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 - Die Forschungstechniken in dieser Studie verwendet wurden durch die Institutional Review Boards (IRB) der Duke University und der University of North Carolina genehmigt. 1. Imaging Task-Entwurf <ol><li> Generieren Sie einen ereignisbasierten Verhaltens Aufgabe, seltene Zielreize (ein Kreis) innerhalb einer Sequenz von mehr häufige Standardreizen (Rührei Bilder) präsentiert. Eine schematische Darstellung der Aufgabe ist in Abbildung 1 dargestellt. Präsentieren Sie die Aufgabe mit Cigal Software 10. </li></ol><img alt="Abbildung 1" src="/files/ftp_upload/51484/51484fig1.jpg" /> Abbildung 1. Schematische der Task-Entwurf. Diese Zahl wurde von Hart et al. 20 geändert wurden, mit Genehmigung. <a href="https://www.jove.com/files/ftp_upload/51484/51484fig1large.jpg" target="_blank">Bitte klicken Sie hier, um eine größere Version dieser Figur zu sehen.</a> <ol style="margin-left: 40px;"><li> Wählen Sie, wieet des aversiven Reizen und eine Reihe von neutralen Stimuli vom internationalen Affective Picture System-Datenbank (IAPS). IAPS Bilder werden auf einer Skala von 1-9, um Ebenen der Erregung und Valenz 11 reflektieren bewertet. Hohe Zahlen zeigen höhere positive Valenz und Erregung. Wählen Sie eine Reihe von Bildern, die altersgerecht an die Studiengruppe sind, wie beispielsweise Bilder von Schlangen, Spinnen oder andere Tiere. Hinweis: Die Stimuli Bilder aufgaben irrelevant aversiven für diese Studie verwendet hatte eine durchschnittliche Wertigkeit Rating von 3,38 (SD = 1,78) und einer mittleren Erregung Rating von 6,14 (SD = 2.08). Die neutrale Reize Bilder hatten eine durchschnittliche Wertigkeit von 6,21 (SD = 0,26) und einer mittleren Erregung Rating von 3,72 (SD = 2.15). </li><li> Programm der Task-Skript, so dass Bilder in einer pseudo-randomisiert Auftrag für 1.500 ms mit 500 ms präsentiert bedeuten Inter-Stimulus-Intervall. Gegenwärtige Zielreize und aufgaben irrelevant neutrale Bilder nicht häufiger als alle 15 Sekunden und stellen jeweils etwa 4% der Reize. Jitter Ereignis Einsetzen mal, um eine bessere Auflösung der hämodynamischen Antwort Funktion bereitzustellen. </li></ol><ol start="2"><li> Erstellen 8 Sätze von Bildern, eine für jeden der 8 funktionelle so verläuft, dass die Teilnehmer mit einem Gesamtvolumen von 40 Zielen und 40 aufgaben irrelevant neutralen Bilder im Verlauf aller 8 verläuft dargestellt. </li></ol> 2. Teilnehmer einrichten und Scannen <ol><li> Rekrutieren Kinder und Jugendliche im Alter von 9 und 18 Jahren, die entweder gesunden Kontrollpersonen oder Personen, die bei familiären hohes Risiko für Psychosen sind zwischen. <ol><li> Stellen Sie sicher, dass gesunde Menschen haben keine psychiatrischen Erkrankungen oder jegliche ersten Grades Familienmitglieder mit einer psychiatrischen Erkrankung. Stellen Sie sicher, dass familiäres Risiko Teilnehmer mindestens einen Verwandten ersten Grades (Eltern oder Geschwister) mit Schizophrenie haben. Auf das Vorhandensein von anderen psychiatrischen Erkrankungen im ersten Grades ausschließen Sie sie nicht. </li><li> Alter und Geschlecht Spiel gesunde Teilnehmer mit familiärer Risikogruppe Teilnehmers. </li></ol></li><li> Erwerben Sie informierten Einwilligung der Teilnehmer im Alter von über 18. Bei Minderjährigen erwerben informierte Einwilligung von den Eltern / Erziehungsberechtigten. Zusätzlich erwerben schriftliche Zustimmung von Minderjährigen, die nehmen an der Studie teil. </li><li> Legen Sie die Teilnehmer in einem nachgebauten MRI-Scanner, um sie mit der Umgebung vertraut zu machen. Spielen Sie eine Audio-Aufnahme von Scannerrauschen und haben ihnen einen Probelauf des Verhaltens Aufgabe abzuschließen, um sicherzustellen, dass sie die Aufgabe, Anweisungen zu verstehen. </li><li> Legen Sie die Teilnehmer in der MRI-Scanner und die erforderlichen Gehirn Lokalisierung Scans und / oder anatomische Bilder zu erwerben. </li><li> Mit Hilfe eines MRI-sicheren Eingabefeld, sagen die Teilnehmer auf eine Schaltfläche mit dem Zeigefinger in Reaktion auf alle Zielreize und eine andere Taste mit ihren Mittelfinger für alle anderen Reize zu drücken. </li><li> Nach fMRI Scannen, sammeln subjektive Bewertungen von Erregung und Valenz für die in der Studie von einer Untergruppe von Teilnehmern verwendeten Bilder. Die current-Studie erhalten Bewertungen 15 Kontrollen und 13 mit familiärer hohes Risiko. </li></ol> 3. Image Acquisition <ol><li> Platz Teilnehmer in einen 3,0-Tesla-MRT. Zunächst erwerben eine Reihe von Strukturbildern einschließlich einer 3D-Bild anatomischen T1-Kontrast coplanar mit einem verdorbenen gradienten-wiederAkquisitionsPulsSequenz (TR: 5,16 ms, TE: 2,04 ms; FOV: 24 cm; Bildmatrix: 256 × 256; Flip Winkel: 20; Voxelgröße: 0,94 mm x 0,94 mm x 1,9 mm; 68 axiale Schichten). </li><li> Erwerben funktionelle Bildgebung Daten unter Verwendung eines Gradienten-Echo-Echo-Planar-Bildgebungssequenz mit voller Hirn Abdeckung (TR: 2.000 ms, TE: 27 ms; FOV: 24 cm; Bildmatrix: 64 × 64; Flip-Winkel: 60; Voxelgröße: 3.75 mm × 3,75 mm × 3,8 mm; 34 axiale Schichten), so dass die Gehirnaktivität während der Leistung des Verhaltens Aufgabe gemessen werden. Führen Sie dieses Bildgebungssequenz für jeden Durchlauf des Verhaltens Aufgabe. Jeder Durchlauf sollte von 120 Bildgebungszeitpunkten bestehen. </li><li> Präsentieren Sie die task in 8 Funktionsläufe von jeweils ca. 4 min. </li></ol> 4. Analysen <ol><li> Bildverarbeitung: Open fMRI Expert Analysis Tool (FEAT) in FSL 12. Wählen Sie zuerst Ebenenanalyse und Pre-stats. <ol><li> Auf der Registerkarte &quot;Daten&quot;, wählen Sie die Anzahl der Eingabebilder und geben Sie den Pfad zu jedem der MR-Bilder, die Sie gehen, um zu verarbeiten sind. Stellen Sie das Ausgabeverzeichnis. Geben Sie die Gesamtmengen, die Anzahl der verworfen Übernahmen und die TR. </li><li> Auf der Registerkarte &quot;Pre-Statistik&quot;, stellen Bewegungskorrektur auf MCFLIRT, räumliche Glättung FWHM bis 5 mm, und &quot;Slice-Zeitkorrektur&quot;. Wählen Sie &quot;BET Gehirn-Extraktion&quot; und &quot;Highpass&quot; zeitliche Filterung aber nicht B0 unwarping wählen (Uness Sie ein Gradientenfeld Karte haben) oder &quot;Ich ntensitiy normaization&quot;. 12,14. </li><li> Auf der Registerkarte &quot;Registrierung&quot;, wählen Sie &quot;Haupt structural Bild &quot;. Geben Sie den Pfad Schädel-abgestreift T1-gewichteten Bild der Person. Nutzen Sie eine lineare normalen Suche mit mindestens 6 DOF. Wählen Sie den Platzkontrollkästchen Norm. Geben Sie den Pfad zu der MNI Bildatlas. Verwenden Sie einen normalen, linearen Suche mit 12 DOF. Drücken Sie Fortfahren. </li><li> Teilnehmer mit mehr als 3 mm Kopfbewegung in der X-, Y- oder Z-Richtung aus. </li></ol></li><li> Ebene 1: Daten zwischen Aufgabe Bedingungen Vergleichen in einem einzigen Durchlauf. Offene FEAT. Wählen Sie &quot;First-Level-Analyse&quot; und &quot;Statistiken + Post-Statistik&quot;. <ol><li> Auf der Registerkarte Daten, stellen Sie die Anzahl der Ein- und geben Sie den Pfad zu jedem der MR-Bilder. Geben Sie einen Pfad für die &quot;Ausgabeverzeichnis&quot;. Geben Sie die &quot;Gesamtvolumen&quot;, die Anzahl der verworfen Übernahmen und die TR. </li><li> Auf der Registerkarte &quot;Statistik&quot;, wählen Sie das Kontrollkästchen &quot;FILM prewhitening&quot; 16. Drücken Sie die &quot;Full-Modell setup &quot;-Taste. Stellen Sie die &quot;Anzahl der Original EVs&quot; auf die Anzahl der Aufgabenbedingungen. Für jede Bedingung, wählen Sie &quot;Benutzerdefiniert (3 Spalten-Format)&quot; aus dem Drop-Down-Menü Grundform und &quot;Double-Gama HRF von der&quot; Faltung &quot;Dropdown-Menü 17,18 und wählen Sie eine Textdatei mit der Aufgabe Timing. <ol><li> Formatieren Sie diese Textdatei in 3 Spalten mit einem Eintrag für jedes &quot;Ereignis&quot; des angegebenen Typs. Die erste Spalte sollte den Beginn der Zeit (in Sekunden) enthalten, sollte der zweite die Dauer (in Sekunden) enthalten, und der dritte sollte die Veranstaltung Gewicht enthalten. Auf der Registerkarte Contrasts &amp; F-Tests, erstellen Sie einen Kontrast für jede Aufgabe Zustand und eine für jeden Vergleich. </li></ol></li><li> Auf den &quot;Post-Statistik&quot;, wählen Sie &quot;Cluster&quot; auf dem &quot;Thresholding&quot; Dropdown-Menü und stellen Sie die &quot;Z Grenzwert&quot; und Cluster P threshold bis 2,3 bzw. 0,05 8,19. </li><li> Auf der Registerkarte &quot;Registrierung&quot;, wählen Sie &quot;Hauptstrukturbild&quot;. Geben Sie den Pfad Schädel-abgestreift T1-gewichteten Bild des Subjekts. Verwenden Sie einen linearen normalen Suche mit mindestens 6 DOF. Wählen Sie den &quot;Standard space&quot; Kontrollkästchen. Geben Sie den Pfad zu der MNI Bildatlas. Verwenden Sie eine normale, lineare Suche mit 12 DOF. Drücken Sie auf &quot;Go&quot;. </li></ol></li><li> Stufe 2: Vergleichsdaten zwischen den Läufen für jeden Task Zustand. Offene FEAT. Wählen Sie &quot;Übergeordnete Analyse&quot; und &quot;Statistiken + Post-Statistiken&quot; aus dem Dropdown-Menü. <ol><li> Auf der Registerkarte Daten, wählen Sie &quot;Eingänge sind untergeordnete FEAT-Verzeichnisse&quot;. Stellen Sie die Anzahl der Ein- und geben Sie den Pfad zu jedem der MR-Bilder. Geben Sie einen Pfad für die &quot;Ausgabeverzeichnis&quot;. </li><li> Auf der Registerkarte &quot;Statistik&quot;, ändern Sie die &quot;Mixed Effects: flame1&quot; Auswahlfeld &quot;Fixed EUSWIRKUNGEN &quot;. Drücken Sie die&quot; Model Setup-Assistent &quot;klicken. Wählen Sie&quot; Einzelgruppendurchschnitt &quot;und klicken Sie auf&quot; Bearbeiten &quot;klicken. </li><li> Auf der Registerkarte &quot;Post-Statistik&quot;, wählen Sie &quot;Cluster&quot; auf dem &quot;Thresholding&quot; Dropdown-Menü und stellen Sie die &quot;Z Grenzwert&quot; und &quot;Cluster P&quot; Schwellenwert auf 2,3 und 0,05 bzw. 8,19. Drücken Sie auf &quot;Go&quot;. </li></ol></li><li> Stufe 3: Vergleichsdaten zwischen Fächern für jede Aufgabe Zustand in allen Läufen. Offene FEAT. Wählen Sie &quot;Übergeordnete Analyse&quot; und &quot;Statistiken + Post-Statistiken&quot; aus dem Dropdown-Menü. <ol><li> Auf der Registerkarte Daten, wählen Sie &quot;Eingänge sind 3D bewältigen Bilder von FEAT-Verzeichnisse.&quot; Stellen Sie die Anzahl der Ein- und geben Sie den Pfad zu jedem der MR-Bilder. Geben Sie einen Pfad für die &quot;Ausgabeverzeichnis&quot;. </li><li> Auf der &quot;Statistik&quot; tab, Drücken Sie die &quot;Full-Modell-Setup&quot;. Stellen Sie die Anzahl von Elektrofahrzeugen, die der Anzahl der Gruppengrößen und Kovariaten wie Diagnosegruppe, Alter, Geschlecht, usw. Geben Sie die Werte für jedes Fach (Eingang 1 - Eingang n) für jeden EV. Sie können das Fenster &quot;Einfügen&quot;, um eine Tabelle dieser Werte kopieren. <ol><li> Auf der Registerkarte &quot;Kontraste &amp; F-Tests&quot;, fügen Sie einen Kontrast für jeden Test variabel und für jeden Kontrast (zB Diagnosegruppe). Für jede Testvariable, stellen Sie den Kontrast, indem Sie den Wert 1 in der Spalte unter dem entsprechenden EV. Für jeden Kontrast, stellen Sie den ersten Wert auf 1 und die zweite auf -1. Wählen Sie &quot;Fertig&quot;. </li></ol></li><li> Auf der Registerkarte &quot;Post-Statistik&quot;, wählen Sie &quot;Cluster&quot; auf dem &quot;Thresholding&quot; Dropdown-Menü und stellen Sie die &quot;Z Grenzwert&quot; und &quot;Cluster P&quot; Schwellenwert auf 2,3 und 0,05 bzw. 8,19 </sup&gt;. Drücken Sie auf &quot;Go&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=Advances+in+functional+and+structural+MR+image+analysis+and+implementation+as+FSL.">Advances in functional and structural MR image analysis and implementation as FSL.</a> Neuroimag. 23, 208-219 (2004).</li><li>Smith, S. 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J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Altered+fronto-limbic+activity+in+children+and+adolescents+with+familial+high+risk+for+schizophrenia..">Altered fronto-limbic activity in children and adolescents with familial high risk for schizophrenia..</a> Psychiatry Re. 212, 19-27 (2013).</li><li>Hariri, A. R., Bookheimer, S. Y., Mazziotta, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modulating+emotional+responses:+effects+of+a+neocortical+network+on+the+limbic+system.">Modulating emotional responses: effects of a neocortical network on the limbic system.</a> Neurorepor. 11, 43-48 (2000).</li><li>Gottesman, I. I., Gould, T. 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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. 
<p class='jove_conte...

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.

Es gab keine Unterschiede zwischen den Gruppen auf Basis von demografischen Merkmalen 20. Verhaltensdaten zeigten, dass die Zielerfassung Aufgabe ist bei einer geeigneten Schwierigkeitsgrad für Kinder und Jugendliche im Alter von 9 bis 18 zwischen. In der aktuellen Studie, Bedienungselemente richtig 82,36% der Ziele (SD = 0,14) identifiziert und die familiäre Risikogruppe 76,8% der Ziele (SD = 0.17) korrekt identifiziert. Beide Gruppen zeigten verringerte Genauigkeit bei der Identifizierun...

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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.

Die Messung der Fronto-limbischen Aktivität unter Verwendung einer emotionalen Oddball Aufgabe bei Kindern mit familiärer High Risk for Schizophrenia

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

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8.8K Aufrufe.  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.

Serie: 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.

Bewertung und Evaluierung der High Risk Neugeborene: Die Neugeborenen-Intensivstation Netzwerk Neuropsychologische Skala

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

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

Handwriting Analysis Zeigt Spontane Dyskinesien in neuroleptisches Naive Jugendliche mit hohem Risiko für Psychosen

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

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.

Kortikale Quelle Analyse von High-Density-Aufnahmen EEG bei Kindern

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.

Entwurf und Implementierung einer fMRI Studie, Gedankenunterdrückung in junge Frauen mit und at-Risk für Depression

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

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.

Messverzögerung Diskontierung in Humans Mit einem Einstellbetrag 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 ...

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.

Die emotionale Stroop Aufgabe: Bewertung der kognitiven Leistung unter Einwirkung von emotionalen Inhalt

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.

Die Bestimmung Schmerzerkennung und Toleranzschwellen mit Hilfe eines integrierten, Multi-Modal Schmerz Task-Batterie

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

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Sentence comprehension relies on the ability to rapidly integrate different types of linguistic and non-linguistic information. However, there is currently a paucity of research exploring how preschool children with autism understand sentences using different types of cues. The mechanisms underlying sentence comprehension remains largely unclear. The present study presents a protocol to examine the sentence comprehension abilities of preschool children with autism. More specifically, a visual world paradigm of eye-tracking is used to explore the moment-to-moment sentence comprehension in the children. The paradigm has multiple advantages. First, it is sensitive to the time course of sentence comprehension and thus can provide rich information about how sentence comprehension unfolds over time. Second, it requires minimal task and communication demands, so it is ideal for testing children with autism. To further minimize the computational burden of children, the present study measures eye movements that arise as automatic responses to linguistic input rather than measuring eye movements that accompany conscious responses to spoken instructions.

We present a protocol to examine the use of morphological cues during real-time sentence comprehension by children with autism.

Mit Hilfe der visuellen Welt Paradigma Satz Verständnis in Mandarin-sprachigen Kindern mit Autismus zu studieren

Sentence comprehension relies on the ability to rapidly integrate different types of linguistic and non-linguistic information. However, there is ...

Impact of High-intensity Interval Exercise and Moderate-Intensity Continuous Exercise on the Cardiac Troponin T Level at an Early Stage of Training

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has been described. The exercise-induced cTnT response distorts the diagnostic role of the cTnT assay. Although high-intensity interval exercise (HIE) is growing in popularity and concerns remain about its safety, available data related to cTnT release after HIE is limited, which hampers the use of HIE as a health intervention. Here, we present three representative HIE protocols [traditional HIE (repeated 4 min cycling at 90% V&#775;O2max interspersed with 3 min rest, 200 kJ/session); sprint interval exercise (SIE, repeated 1 min cycling at 120% V&#775;O2max interspersed with 1.5 min rest, 200 kJ/session); and repeated sprint exercise (RSE, 40 x 6 s all-out sprints interspersed with 9 s rest)] and one representative MCE protocol (continuous cycling exercise at an intensity of 60% V&#775;O2max, 200 kJ/session). Forty-seven sedentary, overweight young women were randomly assigned to one of four groups (HIE, SIE, RSE, and MCE). Six bouts of respective exercise were performed by every single group, with each being 48 h apart. Meanwhile, for four groups, the duration of the entire testing period was identical, being 10 days. Before and after the first and final exercise bouts, an assessment was conducted of cTnT. The current study provides a frame of reference giving a clear picture of how a specific exercise session affects the circulating cTnT concentration at the early stage of training. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise, especially for HIE.

Here, we present protocols of high-intensity interval and moderate-intensity continuous exercise to observe the response of circulating cardiac troponin T (cTnT) concentration to acute exercise over 10 days. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise.

Auswirkungen von High-Intensity-Intervall-Übung und moderate Intensität kontinuierliche Übung auf dem kardialen Troponin T-Niveau in einem frühen Stadium des Trainings

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has ...

A Within-Subject Experimental Design using an Object Location Task in Rats

Object place recognition is a prominent method used to investigate spatial memory in rodents. This object place recognition memory forms the basis of the object location task. This paper provides an extensive protocol to guide the establishment of an object location task with the option of up to four repetitions using the same cohort of rats. Both weak and strong encoding protocols can be used to study short- and long-term spatial memories of varying strength and to enable the implementation of relevant memory-inhibiting or -enhancing manipulations. In addition, repetition of the test with the counterbalancing presented here allows the combination of results from two or more tests for within-subject comparison to reduce variability between rats. This method helps to increase statistical power and is strongly recommended, particularly when running experiments that produce high variation in individual behavior. This directly refines the study by increasing the data obtained from each animal and reducing the overall number of animals needed.&#160;Finally, implementation of the repeated object location task increases the efficiency of studies that involve surgical procedures by saving time and labor.

This protocol provides detailed steps for an object location task with four repetitions using the same cohort of rats. Weak and strong encoding can produce short- and long-term memories. The flexibility of the protocol with repetition can be beneficial for studies involving surgical operations by saving time and labor.

Ein experimentelles Design innerhalb des Subjekts mit einer Objektortungsaufgabe in Ratten

Object place recognition is a prominent method used to investigate spatial memory in rodents. This object place recognition memory forms the basis of the ...