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.

채플 힐 -이 연구 기간 동안 사용 된 연구 기술은 제도적 검토 보드 듀크 대학 (IRB)와 노스 캐롤라이나 대학에 의해 승인되었다. 1. 이미징 작업 디자인 <ol><li> 더 - 자주 표준 자극 (스크램블 이미지)의 순서 내에서 드문 대상 자극 (원)을 제시 이벤트 기반 행동 작업을 생성합니다. 태스크의 개략도는도 1에 도시된다. CIGAL 소프트웨어 (10)를 이용하여 작업을 제시한다. </li></ol><img alt="그림 1" src="/files/ftp_upload/51484/51484fig1.jpg" /> 작업 디자인의 그림 1. 도식은.이 그림은 허가, 하트 등. (20)에서 수정되었습니다. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51484/51484fig1large.jpg" target="_blank">이 그림의 더 큰 버전을 보려면 여기를 클릭하십시오.</a> <ol style="margin-left: 40px;"><li> 등의 선택혐오 자극 등 국제 정서 그림 시스템 데이터베이스 (IAPS)에서 중립 자극의 집합. IAPS 이미지는 각성 및 가전 (11)의 수준을 반영하기 위해 1-9의 규모로 평가된다. 높은 숫자는 더 높은 긍정적 인 원자가과 각성을 나타냅니다. 뱀, 거미, 또는 다른 동물의 사진으로 연령에 적합한 연구 그룹에있는 이미지의 집합을 선택합니다. 참고 : 본 연구에 사용 된 태스크 관련이없는 혐오 자극 이미지는 3.38 (SD = 1.78)와 6.14 (SD = 2.08)의 평균 각성 등급의 평균 원자가 평가했다. 중립 자극 이미지는 6.21 (SD = 0.26)와 3.72 (SD = 2.15)의 평균 각성 등급의 평균 원자가 있었다. </li><li> 이미지는 500 밀리 1500 밀리 초 동안 의사 무작위 순서로 표시되도록 프로그램 태스크 스크립트 자극 간 간격을 의미한다. 현재 목표 자극과 태스크 관련이없는 중립적 인 이미지를 더 자주보다 매 15 초와는 자극의 각각의 약 4 %를합니다. 지혈역학 응답 함수의 더 나은 해상도를 제공하기 위하여 라 자세 히 이벤트 개시 시간. </li></ol><ol start="2"><li> 이미지의 8 세트, 참가자 모두 8 실행의 과정을 통해 40 목표와 40 태스크 관련이없는 중립적 인 이미지의 총되게됩니다 있도록 8 기능 실행의 각 하나를 만듭니다. </li></ol> 2. 참가자 설정 및 스캔 <ol><li> 정신병에 대한 가족의 위험이 높은 건강한 제어 개인 또는 중 하나 인 9 세에서 18 사이의 어린이와 청소년을 모집. <ol><li> 건강한 사람이 정신 질환을 앓고있는 어떤 정신 질환 또는 일급 가족이 없는지 확인하십시오. 가족 위험 참가자가 정신 분열증에 적어도 하나의 제 1도 상대 (부모 또는 형제 자매)가 있는지 확인합니다. 먼저도 친척의 다른 정신 질환의 존재를 배제하지 마십시오. </li><li> 가족 위험 그룹 참가자와 나이와 성별이 일치 건강한 참가자에스. </li></ol></li><li> 미성년자의 경우 18 세 이상 참가자의 동의를 획득 부모 / 법적 보호자로부터 동의를 획득. 또한, 연구에 참여하고있다 미성년자의 서면 동의를 획득. </li><li> 환경을 숙지하기 위해 모의 MRI 스캐너에서 참가자를 놓습니다. 스캐너 노이즈의 오디오 녹음을 재생하고 그들이 작업 지침을 이해했는지 확인하기 위해 행동 작업의 연습 실행을 완료해야합니다. </li><li> MRI 스캐너의 참가자를 놓고 필요한 뇌 현지화 검사 및 / 또는 해부학 적 영상을 획득. </li><li> MRI 안전 입력 상자를 사용하여 모든 대상 자극과 다른 자극에 대한 자신의 가운데 손가락에 다른 버튼에 대한 응답으로 자신의 검지 손가락으로 하나의 버튼을 눌러 참가자를 말한다. </li><li> fMRI를 스캔 후, 참가자의 하위 집합에서 연구에 사용 된 이미지에 대한 각성과 원자가의 주관적 평가를 수집합니다. CURRENT 연구는 15 컨트롤과 가족 위험이 높은 13 등급을 획득했습니다. </li></ol> 3. 이미지 인식 <ol><li> 3.0 테슬라 MRI 스캐너에 배치 할 참가자. TE, 5.16 밀리 초 : 2.04 밀리 초, FOV : 첫째, (TR 버릇 그라데이션 리콜 획득 펄스 시퀀스를 사용하여 해부학 T1 대비 이미지 동일 평면 3D를 포함한 구조 이미지 세트 취득 24cm를, 이미지 매트릭스 : 256 × 256, 플립 각도 : 20; 복셀의 크기 : 0.94 mm × 0.94 mm mm 1.9 × 68 축 조각). </li><li> 전체 뇌 따르면 그라데이션 에코 에코 평면 촬상 시퀀스를 이용하여 기능성 촬상 데이터를 취득 (TR : 2,000 밀리; TE : 27 밀리; FOV : 24cm, 화상 매트릭스 : 64 × 64; 각도 플립 : 60; 복셀의 크기 : 3.75 mm 3.8 mm × 3.75 mm × 34 축선 슬라이스)되도록 뇌 활성 행동 태스크의 수행 동안 측정 될 수있다. 행동 작업의 각 실행이 영상 시퀀스를 실행합니다. 각각의 실행은 120 이미징 시점으로 구성해야합니다. </li><li> 타 제시SK는 8 기능 실행에서, 각각 약 4 분 동안 지속. </li></ol> 4. 분석 <ol><li> 이미지 전처리 : FSL 12에서 열기의 fMRI 전문가 분석 도구 (FEAT). 첫 번째 수준의 분석 및 사전 통계를 선택합니다. <ol><li> &quot;데이터&quot;탭에서, 입력 이미지의 수를 선택하고 처리하려고하는 자기 공명 영상의 각각의 경로를 입력합니다. 출력 디렉토리를 설정합니다. 전체 볼륨, 폐기 인수의 수와 TR을 입력합니다. </li><li> &quot;사전 통계&quot;탭에서, 5 MMS에 MCFLIRT, FWHM을 부드럽게 공간에 모션 보정을 설정 와 &quot;슬라이스 타이밍 보정&quot;. &quot;BET 뇌 추출&quot;와 &quot;하이 패스&quot;시간 필터링을 선택하지만 B0의 뒤 틀어짐 방지를 선택하지 (당신이 그라데이션 필드 맵이 uness) 또는 &quot;나는 normaization을 ntensitiy.&quot;(12, 14). </li><li> &quot;등록&quot;탭에서 &quot;메인 STRU을 선택ctural 이미지 &quot;.. 피사체의 두개골 제거 T1 강조 영상의 경로를 입력 최소 6 DOF와 선형 일반 검색을 사용하십시오. 표준 공간 확인란을 선택합니다. MNI 아틀라스 이미지의 경로를 입력합니다. 보통의 선형을 사용하여 12 DOF.를 눌러 이동하여 검색 할 수 있습니다. </li><li> X, Y보다 큰 3mm 머리의 움직임, 또는 Z 방향으로 참가자를 제외합니다. </li></ol></li><li> 레벨 1 : 단일의 런 작업 조건 사이의 데이터를 비교. 열기 FEAT. &quot;첫 번째 수준의 분석&quot;과 &quot;통계 + 후 통계&quot;를 선택합니다. <ol><li> 데이터 탭에서의 입력 번호를 설정하고 MR 이미지의 각각의 경로를 입력한다. . &quot;출력 디렉토리&quot;에 대한 경로를 입력 &quot;전체 볼륨&quot;, 폐기 인수의 수와 TR을 입력합니다. </li><li> &quot;통계&quot;탭에서 &quot;사용 필름은 사전 백색 화&quot;체크 상자 (16)을 선택합니다. &quot;전체 모델을 누르면 그 자체TUP &quot;버튼을 누릅니다. 작업 조건의 수에 &quot;원래의 전기차 번호&quot;를 설정한다. 각 조건의 경우, 회선 &quot;드롭 다운 메뉴 17, 18&quot;에서 두 번 가마 HRF &quot;기본 모양의 드롭 다운 메뉴에서&quot;사용자 지정 (3 열 형식) &quot;을 선택하고 작업시기를 포함하는 텍스트 파일을 선택합니다. <ol><li> 주어진 유형의 각 &quot;이벤트&quot;에 대한 하나의 항목으로 3 열에서이 텍스트 파일을 포맷합니다. 첫 번째 열은 두 번째 시간 (초) 지속 기간을 포함한다 (초) 시작 시간을 포함해야하고, 세 번째 이벤트 중량을 함유한다. 대조 및 F-테스트 탭에서 한 각 작업 조건에 대한 대비와 각각 비교를 만듭니다. </li></ol></li><li> &quot;후 통계&quot;탭을 선택하고 &#39;형 임계 값 &quot;드롭 다운 메뉴에서&quot;클러스터 &quot;및&quot;Z 임계 값 &quot;및 클러스터 P (T)를 설정각각 2.3과 0.05 8,19에 hreshold. </li><li> &quot;등록&quot;탭에서. &quot;기본 구조 이미지&quot;를 선택 피사체의 두개골 제거 T1 강조 영상의 경로를 입력합니다. 최소 6 DOF와 선형 일반 검색을 사용합니다. &quot;표준 공간&quot;확인란을 선택합니다. MNI 아틀라스 이미지의 경로를 입력합니다. 12 DOF와 정상, 선형 검색을 사용합니다. 를 눌러 &quot;이동&quot;. </li></ol></li><li> 2 단계 : 각 작업 조건에 대해 실행 사이의 데이터를 비교. 열기 FEAT. 드롭 다운 메뉴에서 &quot;높은 수준의 분석&quot;과 &quot;통계 + 후 통계&quot;를 선택합니다. <ol><li> 데이터 탭에서 &quot;입력은 낮은 수준의 묘기 디렉토리입니다&quot;를 선택합니다. 입력의 수를 설정하고 자기 공명 영상의 각각의 경로를 입력합니다. &quot;출력 디렉토리&quot;에 대한 경로를 입력합니다. </li><li> &quot;통계&quot;탭에서 &quot;혼합 효과 : FLAME1&quot;변경 선택 상자하는 &quot;E 고정하기효과 적용 &quot;.을 눌러&quot;모델 설정 마법사 하나의 그룹 평균 &quot;버튼을 누릅니다. 선택&quot; &quot;를 클릭&quot;프로세스 &quot;버튼을 클릭합니다. </li><li> &quot;후 통계&quot;탭에서 &quot;형 임계 값&quot;드롭 다운 메뉴에서 &quot;클러스터&quot;를 선택하고 각각 2.3과 0.05 8,19에 &quot;Z 임계 값&quot;과 &quot;클러스터 P&quot;임계 값을 설정합니다. 를 눌러 &quot;이동&quot;. </li></ol></li><li> 레벨 3 : 모든 실행에서 각 작업 조건에 대한 주제 사이의 데이터를 비교. 열기 FEAT. 드롭 다운 메뉴에서 &quot;높은 수준의 분석&quot;과 &quot;통계 + 후 통계&quot;를 선택합니다. <ol><li> 데이터 탭에서 입력의 수를 설정합니다. &quot;입력은 FEAT 디렉토리에서 이미지를 극복 차원이다&quot;를 선택하고 자기 공명 영상의 각각의 경로를 입력합니다. &quot;출력 디렉토리&quot;에 대한 경로를 입력합니다. </li><li> &quot;통계&quot;T에AB, 눌러 &quot;전체 모델 설정&quot;. 각 EV 용 - 같은 진단 등 그룹, 나이, 성별, 각 주제에 대한 값 (입력 N 입력 1)을 입력으로 그룹 변수와 공변량의 수와 같은 전기 자동차의 수를 설정합니다. 이러한 값의 스프레드 시트를 복사하려면 &quot;붙여 넣기&quot;창을 사용할 수 있습니다. <ol><li> &quot;대조 및 F-테스트&quot;탭에서, 각 시험 변수에 대해 각각의 대비 (예를 들어, 진단 그룹)에 대한 대비를 추가 할 수 있습니다. 각각의 실험 변수에 대한 적절한 EV 아래 열의 값 하나를 선택하여 명암을 설정한다. 각각의 대비, 1 첫 번째 값과 -1로 두 번째를 설정합니다. &quot;완료&quot;를 선택합니다. </li></ol></li><li> &quot;후 통계&quot;탭을 선택하고 &#39;형 임계 값 &quot;드롭 다운 메뉴에서&quot;클러스터 &quot;를 각각 2.3과 0.05 8,19에&quot;Z 임계 값 &quot;과&quot;클러스터 P &quot;임계 값을 설정 </suP&gt;. 를 눌러 &quot;이동&quot;. </li></ol></li></ol>

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M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+robust+automated+brain+extraction.">Fast robust automated brain extraction.</a> Hum Brain Map. 17, 143-155 (2002).</li><li>Jenkinson, M., Bannister, P., Brady, M., Smith, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+optimization+for+the+robust+and+accurate+linear+registration+and+motion+correction+of+brain+images.">Improved optimization for the robust and accurate linear registration and motion correction of brain images.</a> Neuroimag. 17, 825-841 (2002).</li><li>Jenkinson, M., Smith, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+global+optimisation+method+for+robust+affine+registration+of+brain+images.">A global optimisation method for robust affine registration of brain images.</a> Med Image Ana. 5, 143-156 (2001).</li><li>Woolrich, M. 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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.

인구 통계 학적 특성 (20)에 따라 그룹 사이에 차이가 없었다. 행동 데이터는 표적 탐지 작업이 9-18 세 사이의 어린이와 청소년을위한 어려움이 적절한 수준에 있음을 지적했다. 현재의 연구에서 제대로 대상 (SD = 0.14)의 82.36 %를 식별하고 가족 위험군은 제대로 목표 (SD = 0.17)의 76.8 %를 식별 제어합니다. 두 그룹은 중립 사진에 비해 감정적 인 사진을 식별 할 때 정확성이 감소 하였다 (F (1,40) = 5.63, P = 0.03). 촬상 데이터는 실험 조건은 이그제큐티브 감정적 프로세싱 동안 채용 될 것으로 예상 지역의 상당한 활성화를 이끄는 것으로 나타났다. 정품 인증 대상 시험 중에 오른쪽 편도, 양측 안와 전두엽 피질, 방추형 피질과 시각 고전에서 전두엽, 전방 미상, 편협, 후방 정수리 영역에서 관찰되었다두 그룹 모두에서 혐오 시험 동안 광 케이블 (optical) 지역. 표 1은 각각의 조건에 대한 컨트롤이 크게 활성화의 영역을 보여줍니다. 이 패러다임은 또한 컨트롤과 정신 분열증에 대한 가족 위험이 높은 개인들 사이의 활성화에 유의 한 차이를 이끌어 냈다. 가족 위험이 높은 그룹은 자극을 대상으로 응답 전두 - 선조체 회로의 활성화를 감소했다. 컨트롤은, 대조적으로, 중간 및 뇌섬 이랑 정면에서 더 활성을 보였다. 조건 between 그룹 차이. 표 2 및도 2에 도시되어 가족 성 고 위험 집단은 또한 대상에 응답하여 제어 및 혐오 자극 (도 3)에 비해 연령과 관련된 활성화의 다른 패턴을 나타내었다. <img alt="그림 2" src="/files/ftp_upload/51484/51484fig2.jpg" /> 집단 간 차이 2. 활성화지도 그림. () 가족 위험이 높은 군 (N = 21)가 대상을 처리하는 동안 컨트롤 (N = 21)보다 더 큰 활성화를 보여 주었다 분야. CAUD = 미상; IFG = 열등한 정면 이랑; ITG = 열등한 시간 이랑. (B) 제어 대상을 처리하는 동안 가족 위험이 높은 그룹보다 더 큰 활성화를 보여 주었다 분야. INS = 섬엽; MFG = 중간 정면 이랑; MTG는 = 중​​간 시간 이랑. (C) 가족 위험이 높은 그룹이 혐오&gt; 중립 대비 동안 컨트롤보다 더 활성화 된 지역. COC = 중앙 아가미 피질. (D) 컨트롤이 혐오&gt; 중립 대비 동안 가족 위험이 높은 그룹보다 더 활성화 된 분야. ACC = 앞쪽에 cingulate 피질; PC = Precuneus. 이 수치는 하트 등의 알에서 수정되었습니다. (20), 허가. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51484/51484fig2large.jpg" target="_blank">이 F의 더 큰 버전을 보려면 여기를 클릭하십시오</a>igure. <img alt="그림 3" src="/files/ftp_upload/51484/51484fig3.jpg" /> 그림 3. 연령 관련 그룹 차이의 활성화지도. 대상을 처리하는 동안 컨트롤에 비해 가족 위험이 높은 그룹의 나이가 더 많은 양의 상관 관계 (A) 분야. ACC = 앞쪽에 cingulate 피질; INS = 섬엽; OFC = 안와 전두엽 피질; TH = 시상. 혐오&gt; 중립 대비 동안 가족 위험이 높은 군에 비해 대조군에서 나이가 더 많은 양의 상관 관계와 (B) 분야. IFG = 열등한 정면 이랑; PostCG = Postcentral 이랑; PreCG = Precentral 이랑. 이 그림은 권한, 하트 ​​등. (20)에서 수정되었습니다. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51484/51484fig3large.jpg" target="_blank">이 그림의 더 큰 버전을 보려면 여기를 클릭하십시오.</a> <table border="0" cellpadding="0" cellspacing="0"> &lt;TBODY&gt; <tr><td> (ST1). 내-그룹 활성화 초점 제어에 (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 좌표 </td><td></td><td></td></tr><tr><td> 부위 </td><td> 반구 </td><td> 엑스 </td><td> 와이 </td><td> 지 </td><td> 최대 Z 값 </td><td> 최대 p- 값 (1) </td></tr><tr><td> 대상 활성화 (P &lt;0.05, 허위 발견 속도 수정) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> 중간 정면 이랑 / 정면 극 </td><td> 비 </td><td> -30 </td><td> -2 </td><td> (50) </td><td> 5.57 </td><td> &lt;0.0000001 </td></tr><tr><td> 열등한 정면 이랑 </td><td> 비 </td><td> (46) </td><td> (12) </td><td> (32) </td><td> 5.41 </td><td> &lt;0.0000001 </td></tr><tr><td> 섬엽 </td><td> 비 </td><td> -32 </td><td> (24) </TD&gt; <td> 0 </td><td> 5.4 </td><td> &lt;0.0000001 </td></tr><tr><td> Precentral 이랑 </td><td> 비 </td><td> -40 </td><td> -22 </td><td> (48) </td><td> 5.53 </td><td> &lt;0.0000001 </td></tr><tr><td> 시상 </td><td> 비 </td><td> -12 </td><td> -16 </td><td> (12) </td><td> 5.03 </td><td> &lt;0.0000001 </td></tr><tr><td> 꼬리가있는 </td><td> 비 </td><td> -12 </td><td> (12) </td><td> 4 </td><td> 4.07 </td><td> 0.000003 </td></tr><tr><td> 피질 </td><td> 비 </td><td> (18) </td><td> 8 </td><td> (2) </td><td> 4.27 </td><td> 0.00009 </td></tr><tr><td> 앞쪽에 cingulate / Paracingulate 이랑 </td><td> 비 </td><td> 0 </td><td> (12) </td><td> (46) </td><td> 5.6 </td><td> &lt;0.0000001 </td></tr><tr><td> 후방에 cingulate 이랑 </td><td> 비 </td><td> 8 </td><td> -16 </td><td> (28) </td><td> 5.2 </td><td> &lt;0.0000001 </td></tr><tr><td> 수 페리 어 / 중간 시간 이랑 </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> 방추형 / 열등 시간 이랑 </td><td> 비 </td><td> -30 </td><td> -50 </td><td> -12 </td><td> 5.64 </td><td> &lt;0.0000001 </td></tr><tr><td> 우수한 정수리 소엽 / Supramarginal 이랑 / Postcentral 이랑 </td><td> 비 </td><td> (30) </td><td> -44 </td><td> (44) </td><td> (6) </td><td> &lt;0.0000001 </td></tr><tr><td> 측면 후두 피질 </td><td> 비 </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> 혐오&gt; 중립 활성화 (P &lt;0.05, 허위 발견 속도는 수정) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> 열등한 정면 이랑 </td><td> 엘 </td><td> -44 </td><td> (14) </TD&gt; <td> (14) </td><td> 3.16 </td><td> 0.0004 </td></tr><tr><td> 정면 극 / 내측 정면 피질 </td><td> 비 </td><td> -2 </td><td> (64) </td><td> 0 </td><td> 3.42 </td><td> 0.0005 </td></tr><tr><td> Postcentral 이랑 </td><td> 엘 </td><td> -62 </td><td> -22 </td><td> (34) </td><td> 3.12 </td><td> 0.0004 </td></tr><tr><td> 앞쪽에 cingulate 피질 </td><td> 비 </td><td> -4 </td><td> (34) </td><td> 8 </td><td> 3.27 </td><td> 0.0002 </td></tr><tr><td> 후방에 cingulate 이랑 </td><td> 비 </td><td> 0 </td><td> -44 </td><td> (28) </td><td> 3.26 </td><td> 0.0002 </td></tr><tr><td> 열등한 시간 / 방추형 이랑 </td><td> 비 </td><td> -44 </td><td> -44 </td><td> -14 </td><td> 3.03 </td><td> 0.0006 </td></tr><tr><td> 각회 </td><td> 비 </td><td> (46) </td><td> -64 </td><td> 8 </td><td> 3.42 </td><td> 0.0001 </td></tr><tr><td> Supramarginal 이랑 </td><td> 엘 </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> 혐오 활성화 (P &lt;0.05, 허위 발견 속도 수정) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> 편도 </td><td> 아르 자형 </td><td> (22) </td><td> -4 </td><td> -18 </td><td> 2.86 </td><td> 0.001 </td></tr><tr><td> 안와 전두엽 피질 / 섬엽 </td><td> 비 </td><td> (36) </td><td> (22) </td><td> -4 </td><td> 4.93 </td><td> &lt;0.0000001 </td></tr><tr><td> 중간 정면 이랑 </td><td> 비 </td><td> (32) </td><td> 4 </td><td> (40) </td><td> 4.7 </td><td> &lt;0.0000001 </td></tr><tr><td> 정면 극 </td><td> 비 </td><td> -38 </td><td> (36) </td><td> (10) </td><td> 4.95 </td><td> &lt;0.0000001 </td></tr><tr><td> 앞쪽에 cingulate /paracingulate 이랑 </td><td> 비 </td><td> (6) </td><td> (16) </td><td> (50) </td><td> 4.85 </td><td> &lt;0.0000001 </td></tr><tr><td> 후방에 cingulate 이랑 </td><td> 비 </td><td> (2) </td><td> -28 </td><td> (24) </td><td> 5.88 </td><td> &lt;0.0000001 </td></tr><tr><td> 시상 </td><td> 비 </td><td> (18) </td><td> -26 </td><td> (2) </td><td> 5.44 </td><td> &lt;0.0000001 </td></tr><tr><td> Precentral 이랑 </td><td> 비 </td><td> -44 </td><td> 8 </td><td> (34) </td><td> 4.54 </td><td> &lt;0.0000001 </td></tr><tr><td> 우수한 정수리 소엽 </td><td> 비 </td><td> -20 </td><td> -56 </td><td> (54) </td><td> 6.05 </td><td> &lt;0.0000001 </td></tr><tr><td> 측면 후두 피질 </td><td> 비 </td><td> -36 </td><td> -82 </td><td> 4 </td><td> 6.05 </td><td> &lt;0.0000001 </td></tr><tr><td> 후두 극 </td><td> 비 </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, 양자 </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)보고 P-값의 루즈 벨트 보정 값 &lt;0.05에서 유의, 보정입니다 </td></tr></tbody></table> 컨트롤 (N = 21) 중 집단 내 활성화 초점 표 1..이 테이블은 허가, 하트 등. (20)에서 수정되었습니다. <table border="0" cellpadding="0" cellspacing="0"><tbody><tr><td> 표 2. 집단 간 활성화의 차이 </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td colspan="3"> MNI 좌표 </td><td></td><td></td></TR&gt; <tr><td></td><td> 반구 </td><td> 엑스 </td><td> 와이 </td><td> 지 </td><td> 최대 Z 값 </td><td> 최대 p- 값 (1) </td></tr><tr><td> 대상 </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> 가족 성 고 위험&gt; 제어 (P &lt;0.05, 허위 발견 속도 수정) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> 정면 극 </td><td> 비 </td><td> (16) </td><td> (76) </td><td> (6) </td><td> 3.52 </td><td> 0.00007 </td></tr><tr><td> 열등한 정면 이랑 </td><td> 엘 </td><td> -58 </td><td> (16) </td><td> (18) </td><td> 3.37 </td><td> 0.0001 </td></tr><tr><td> 꼬리가있는 </td><td> 비 </td><td> -14 </td><td> (20) </td><td> (10) </td><td> 3.2 </td><td> 0.0003 </td></tr><tr><td> 열등한 시간 이랑 </td><td> 엘 </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> 컨트롤&gt; 가족 성 위험이 높은 (P &lt;0.05, 허위 발견 속도는 수정) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> 중간 정면 이랑 / Precentral 이랑 </td><td> 아르 자형 </td><td> (48) </td><td> 8 </td><td> (34) </td><td> 3 </td><td> 0.0007 </td></tr><tr><td> 정면 아가미 피질 </td><td> 엘 </td><td> -46 </td><td> (16) </td><td> -4 </td><td> 2.94 </td><td> 0.0009 </td></tr><tr><td> 보조 운동 영역 </td><td> 아르 자형 </td><td> (18) </td><td> -16 </td><td> (40) </td><td> 3.02 </td><td> 0.0007 </td></tr><tr><td> 섬엽 </td><td> 엘 </td><td> -34 </td><td> -18 </td><td> 4 </td><td> 2.94 </td><td> 0.0009 </td></tr><tr><td> Precentral 이랑 </td><td> 비 </td><td> (10) </td><td> -26 </td><td> (60) </td&gt; <td> 3.29 </td><td> 0.0002 </td></tr><tr><td> Postcentral 이랑 </td><td> 비 </td><td> (14) </td><td> -38 </td><td> (54) </td><td> 3.57 </td><td> 0.0001 </td></tr><tr><td> 우수한 시간적 이랑 </td><td> 아르 자형 </td><td> (54) </td><td> -6 </td><td> -4 </td><td> 3.18 </td><td> 0.0003 </td></tr><tr><td> 중간 시간 이랑 </td><td> 아르 자형 </td><td> (48) </td><td> -46 </td><td> 8 </td><td> 3.65 </td><td> 0.00004 </td></tr><tr><td> Precuneus </td><td> 아르 자형 </td><td> (2) </td><td> -40 </td><td> (46) </td><td> 2.89 </td><td> 0.001 </td></tr><tr><td> 측면 후두 피질 </td><td> 비 </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> 혐오 - 중립 </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><tD&gt; 가족 성 위험이 높은&gt; 제어 (P &lt;0.05, 허위 발견 속도는 수정) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> 중앙 아가미 피질 </td><td> 아르 자형 </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> 컨트롤&gt; 가족 성 위험이 높은 (P &lt;0.05, 허위 발견 속도는 수정) </td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td> 앞쪽에 cingulate 피질 </td><td> 엘 </td><td> -6 </td><td> (38) </td><td> 8 </td><td> 2.68 </td><td> 0.002 </td></tr><tr><td> Precuneus </td><td> 엘 </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, 양자 </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)보고 P-값의 루즈 벨트 보정 값 &lt;0.05에서 유의, 보정입니다 </td></tr></tbody></table> 표 2. 집단 간 활성화 초점의 차이.이 테이블은 허가, 하트 등. (20)에서 수정되었습니다.

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.

정신 분열증에 대한 가족 성 위험이 높은 아동의 정서적 괴짜 작업을 사용하여 이마 관자 - 변연계의 활동의 측정

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

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JoVE Journal

Behavior

8.8K 조회수.  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.

비디오: 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.

평가 및 고위험 신생아의 평가 : 신생아 중환자 실 네트워크 신경 행동 학적 규모

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

연구

행동분석학

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

필적 분석은 정신병에 대한 높은 위험 신경 이완제 나이브 청소년의 자발적인 운동 장애를 나타냅니다

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.

장애 아동의 대뇌 피질의 청각 - 촉각 처리의 정량 평가

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.

아이들의 고밀도 EEG 레코딩의 대뇌 피질의 자료 분석

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.

의 fMRI 연구의 설계 및 구현은 젊은 여성 진압 생각하고, 위험에 처한, 우울증에 대한 검사

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.

숙련 된 앞다리가 쥐에서 작업을 도달하는 고정밀의 취득

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.

지연하는 조정 금액 태스크를 사용하여 인간에로 할인 측정

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.

감정적 인 스트 루프 작업은 : 감정적 콘텐츠에 대한 노출에서인지 성능 평가

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.

통합, 멀티 모달 통증 작업 배터리를 사용하여 통증 탐지 및 공차 임계 값을 결정

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.

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