delay discounting task that is easy to implement and allows for the quantification of the degree of delay discounting in human participants.

Gecikme bir ayarlama Tutar görev kullanma İnsanlardaki İndirim Ölçme

Delay discounting refers to a decline in the value of a reward when it is delayed relative to when it is immediately available. Delay discounting tasks ...

The Emotional Stroop Task: Assessing Cognitive Performance under Exposure to Emotional Content

The emotional Stroop effect (ESE) is the result of longer naming latencies to ink colors of emotion words than to ink colors of neutral words. The difference shows that people are affected by the emotional content conveyed by the carrier words even though they are irrelevant to the color-naming task at hand. The ESE has been widely deployed with patient populations, as well as with non-selected populations, because the emotion words can be selected to match the tested pathology. The ESE is a powerful tool, yet it is vulnerable to various threats to its validity. This report refers to potential sources of confounding and includes a modal experiment that provides the means to control for them. The most prevalent threat to the validity of existing ESE studies is sustained effects and habituation wrought about by repeated exposure to emotion stimuli. Consequently, the order of exposure to emotion and neutral stimuli is of utmost importance. We show that in the standard design, only one specific order produces the ESE.

The emotional Stroop effect (ESE) is the result of longer naming latencies to ink colors of emotion words than those of neutral words. This report refers to potential sources of confounding and includes a modal experiment that provides the means to control for them.

Duygusal Stroop Görev: Duygusal İçeriğe Pozlama altında Bilişsel Performans Değerlendirme

The emotional Stroop effect (ESE) is the result of longer naming latencies to ink colors of emotion words than to ink colors of neutral words. The ...

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify potentially suitable therapeutic populations. The need to use a comprehensive battery of pain models is highlighted by studies whereby only a single pain model, thought to relate to the clinical situation, demonstrates lack of efficacy. No single experimental model can mimic the complex nature of clinical pain. The integrated, multi-modal pain task&#160;battery presented here encompasses the electrical stimulation task, pressure stimulation task, cold pressor task, the UVB inflammatory model which includes a thermal task and a paradigm for inhibitory conditioned pain modulation. These human pain models have been tested for predicative validity and reliability both in their own right and in combination, and can be used repeatedly, quickly, in short succession, with minimum burden for the subject and with a modest quantity of equipment. This allows a drug to be fully characterized and profiled for analgesic effect which is especially useful for drugs with a novel or untested mechanism of action.

Human pain models are valuable tools used to assess the analgesic potential of novel compounds and predict their clinical efficacy, especially when used in an integrated manner. Although implementation of these models is complex, with proper execution, the pain models described in this protocol can provide predictive and reliable results.

Bir Entegre, Multi-Modal Ağrı Görev Kullanma Pil Ağrı Algılama ve Tolerans eşikleri Belirlenmesi

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify ...

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

Involvement of patients and the public is now recognized to be essential for the good conduct of research. Patient and public involvement in research priority setting and funding decisions is only beginning to be recognized as important, and methods for doing so are nascent. This protocol describes the Research Prioritization by Affected Communities (RPAC) protocol and findings from its use with women at high socio-demographic risk for preterm birth. The goal was to directly involve these women in identifying and prioritizing their unanswered questions about pregnancy, birth and neonatal care, and treatment so that their views could be included in research priority setting by funders and researchers. The RPAC protocol may be used to meaningfully involve under-represented groups at high-risk for specific health problems, or those who face disproportionate burden of disease, in research strategy and funding priority setting.

This manuscript describes the Research Prioritization by Affected Communities (RPAC) protocol and findings from its use with women at risk for preterm birth. Using the protocol, women identified and prioritized their unanswered questions about pregnancy, birth and neonatal care aimed at influencing research priority setting by funders and researchers.

Kadınlar renk araştırma öncelik ayarı olarak erken doğum için yüksek risk içeren bir roman yöntemi

Involvement of patients and the public is now recognized to be essential for the good conduct of research. Patient and public involvement in research ...