identification-disease-related-spatial-covariance-patterns-using

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

The Feinstein Institute for Medical Research

Striatal-to-occipital ratio (SOR) and influx constant K(i)(occ) are commonly used as analytic parameters in L-3,4-dihydroxy-6-(18)F-fluorophenylalanine (FDOPA) PET studies. Both have been shown to be useful in discriminating Parkinson's disease (PD) patients from healthy subjects. We evaluated the relative performance of SOR and influx constant (K(i)(occ)) in the clinical assessment of nigrostriatal dopaminergic function in PD.

Comparative analysis of striatal FDOPA uptake in Parkinson's disease: ratio method versus graphical approach.

Ratio striatum-&agrave;-occipital (ROS) et constante de l&#39;afflux K(i)(occ) sont utilis&eacute;es comme param&egrave;tres analytiques dans les &eacute;tudes de L-3,4-dihydroxy-6-(18)F-fluorophenylalanine (FDOPA) PET. Tous deux auraient d&ucirc; &ecirc;tre divulgu&eacute;s utiles pour distinguer la maladie de Parkinson (PD) patients provenant de sujets sains. Nous avons &eacute;valu&eacute; la performance relative de SOR et afflux constant (K(i)(occ)) dans l&#39;&eacute;valuation clinique de la fonction dopaminergique sont dans PD.

Analyse comparative d&#39;absorption FDOPA striatale dans la maladie de Parkinson : m&eacute;thode du ratio versus approche graphique.

Rapporto di striatal-occipitale (SOR) e costante afflusso K(i)(occ) sono comunemente usati come parametri analitici negli studi di L-3,4-dihydroxy-6-(18)F-fluorophenylalanine (FDOPA) PET. Entrambi hanno dimostrati di essere utile in acute della malattia di Parkinson (PD) i pazienti da soggetti sani. Abbiamo valutato la performance relativa di SOR e afflusso costante (K(i)(occ)) nella valutazione clinica della funzione dopaminergica Sonografia nel PD.

Analisi comparativa di assorbimento FDOPA striatal nella malattia di Parkinson: Metodo ratio versus approccio grafico.

Abbiamo testato l&#39;ipotesi che il genotipo DYT1 &egrave; associato a un disturbo di connettivit&agrave; anatomica che coinvolgono principalmente la corteccia sensitivo-motoria. Abbiamo usato diffusion tensor imaging a risonanza magnetica (DTI) per valutare la microstruttura di materia bianca percorsi in portatori di mutazione e soggetti di controllo. Anisotropia frazionale (FA), una misura di axonal integrit&agrave; e coerenza, &egrave; stato ridotto (p &lt; 0,005) in subgyral materia bianca della corteccia sensitivo-motoria dei vettori DYT1. Connettivit&agrave; Anatomica anormale dell&#39;area motore supplementare pu&ograve; contribuire la suscettibilit&agrave; dei vettori DYT1 sviluppare manifestazioni cliniche della distonia.

Cambiamenti microstrutturali materia bianca nei portatori della mutazione del gene DYT1.

We tested the hypothesis that the DYT1 genotype is associated with a disorder of anatomical connectivity involving primarily the sensorimotor cortex. We used diffusion tensor magnetic resonance imaging (DTI) to assess the microstructure of white matter pathways in mutation carriers and control subjects. Fractional anisotropy (FA), a measure of axonal integrity and coherence, was reduced (p < 0.005) in the subgyral white matter of the sensorimotor cortex of DYT1 carriers. Abnormal anatomical connectivity of the supplementary motor area may contribute to the susceptibility of DYT1 carriers to develop clinical manifestations of dystonia.

Microstructural white matter changes in carriers of the DYT1 gene mutation.

Nous avons test&eacute; l&#39;hypoth&egrave;se que le g&eacute;notype DYT1 est associ&eacute; &agrave; un trouble de la connectivit&eacute; anatomique impliquant principalement le cortex sensorimoteur. Nous avons utilis&eacute; la diffusion tensor magnetic resonance imaging (DTI) pour &eacute;valuer la microstructure des voies de la substance blanche en porteurs de la mutation et de sujets t&eacute;moins. Anisotropie fractionnaire (FA), une mesure d&#39;int&eacute;grit&eacute; axonale et de coh&eacute;rence, a &eacute;t&eacute; r&eacute;duite (p &lt; 0,005) dans la substance blanche subgyral du cortex sensorimoteur des transporteurs DYT1. Une connectivit&eacute; anatomique anormale de la zone moteur suppl&eacute;mentaire peut contribuer &agrave; la susceptibilit&eacute; des transporteurs DYT1 &agrave; d&eacute;velopper des manifestations cliniques de la dystonie.

Modifications de microstructures de substance blanche chez les porteurs de la mutation du g&egrave;ne DYT1.

Parkinson's disease (PD) is associated with an abnormal pattern of regional brain function. The expression of this PD-related covariance pattern (PDRP) has been used to assess disease progression and the response to treatment. In this study, we validated the PDRP network as a measure of parkinsonism by prospectively computing its expression (PDRP scores) in (15)O-water (H(2)(15)O) and (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scans from PD patients and healthy volunteers. The reliability of this measure was also assessed within subjects using a test-retest design in mildly affected and advanced PD patients scanned at baseline and during treatment with levodopa or deep brain stimulation (DBS). We found that PDRP expression was significantly elevated in PD patients (P<0.001) relative to controls in a prospective analysis of brain scans obtained with either H(2)(15)O or FDG PET. A significant correlation (R(2)=0.61; P<0.001) was evident between PDRP scores computed from H(2)(15)O and FDG images in PD subjects scanned with both tracers. Test-retest reproducibility was very high (intraclass correlation coefficient (ICC)>0.92) for PDRP scores measured both within PET session and between sessions separated by up to 2 months. This high reproducibility was observed in both early stage and advanced PD patients scanned at baseline and during treatment. The within-subject variability of this measure was less than 10% for both unmedicated and treated conditions. These findings suggest that the PDRP network is a reproducible and stable descriptor of regional functional abnormalities in parkinsonism. The quantification of PDRP expression in PD patients can serve as a potential biomarker in PET intervention studies for this disorder.

Abnormal metabolic network activity in Parkinson's disease: test-retest reproducibility.

Maladie de Parkinson (MP) est associ&eacute;e un mod&egrave;le anormaux de la fonction c&eacute;r&eacute;brale r&eacute;gionale. L&#39;expression de ce mod&egrave;le de covariance PD-associ&eacute;s (PDRP) a &eacute;t&eacute; utilis&eacute;e pour &eacute;valuer la progression de la maladie et la r&eacute;ponse au traitement. Dans cette &eacute;tude, nous avons valid&eacute; le r&eacute;seau PDRP en guise de parkinsonisme en calculant prospectivement son expression (scores PDRP) dans l&#39;eau (15) O (H(2)(15)O) et (18) F-fluorod&eacute;soxyglucose (FDG) par &eacute;mission de positrons (TEP) analyses de patients Parkinsoniens et des volontaires sains. La fiabilit&eacute; de cette mesure a aussi &eacute;t&eacute; &eacute;valu&eacute;e dans les sujets selon un mod&egrave;le de test-retest dans l&eacute;g&egrave;rement touch&eacute;s et avanc&eacute;s des patients Parkinsoniens scann&eacute;s au d&eacute;part et pendant le traitement par l&eacute;vodopa ou stimulation c&eacute;r&eacute;brale profonde (SCP). Nous avons trouv&eacute; qu&#39;expression PDRP &eacute;tait significativement &eacute;lev&eacute;e chez les patients Parkinsoniens (P &lt; 0,001) par rapport aux t&eacute;moins dans une analyse prospective des scanographies du cerveau obtenues par application (15) O ou FDG PET. Une corr&eacute;lation significative (R (2) = 0,61 ; P &lt; 0,001) &eacute;tait &eacute;vident entre les scores PDRP calcul&eacute;s &agrave; partir d&#39;application (15) O et images FDG dans PD sujets scann&eacute;s avec les deux traceurs. Test-retest de reproductibilit&eacute; &eacute;tait tr&egrave;s &eacute;lev&eacute; (coefficient de corr&eacute;lation intraclasse (ICC) &gt; 0,92) pour les scores PDRP mesur&eacute;es au sein de la session de PET et entre les sessions s&eacute;par&eacute;es par jusqu&#39;&agrave; 2 mois. Cette reproductibilit&eacute; &eacute;lev&eacute;e a &eacute;t&eacute; observ&eacute;e dans les deux premi&egrave;res &eacute;tapes et pointe PD les patients analys&eacute;s au d&eacute;part et pendant le traitement. La variabilit&eacute; intra-sujet de cette mesure &eacute;tait inf&eacute;rieur &agrave; 10 % pour des conditions non m&eacute;dicamenteux et de trait&eacute;s. Ces r&eacute;sultats sugg&egrave;rent que le r&eacute;seau PDRP est un descripteur stable et reproductible des anomalies fonctionnelles r&eacute;gionales parkinsonisme. La quantification de l&#39;expression de PDRP chez les patients Parkinsoniens peut servir comme biomarqueurs potentiels dans les &eacute;tudes d&#39;intervention PET pour ce trouble.

Activit&eacute; r&eacute;seau m&eacute;tabolique anormale dans la maladie de Parkinson : test-retest reproductibilit&eacute;.

Malattia di Parkinson (MdP) &egrave; associata un modello anormale della funzione cerebrale regionale. L&#39;espressione di questo modello di covarianza PD-correlati (PDRP) &egrave; stato utilizzato per valutare la progressione della malattia e la risposta al trattamento. In questo studio, abbiamo convalidato la rete PDRP come una misura di parkinsonismo calcolando prospetticamente la sua espressione (gol di PDRP) in acqua (15) O (H(2)(15)O)) e (18) F-fluorodesossiglucosio (FDG) scansioni di tomografia ad emissione di positroni (PET) dal PD pazienti e volontari sani. L&#39;affidabilit&agrave; di questa misura &egrave; stata valutata anche nei soggetti che utilizzano un design di ripetere il test-test nei pazienti PD lievemente colpiti e avanzati analizzati al basale e durante il trattamento con levodopa o la stimolazione cerebrale profonda (DBS). Abbiamo trovato che espressione PDRP era significativamente elevati nei pazienti PD (P &lt; 0.001) rispetto a controlli in un&#39;analisi prospettica di scansioni cerebrali ottenute con FDG PET. o H(2) (15) O Una significativa correlazione (R (2) = 0.61; P &lt; 0.001) era evidente tra PDRP punteggi calcolati da H(2) (15) O e immagini FDG nel PD soggetti analizzati con entrambi riproducibilit&agrave; traccianti. ripetere il Test-test era molto alta (coefficiente di correlazione intraclasse (ICC) &amp;gt; 0,92) per i punteggi PDRP misurate all&#39;interno della sessione PET e tra sessioni separate da fino a 2 mesi. Questa elevata riproducibilit&agrave; fu osservata in entrambi fase precoce e avanzata PD pazienti analizzati al basale e durante il trattamento. La variabilit&agrave; all&#39;interno del soggetto di questa misura &egrave; stata inferiore al 10% per le condizioni sia unmedicated e trattati. Questi risultati suggeriscono che la rete PDRP &egrave; un descrittore di stabile e riproducibile di anomalie funzionali regionali nel parkinsonismo. La quantificazione dell&#39;espressione PDRP nei pazienti PD pu&ograve; servire come un potenziale biomarcatore in studi di intervento di PET per questo disturbo.

Attivit&agrave; di rete metabolica anormale nel morbo di Parkinson: test-ritestare riproducibilit&agrave;.

New strategies are considered for automated, single-subject differential diagnosis of independent degenerative brain disorders characterized by similar clinical symptoms using functional imaging. The methodology of these strategies is described and its application in parkinsonian movement disorders is illustrated for PET data. Using an automated diagnostic Topographic Profile Rating (TPR) technique based on the Scaled Subprofile Model (SSM-PCA), single-subject score values for different conditions are compared with reference values to predict diagnosis. The discriminatory parameters of reference score sets associated with significant SSM principal components referred to as group invariant subprofiles (GIS networks) are examined. It is shown that the extraction of exclusive sub-networks that stem from contrasting image features between conditions can be an effective tool for optimization that does not require expert knowledge.

New strategies for automated differential diagnosis of degenerative brain disorders.

Nouvelles strat&eacute;gies sont consid&eacute;r&eacute;s pour le diagnostic diff&eacute;rentiel automatis&eacute;, sujet unique des troubles ind&eacute;pendants c&eacute;r&eacute;brale d&eacute;g&eacute;n&eacute;rative caract&eacute;ris&eacute;e par des sympt&ocirc;mes cliniques similaires &agrave; l&#39;aide de l&#39;imagerie fonctionnelle. La m&eacute;thodologie de ces strat&eacute;gies est d&eacute;crite et son application dans les troubles parkinsoniens est illustr&eacute;e pour donn&eacute;es de PET. En utilisant une technique de notation de profil topographique (TPR) diagnostic automatis&eacute; bas&eacute; sur le Scaled mod&egrave;le sous-profils (SSM-PCA), sujet unique partition valeurs pour diff&eacute;rentes conditions sont compar&eacute;es aux valeurs de r&eacute;f&eacute;rence pour pr&eacute;dire le diagnostic. Les param&egrave;tres discriminatoires de r&eacute;f&eacute;rence partition d&eacute;finit associ&eacute; d&#39;importantes composantes principales SSM, d&eacute;nomm&eacute;s sous-profils invariant de groupe (r&eacute;seaux de GIS) sont examin&eacute;s. Il est d&eacute;montr&eacute; que l&#39;extraction des sous-r&eacute;seaux exclusifs qui en d&eacute;coulent contrastant les caract&eacute;ristiques de l&#39;image entre les conditions peut &ecirc;tre un outil efficace pour l&#39;optimisation qui ne n&eacute;cessite pas de connaissances sp&eacute;cialis&eacute;es.

Nouvelles strat&eacute;gies automatis&eacute;es diagnostic diff&eacute;rentiel des troubles c&eacute;r&eacute;braux d&eacute;g&eacute;n&eacute;ratifs.

Nuove strategie sono considerati per la diagnosi differenziale automatica, single-soggetto di disturbi indipendenti degenerativa del cervello caratterizzata da sintomi clinici simili mediante imaging funzionale. Viene descritta la metodologia di queste strategie e sua applicazione nei disturbi del movimento parkinsoniano &egrave; illustrata per i dati di PET. Utilizzando una tecnica di valutazione profilo topografico (TPR) diagnostico automatizzato basato sul scalato Subprofile modello (SSM-PCA), punteggio singolo soggetto i valori per le diverse condizioni vengono confrontate con i valori di riferimento per predire la diagnosi. I parametri di riferimento discriminatori punteggio set associato a significativi SSM principali componenti denominati subprofiles invariante del gruppo (reti GIS) sono esaminati. Si &egrave; dimostrato che l&#39;estrazione delle sottoreti esclusivi che derivano dal contrasto caratteristiche immagine tra le condizioni pu&ograve; essere un efficace strumento di ottimizzazione che non richiede conoscenza di esperti.

Nuove strategie per la diagnosi differenziale automatica di malattie degenerative del cervello.

In the current paper, we describe methodologies for single subject differential diagnosis of degenerative brain disorders using multivariate principal component analysis (PCA) of functional imaging scans. An automated routine utilizing these methods is applied to positron emission tomography (PET) brain data to distinguish several discrete parkinsonian movement disorders with similar clinical manifestations. Disease specific expressions of voxel-based spatial covariance patterns are predetermined using the Scaled Subprofile Model (SSM/PCA) and a scalar measure of the manifestation of each pattern in prospective subject images is subsequently derived. Scores are automatically compared to reference values generated for each pathological condition in a corresponding set of patient and control scans. Diagnostic outcome is optimized using strategies such as the derivation of patterns in a voxel subspace that reflects contrasting image characteristics between conditions, or by using an independent patient population as controls. The prediction models for two, three and four way classification problems using direct scalar comparison as well as classical discriminant analysis are assessed in a composite training population comprised of three different patient classes and normal controls, and validated in a similar independent test population. Results illustrate that highly accurate diagnosis can often be achieved by simple comparison of scores utilizing optimized patterns.

Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features.

Dans le pr&eacute;sent document, nous d&eacute;crivons les m&eacute;thodologies pour le diagnostic diff&eacute;rentiel des troubles c&eacute;r&eacute;braux d&eacute;g&eacute;n&eacute;ratifs avec analyse multivari&eacute;e composantes principales (PCA) de scans d&#39;imagerie fonctionnelles unique sujet. Une routine automatis&eacute;e utilisant ces m&eacute;thodes est appliqu&eacute;e aux donn&eacute;es de cerveau de tomographie par &eacute;mission de positrons (PET) pour distinguer plusieurs troubles du mouvement discret parkinsonisme avec manifestations cliniques similaires. Expressions sp&eacute;cifiques de la maladie des patrons de la covariance spatiale ax&eacute;e sur les voxels sont pr&eacute;d&eacute;termin&eacute;es en utilisant le mod&egrave;le des sous-profils Scaled (SSM/PCA) et une mesure scalaire de la manifestation de chaque mod&egrave;le dans les images du sujet pressenti est d&eacute;riv&eacute;e par la suite. Scores sont automatiquement compar&eacute;s pour faire r&eacute;f&eacute;rence &agrave; des valeurs g&eacute;n&eacute;r&eacute;es pour chaque &eacute;tat pathologique dans un jeu correspondant de scans de patient et contr&ocirc;le. R&eacute;sultat diagnostique est optimis&eacute; &agrave; l&#39;aide de strat&eacute;gies telles que la d&eacute;rivation des patrons dans un sous-espace de voxel qui refl&egrave;te les caract&eacute;ristiques de l&#39;image contrast&eacute;e entre les conditions, ou &agrave; l&#39;aide d&#39;une population de patients ind&eacute;pendante comme t&eacute;moins. Les mod&egrave;les de pr&eacute;diction pour voie deux, trois ou quatre probl&egrave;mes de classification &agrave; l&#39;aide de comparaison scalaire directe ainsi que l&#39;analyse discriminante classique sont &eacute;valu&eacute;s dans une population de formation composite compos&eacute; de trois diff&eacute;rentes cat&eacute;gories de patients et les t&eacute;moins normaux et valid&eacute;s dans un semblable ind&eacute;pendant tester la population. Les r&eacute;sultats illustrent qu&#39;un diagnostic tr&egrave;s pr&eacute;cis est possible souvent par simple comparaison des scores utilisant des profils optimis&eacute;s.

Diagnostic diff&eacute;rentiel des syndromes parkinsoniens &agrave; l&#39;aide de fonctions d&#39;imagerie fonctionnelles ax&eacute;e sur les PCA.

Nel documento corrente, descriviamo le metodologie per la diagnosi differenziale del singolo soggetto di malattie degenerative del cervello mediante analisi multivariata componente principale (PCA) di scansioni di imaging funzionale. Una procedura automatizzata utilizzando questi metodi &egrave; applicata ai dati cervello tomografia ad emissione di positroni (PET) per distinguere i diversi disturbi del movimento discreto parkinsoniano con manifestazioni cliniche simili. Malattie specifiche espressioni di modelli di voxel-based spatial covarianza sono predeterminate utilizzando il modello in scala Subprofile (SSM/PCA) e successivamente &egrave; derivata una misura scalare della manifestazione di ogni modello in immagini soggetto prospettico. Punteggi vengono automaticamente confrontati per fare riferimento a valori generati per ogni condizione patologica in una corrispondente serie di scansioni paziente e controllo. Risultato diagnostico &egrave; ottimizzata mediante strategie quali la derivazione dei modelli in un sottospazio di voxel che riflette le caratteristiche dell&#39;immagine contrasto tra le condizioni, o utilizzando una popolazione di pazienti indipendenti come controlli. I modelli di previsione per due, tre e quattro modo problemi di classificazione mediante confronto diretto scalare, come pure analisi discriminante classica sono valutati in una popolazione composita formazione composta da tre differenti classi di pazienti e controlli normali e convalidato in un simile indipendente dalla popolazione di prova. Risultati illustrano che altamente accurata diagnosi spesso pu&ograve; essere raggiunto dal semplice confronto dei punteggi che utilizzano modelli ottimizzati.

Diagnosi differenziale delle sindromi parkinsoniane utilizzando funzionalit&agrave; di imaging funzionale basato su PCA.

Consistent functional brain abnormalities in Parkinson's disease (PD) are difficult to pinpoint because differences from the normal state are often subtle. In this regard, the application of multivariate methods of analysis has been successful but not devoid of misinterpretation and controversy. The Scaled Subprofile Model (SSM), a principal components analysis (PCA)-based spatial covariance method, has yielded critical information regarding the characteristic abnormalities of functional brain organization that underlie PD and other neurodegenerative disorders. However, the relevance of disease-related spatial covariance patterns (metabolic brain networks) and the most effective methods for their derivation has been a subject of debate. We address these issues here and discuss the inherent advantages of proper application as well as the effects of the misapplication of this methodology. We show that ratio pre-normalization using the mean global metabolic rate (GMR) or regional values from a "reference" brain region (e.g. cerebellum) that may be required in univariate analytical approaches is obviated in SSM. We discuss deviations of the methodology that may yield erroneous or confounding factors.

Scaled subprofile modeling of resting state imaging data in Parkinson's disease: methodological issues.

Anomalies c&eacute;r&eacute;brales fonctionnelles coh&eacute;rentes dans la maladie de Parkinson (MP) sont difficiles &agrave; cerner car les diff&eacute;rences par rapport &agrave; l&#39;&eacute;tat normal sont souvent subtils. &Agrave; cet &eacute;gard, l&#39;application des m&eacute;thodes multivari&eacute;es d&#39;analyse a &eacute;t&eacute; r&eacute;ussie, mais non d&eacute;nu&eacute;e d&#39;erreurs d&#39;interpr&eacute;tation et de controverse. Le mod&egrave;le des sous-profils mise &agrave; l&#39;&eacute;chelle (SSM), une analyse en composantes principales (PCA)-m&eacute;thode de la fonction covariance spatiale, a permis d&#39;obtenir des informations critiques concernant les anomalies caract&eacute;ristiques d&#39;organisation c&eacute;r&eacute;brale fonctionnelle qui sous-tendent les PD et autres maladies neurod&eacute;g&eacute;n&eacute;ratives. Toutefois, la pertinence des mod&egrave;les (r&eacute;seaux m&eacute;taboliques c&eacute;r&eacute;brales) de la covariance spatiale li&eacute;e &agrave; la maladie et les m&eacute;thodes les plus efficaces pour leur d&eacute;rivation a &eacute;t&eacute; un sujet de d&eacute;bat. Nous r&eacute;gler ces probl&egrave;mes ici et de discuter les avantages inh&eacute;rents &agrave; une bonne application ainsi que les effets de la mauvaise application de cette m&eacute;thodologie. Nous montrons que pre-normalization ratio en utilisant le taux m&eacute;tabolique global moyen (GMR) ou des valeurs r&eacute;gionales d&#39;une r&eacute;gion du cerveau &laquo; r&eacute;f&eacute;rence &raquo; (p. ex. le cervelet) qui peuvent &ecirc;tre n&eacute;cessaires dans une variable analytique des approches est pr&eacute;venu SSM. Nous discutons des d&eacute;viations de la m&eacute;thodologie peut transmettre des facteurs erron&eacute;s ou confusionnels.

Mise &agrave; l&#39;&eacute;chelle sous-profils mod&eacute;lisation des donn&eacute;es d&#39;imagerie de l&#39;&eacute;tat de repos dans la maladie de Parkinson : questions m&eacute;thodologiques.

Anomalie cerebrali funzionali coerenti nella malattia di Parkinson (MdP) sono difficili da individuare perch&eacute; le differenze dello stato normale sono spesso sottili. A questo proposito, l&#39;applicazione di metodi multivariati di analisi &egrave; stato successo, ma non privo di interpretazioni e polemiche. La scala Subprofile modello (SSM), un&#39;analisi delle componenti principali (PCA)-metodo basato covarianza spaziale, ha dato informazioni critiche per quanto riguarda le anomalie caratteristiche dell&#39;organizzazione funzionale del cervello che sottendono PD e altre patologie neurodegenerative. Tuttavia, la rilevanza dei modelli riguardanti le malattie covarianza spaziale (reti metaboliche cerebrali) e i metodi pi&ugrave; efficaci per la loro derivazione &egrave; stato oggetto di dibattito. Noi di affrontare questi problemi qui e discutere i vantaggi intrinseci della corretta applicazione, nonch&eacute; gli effetti dell&#39;erronea applicazione di questa metodologia. Mostriamo che pre-normalization rapporto utilizzando il tasso metabolico globale medio (GMR) o valori regionali da una regione del cervello &quot;riferimento&quot; (p. es. cervelletto) che pu&ograve; essere richiesto in analisi univariata approcci &egrave; ovviato in SSM. Discutiamo le deviazioni della metodologia che pu&ograve; produrre fattori confondenti o erronei.

Scala subprofile modellazione di dati di imaging dello stato di riposo nella malattia di Parkinson: questioni metodologiche.

Abnormal physiological networks of brain areas in disease can be identified by applying specialized multivariate computational algorithms based on principal component analysis to functional image data. Here we demonstrate the reproducibility of network patterns derived using positron emission tomography (PET) data in independent populations of parkinsonian patients for a large, clinically validated data set comprised of subjects with idiopathic Parkinson's disease (iPD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Correlation of voxel values of network patterns derived for the same condition in different data sets was high. To further illustrate the validity of these networks, we performed single subject differential diagnosis of prospective test subjects to determine the most probable case based on a subject's network scores expressed for each of these distinct parkinsonian syndromes. Three-fold cross-validation was performed to determine accuracy and positive predictive rates based on networks derived in separate folds of the composite data set. A logistic regression based classification algorithm was used to train in each fold and test in the remaining two folds. Combined accuracy for each of the three folds ranged from 82% to 93% in the training set and was approximately 81% for prospective test subjects.

Three-fold cross-validation of parkinsonian brain patterns.

Les r&eacute;seaux physiologiques anormales des zones du cerveau dans la maladie peuvent &ecirc;tre identifi&eacute;s par l&#39;algorithmie sp&eacute;cialis&eacute;es multivari&eacute;e calcul bas&eacute; sur l&#39;analyse en composantes principales donn&eacute;es d&#39;image fonctionnelle. Nous d&eacute;montrons la reproductibilit&eacute; des mod&egrave;les de r&eacute;seau en utilisant le jeu de donn&eacute;es de donn&eacute;es dans des populations ind&eacute;pendantes de patients parkinsoniens pour de grands, cliniquement valid&eacute;es par &eacute;mission de positrons (TEP) compos&eacute; de sujets souffrant de la maladie de Parkinson idiopathique (iPD), multiples atrophie (MSA) et paralysie supranucl&eacute;aire progressive (PSP). Corr&eacute;lation des valeurs de voxel de r&eacute;seau mod&egrave;les d&eacute;riv&eacute;s pour la m&ecirc;me maladie dans diff&eacute;rentes bases de donn&eacute;es &eacute;tait &eacute;lev&eacute;e. Pour mieux illustrer la validit&eacute; de ces r&eacute;seaux, nous avons effectu&eacute; le diagnostic diff&eacute;rentiel seul sujet d&#39;&eacute;ventuels sujets afin de d&eacute;terminer le cas plus probable bas&eacute; sur les scores de r&eacute;seau du sujet exprim&eacute;s pour chacun de ces syndromes parkinsoniens distinctes. Triple cross-validation a &eacute;t&eacute; effectu&eacute;e pour d&eacute;terminer la pr&eacute;cision et des taux positifs pr&eacute;dictifs issu des r&eacute;seaux d&eacute;riv&eacute;s dans les plis distincts de l&#39;ensemble de donn&eacute;es composite. Un algorithme de classification de la base de r&eacute;gression logistique a &eacute;t&eacute; utilis&eacute; pour former dans chaque pli et tester dans les deux plis restants. Pr&eacute;cision combin&eacute;e pour chacun des trois plis entre 82 % et 93 % dans la formation d&eacute;finie et &eacute;tait d&#39;environ 81 % pour les sujets potentiels.

Triple validation crois&eacute;e de mod&egrave;les de cerveau parkinsonien.

Reti fisiologiche anormali delle aree del cervello nella malattia possono essere identificati applicando algoritmi specializzati multivariati computazionali basati sull&#39;analisi delle componenti principali di dati immagine funzionale. Qui noi dimostrare la riproducibilit&agrave; dei modelli di rete derivato usando set di dati di dati indipendenti popolazioni di pazienti parkinsoniani per un grande, clinicamente validati tomografia ad emissione di positroni (PET) composto da soggetti con di Parkinson idiopatica (iPD) multiple atrofia multisistemica (MSA) e Paralisi sopranucleare progressiva (PSP). Correlazione dei valori di voxel della rete modelli derivati per la stessa condizione in diversi insiemi di dati &egrave; stato elevato. Per illustrare ulteriormente la validit&agrave; di queste reti, abbiamo eseguito diagnosi differenziale unico soggetto dei potenziali soggetti per determinare il pi&ugrave; probabile caso basato su punteggi di rete di un soggetto espressi per ognuna di queste sindromi parkinsoniane distinti. Triplice cross-validazione &egrave; stata eseguita per determinare la precisione e tariffe predittivi positivi basati su reti derivate in pieghe separate del set di dati compositi. Un algoritmo di classificazione basato su regressione logistica &egrave; stato utilizzato per formare in ogni piega e testare nelle restanti due pieghe. Precisione combinata per ciascuna delle tre pieghe variava dall&#39;82% al 93% nella formazione impostata ed era circa 81% per i soggetti potenziali.

Triplice cross-validazione dei modelli di cervello parkinsoniano.

Parkinson's disease (PD) is associated with a characteristic regional metabolic covariance pattern that is modulated by treatment. To determine whether a homologous metabolic pattern is also present in nonhuman primate models of parkinsonism, 11 adult macaque monkeys with parkinsonism secondary to chronic systemic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and 12 age-matched healthy animals were scanned with [(18)F]fluorodeoxyglucose (FDG) positron emission tomography (PET). A subgroup comprising five parkinsonian and six control animals was used to identify a parkinsonism-related pattern (PRP). For validation, analogous topographies were derived from other subsets of parkinsonian and control animals. The PRP topography was characterized by metabolic increases in putamen/pallidum, thalamus, pons, and sensorimotor cortex, as well as reductions in the posterior parietal-occipital region. Pattern expression was significantly elevated in parkinsonian relative to healthy animals (P<0.00001). Parkinsonism-related topographies identified in the other derivation sets were very similar, with significant pairwise correlations of region weights (r>0.88; P<0.0001) and subject scores (r>0.74; P<0.01). Moreover, pattern expression in parkinsonian animals correlated with motor ratings (r>0.71; P<0.05). Thus, homologous parkinsonism-related metabolic networks are demonstrable in PD patients and in monkeys with experimental parkinsonism. Network quantification may provide a useful biomarker for the evaluation of new therapeutic agents in preclinical models of PD.

Abnormal metabolic brain networks in a nonhuman primate model of parkinsonism.

Maladie de Parkinson (MP) est associ&eacute;e &agrave; un motif caract&eacute;ristique de covariance m&eacute;taboliques r&eacute;gionaux qui est modul&eacute; par le traitement. Pour d&eacute;terminer si un profil m&eacute;tabolique homologue est &eacute;galement pr&eacute;sent dans les mod&egrave;les de primates non-humains de parkinsonisme, 11 macaques adultes avec parkinsonisme secondaire &agrave; 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine syst&eacute;mique chronique (MPTP) et 12 appari&eacute;s selon l&#39;&acirc;ge des animaux sains ont &eacute;t&eacute; scann&eacute;es avec [(18) F] fluorod&eacute;soxyglucose (FDG), tomographie par &eacute;mission (TEP). Un sous-groupe comprenant cinq parkinsoniens et six animaux t&eacute;moins a &eacute;t&eacute; utilis&eacute; pour identifier un mod&egrave;le ax&eacute;es sur la maladie de Parkinson (PRP). Pour la validation, topographies analogues proviennent d&#39;autres sous-ensembles des parkinsoniens et contr&ocirc;le des animaux. La topographie de la PRP a &eacute;t&eacute; caract&eacute;ris&eacute;e par m&eacute;tabolique augmente dans le putamen/pallidum, thalamus, pons et cortex sensorimoteur, ainsi que des r&eacute;ductions dans la r&eacute;gion pari&eacute;tale-occipitale post&eacute;rieure. Expression de motif &eacute;tait significativement &eacute;lev&eacute;e chez les parkinsoniens par rapport aux animaux sains (P &lt; 0,00001). topographies Parkinsonisme li&eacute;es identifi&eacute;es dans les autres jeux de d&eacute;rivation ont &eacute;t&eacute; tr&egrave;s similaires, avec des corr&eacute;lations significatives par paires de poids de la r&eacute;gion (r &gt; 0,88 ; P &lt; 0,0001) et des scores de sujet (r &gt; 0,74 ; P &lt; 0,01). en Outre, expression de mod&egrave;le chez les parkinsoniens animaux en corr&eacute;lation avec cotes moteurs (r &gt; 0,71 ; P &lt; 0,05). Ainsi, les homologues r&eacute;seaux m&eacute;taboliques li&eacute;s &agrave; la maladie de Parkinson sont d&eacute;montrables dans les patients Parkinsoniens et chez le singe avec parkinsonisme exp&eacute;rimental. Quantification de r&eacute;seau peut fournir un biomarqueur utile pour l&#39;&eacute;valuation de nouveaux agents th&eacute;rapeutiques dans des mod&egrave;les pr&eacute;cliniques de PD.

R&eacute;seaux m&eacute;taboliques anormale du cerveau dans un mod&egrave;le de primate non humain de la maladie de Parkinson.

Malattia di Parkinson (MdP) &egrave; associata con un pattern caratteristico regionale covarianza metabolica che &egrave; modulato da un trattamento. Per determinare se &egrave; presente in modelli di primati non umani di parkinsonismo, 11 scimmie Macaco adulte con parkinsonismo anche un omologo modello metabolico secondario a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine sistemica cronica (MPTP) e 12 animali sani di et&agrave; erano scansionate con [(18) F] fluorodesossiglucosio (FDG) positron emission tomography (PET). Un sottogruppo comprendente cinque parkinsoniano e sei animali di controllo &egrave; stato utilizzato per identificare un modello di parkinsonismo-correlati (PRP). Per la convalida, topografie analoghe sono derivati da altri sottoinsiemi del parkinsoniano e controllo degli animali. La topografia PRP &egrave; stata caratterizzata da metabolica aumenta nel putamen/pallidum, talamo, pons e corteccia sensitivo-motoria, come pure riduzioni nella regione parietale-occipitale posteriore. Modello espressione era significativamente elevata in parkinsoniani rispetto gli animali sani (P &lt; 0.00001). topografie parkinsonismo-correlati identificati in altri set di derivazione erano molto simili, con significative correlazioni coppie di pesi di regione (r &amp;gt; 0,88; P &lt; 0,0001) e gol di soggetto (r &amp;gt; 0,74; P &lt; 0,01). Inoltre, espressione del modello nel parkinsoniano animali correlato con motore voti (r &amp;gt; 0,71; P &lt; 0,05). Cos&igrave;, omologhe reti metaboliche parkinsonismo-correlati sono dimostrabili in pazienti PD e nelle scimmie con parkinsonismo sperimentale. Quantificazione della rete pu&ograve; fornire un utile biomarcatore per la valutazione di nuovi agenti terapeutici in modelli preclinici di PD.

Reti di cervello metabolico anormale in un modello di primati non umani di parkinsonismo.

Changes in regional brain activity can be observed following global normalization procedures to reduce variability in the data. In particular, spurious regional differences may appear when scans from patients with low global activity are compared to those from healthy subjects. It has thus been suggested that the consistent increases in subcortical activity that characterize the abnormal Parkinson's disease-related metabolic covariance pattern (PDRP) are artifacts of global normalization, and that similar topographies can be identified in scans from healthy subjects with varying global activity. To address this issue, we examined the effects of experimental reductions in global metabolic activity on PDRP expression. Ten healthy subjects underwent Â¹â¸F-fluorodeoxyglucose PET in wakefulness and following sleep induction. In all subjects, the global metabolic rate (GMR) declined with sleep (mean -34%, range: -17 to -56%), exceeding the test-retest differences of the measure (p<0.001). By contrast, sleep-wake differences in PDRP expression did not differ from test-retest differences, and did not correlate (RÂ²=0.04) with concurrent declines in global metabolic activity. Indeed, despite significant GMR reductions in sleep, PDRP values remained within the normal range. Likewise, voxel weights on the principal component patterns resulting from combined analysis of the sleep and wake scans did not correlate (RÂ²<0.07) with the corresponding regional loadings on the PDRP topography. In aggregate, the data demonstrate that abnormal PDRP expression is not induced by reductions in global activity. Moreover, significant declines in GMR are not associated with the appearance of PDRP-like spatial topographies.

Abnormal network topographies and changes in global activity: absence of a causal relationship.

Changements dans l&#39;activit&eacute; c&eacute;r&eacute;brale r&eacute;gionale peuvent &ecirc;tre observ&eacute;s en suivant les proc&eacute;dures de normalisation globale pour r&eacute;duire la variabilit&eacute; des donn&eacute;es. En particulier, fausses diff&eacute;rences r&eacute;gionales peuvent appara&icirc;tre lorsque les analyses de patients atteints de faible activit&eacute; mondiale sont compar&eacute;s &agrave; ceux de sujets sains. Il a donc &eacute;t&eacute; sugg&eacute;r&eacute; que les augmentations sous-corticale activit&eacute; coh&eacute;rentes qui caract&eacute;risent le mod&egrave;le anormale de covariance m&eacute;taboliques li&eacute;s &agrave; la maladie de Parkinson (PDRP) sont artefacts de la normalisation mondiale, et que les topographies similaires peuvent &ecirc;tre identifi&eacute;s dans les analyses de sujets sains avec des variables de l&#39;activit&eacute; mondiale. Pour r&eacute;soudre ce probl&egrave;me, nous avons examin&eacute; les effets des r&eacute;ductions exp&eacute;rimentales dans l&#39;activit&eacute; m&eacute;tabolique globale sur expression PDRP. Dix sujets sains ont subi &sup1;&#x2078;F-fluorod&eacute;soxyglucose PET dans l&#39;&eacute;veil et l&#39;induction de sommeil suivante. Dans toutes les mati&egrave;res, le taux de m&eacute;tabolisme global (GMR) a diminu&eacute; avec le sommeil (moyenne -34 %, la gamme : -17 &agrave; -56 %), d&eacute;passant les diff&eacute;rences de test-retest de la mesure (p &lt; 0,001). En revanche, sommeil-&eacute;veil des diff&eacute;rences dans l&#39;expression PDRP ne diff&eacute;rait pas de diff&eacute;rences de test-retest et n&#39;est pas corr&eacute;l&eacute; (R&sup2; = 0,04) simultan&eacute;es d&eacute;clinant une activit&eacute; m&eacute;tabolique global. En effet, malgr&eacute; des r&eacute;ductions significatives de GMR dans le sommeil, les valeurs de PDRP demeurent dans la plage normale. De m&ecirc;me, voxel poids sur les mod&egrave;les de composantes principales r&eacute;sultant de combin&eacute; analyse du sommeil et scans de r&eacute;veil ne sont pas corr&eacute;l&eacute; (R&sup2; &lt; 0,07) avec les charges correspondants r&eacute;gionales sur la topographie PDRP. Dans l&#39;ensemble, les donn&eacute;es d&eacute;montrent qu&#39;expression PDRP anormale n&#39;est pas induite par des r&eacute;ductions dans l&#39;activit&eacute; mondiale. En outre, baisses significatives des GMR ne sont pas associ&eacute;s avec l&#39;apparition des topographies spatiales PDRP ressemblant.

R&eacute;seau anormale topographies et des changements dans l&#39;activit&eacute; mondiale : absence d&#39;un lien de causalit&eacute;.

Cambiamenti nell&#39;attivit&agrave; cerebrale regionale possono essere osservate dopo le procedure di normalizzazione globale per ridurre la variabilit&agrave; nei dati. In particolare, le differenze regionali spurie possono apparire scansioni da pazienti con bassa attivit&agrave; globali sono rispetto a quelli provenienti da soggetti sani. Si &egrave; quindi ipotizzato che gli aumenti consistenti in attivit&agrave; sottocorticali che caratterizzano il modello anormale covarianza metabolici correlati a malattia di Parkinson (PDRP) sono artefatti di normalizzazione globale, e che topografie simili possono essere identificati nelle scansioni da soggetti sani con diverse attivit&agrave; globale. Per risolvere questo problema, abbiamo esaminato gli effetti delle riduzioni sperimentali in attivit&agrave; metabolica globale sull&#39;espressione PDRP. Dieci soggetti sani sono stati sottoposti &sup1;&#x2078;F-fluorodesossiglucosio PET in veglia e nell&#39;induzione del sonno. In tutte le materie, il tasso metabolico globale (GMR) &egrave; diminuita con il sonno (dire -34%, range: -17 a -56%), superare le differenze di prova-ripetizione della misura (p &lt; 0.001). Al contrario, sonno-veglia differenze nell&#39;espressione PDRP non differiscono dalle differenze di ripetere il test-test e non ha fatto correlare (R&sup2; = 0,04) con ribassi simultanee in attivit&agrave; metabolica globale. Infatti, nonostante significative riduzioni di GMR nel sonno, i valori PDRP rimasero all&#39;interno della gamma normale. Similarmente, voxel pesi sui modelli componenti principali derivanti dal combinato analisi del sonno e scansioni scia non correlare (R&sup2; &lt; 0,07) con i corrispondenti regionali carichi sulla topografia PDRP. Nel complesso, i dati dimostrano che anormale espressione PDRP non &egrave; indotta da riduzioni nell&#39;attivit&agrave; globale. Inoltre, significative diminuzione di GMR non sono associati con la comparsa di topografie spaziale PDRP-like.

Rete anomala Topografie e cambiamenti nell&#39;attivit&agrave; globale: assenza di una relazione causale.

We used a network approach to assess systems-level abnormalities in motor activation in humans with Parkinson's disease (PD). This was done by measuring the expression of the normal movement-related activation pattern (NMRP), a previously validated activation network deployed by healthy subjects during motor performance. In this study, NMRP expression was prospectively quantified in (15)O-water PET scans from a PD patient cohort comprised of a longitudinal early-stage group (n = 12) scanned at baseline and at two or three follow-up visits two years apart, and a moderately advanced group scanned on and off treatment with either subthalamic nucleus deep brain stimulation (n = 14) or intravenous levodopa infusion (n = 14). For each subject and condition, we measured NMRP expression during both movement and rest. Resting expression of the abnormal PD-related metabolic covariance pattern was likewise determined in the same subjects. NMRP expression was abnormally elevated (p < 0.001) in PD patients scanned in the nonmovement rest state. By contrast, network activity measured during movement did not differ from normal (p = 0.34). In the longitudinal cohort, abnormal increases in resting NMRP expression were evident at the earliest clinical stages (p < 0.05), which progressed significantly over time (p = 0.003). Analogous network changes were present at baseline in the treatment cohort (p = 0.001). These abnormalities improved with subthalamic nucleus stimulation (p < 0.005) but not levodopa (p = 0.25). In both cohorts, the changes in NMRP expression that were observed did not correlate with concurrent PD-related metabolic covariance pattern measurements (p > 0.22). Thus, the resting state in PD is characterized by changes in the activity of normal as well as pathological brain networks.

Parkinson's disease: increased motor network activity in the absence of movement.

Nous avons utilis&eacute; une approche de r&eacute;seau afin d&#39;&eacute;valuer les anomalies au niveau des syst&egrave;mes dans l&#39;activation du moteur chez l&#39;homme avec la maladie de Parkinson (MP). Cela a &eacute;t&eacute; fait en mesurant l&#39;expression du mod&egrave;le d&#39;activation d&#39;ax&eacute;s sur le mouvement normal (NRMP), un r&eacute;seau d&#39;activation pr&eacute;c&eacute;demment valid&eacute;s d&eacute;ploy&eacute; par des sujets sains au cours de la performance motrice. Dans cette &eacute;tude, l&#39;expression NRMP &eacute;tait prospectivement quantifi&eacute;e dans (15) O-eau PET scan d&#39;une cohorte de patients PD compos&eacute; d&#39;un groupe de d&eacute;but de croissance longitudinal (n = 12) scann&eacute; au d&eacute;part et &agrave; deux ou trois visites de suivi deux ans d&#39;intervalle, et un groupe moyennement avanc&eacute; scann&eacute; et d&eacute;sactiver le traitement par stimulation c&eacute;r&eacute;brale profonde noyau sous-thalamique (n = 14) ou la perfusion intraveineuse de l&eacute;vodopa (n = 14). Pour chaque sujet et de l&#39;&Eacute;tat, nous avons mesur&eacute; l&#39;expression NRMP pendant le mouvement et de repos. Expression du motif anormales li&eacute;es &agrave; PD de covariance m&eacute;tabolique de repos a &eacute;t&eacute; &eacute;galement d&eacute;termin&eacute; dans les m&ecirc;mes sujets. L&#39;expression NRMP &eacute;tait anormalement &eacute;lev&eacute; (p &lt; 0,001) en patients Parkinsoniens num&eacute;ris&eacute;s dans l&#39;&eacute;tat de repos nonmovement. En revanche, l&#39;activit&eacute; r&eacute;seau mesur&eacute;e durant le mouvement ne diff&eacute;rait pas normale (p = 0,34). Dans la cohorte longitudinale, des augmentations anormales d&#39;expression NRMP de repos sont &eacute;videntes dans les phases cliniques plus t&ocirc;t (p &lt; 0,05), qui a consid&eacute;rablement progress&eacute; au fil du temps (p = 0,003). Modifications du r&eacute;seau analogue &eacute;taient pr&eacute;sentes au d&eacute;part dans la cohorte de traitement (p = 0,001). Ces anomalies am&eacute;lior&eacute;e avec la stimulation du noyau subthalamique (p &lt; 0,005) mais pas de l&eacute;vodopa (p = 0,25). Dans les deux cohortes, les changements dans l&#39;expression NRMP observ&eacute;es ne sont pas corr&eacute;l&eacute; avec des mesures simultan&eacute;es de covariance m&eacute;taboliques li&eacute;es &agrave; PD mod&egrave;le (p &gt; 0,22). Ainsi, l&#39;&eacute;tat de repos dans les PD se caract&eacute;rise par des changements dans l&#39;activit&eacute; de la normale ainsi que de r&eacute;seaux c&eacute;r&eacute;braux pathologique.

La maladie de Parkinson : augmentation de l&#39;activit&eacute; de r&eacute;seau moteur en l&#39;absence de mouvement.

Abbiamo utilizzato un approccio di rete per valutare il livello dei sistemi anomalie nell&#39;attivazione motore negli uomini con malattia di Parkinson (MdP). Questo &egrave; stato fatto misurando l&#39;espressione del modello normale attivazione legati al movimento (NMRP), una rete di attivazione precedentemente validato distribuito da soggetti sani durante le prestazioni di motore. In questo studio, l&#39;espressione NMRP era prospetticamente quantificati in (15) O acqua PET scansioni da una coorte di paziente PD composto da un gruppo di early-stage longitudinale (n = 12) analizzati al basale e a due o tre visite di follow-up due anni di distanza, e un gruppo moderatamente avanzato analizzati su e il trattamento con la stimolazione cerebrale profonda o nucleo subtalamico (n = 14) o infusione di levodopa per via endovenosa (n = 14). Per ogni soggetto e condizione, abbiamo misurato l&#39;espressione NMRP durante il movimento sia a riposo. Espressione del modello anomala PD-correlati metabolici covarianza di riposo similarmente &egrave; stato determinato nelle stesse materie. NMRP espressione era anormalmente elevato (p &lt; 0.001) nei pazienti PD scansionate in stato di riposo il nonmovement. Al contrario, attivit&agrave; di rete misurata durante il movimento non differiscono dal normale (p = 0,34). Nella coorte longitudinale, aumento anormale espressione NMRP di riposo era evidenti presso le prime fasi cliniche (p &lt; 0.05), che ha progredito significativamente nel corso del tempo (p = 0,003). Cambiamenti di analoga rete erano presenti al basale nella coorte di trattamento (p = 0,001). Queste anomalie migliorato con la stimolazione del nucleo subtalamico (p &lt; 0,005) ma non levodopa (p = 0.25). In due coorti, i cambiamenti nell&#39;espressione NMRP che furono osservati ha fatto non correlare con misurazioni di pattern simultanee covarianza metabolici correlati a PD (p &amp;gt; 0,22). Cos&igrave;, lo stato di riposo nel PD &egrave; caratterizzato da cambiamenti nell&#39;attivit&agrave; del normale cos&igrave; come reti patologica del cervello.

Morbo di Parkinson: aumento dell&#39;attivit&agrave; di rete motore in assenza di movimento.

To generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [(18) F]fluorodeoxyglucose PET scans from patients with PD and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5, and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in patients with PD imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras. Hum Brain Mapp, 2013. Â© 2012 Wiley Periodicals, Inc.

Characterization of disease-related covariance topographies with SSMPCA toolbox: Effects of spatial normalization and PET scanners.

Pour g&eacute;n&eacute;rer des biomarqueurs d&#39;imagerie de r&eacute;seaux c&eacute;r&eacute;braux sp&eacute;cifiques &agrave; une maladie, nous avons impl&eacute;ment&eacute; une bo&icirc;te &agrave; outils g&eacute;n&eacute;raux pour effectuer rapidement des mod&eacute;lisation &agrave; l&#39;&eacute;chelle des sous-profils (SSM) bas&eacute;e sur l&#39;analyse en composantes principales (PCA) sur des images du cerveau des patients et normales. Cette bo&icirc;te &agrave; outils SSMPCA peut d&eacute;finir des mod&egrave;les de covariance spatiale dont l&#39;expression dans les sujets individuels peut distinguer les patients des contr&ocirc;les ou pr&eacute;dire les mesures comportementales. La technique peut d&eacute;pendre des diff&eacute;rences dans les algorithmes de normalisation spatiale et des syst&egrave;mes d&#39;imagerie c&eacute;r&eacute;brale. Nous avons &eacute;valu&eacute; la reproductibilit&eacute; des profils m&eacute;taboliques caract&eacute;ristiques g&eacute;n&eacute;r&eacute; par SSMPCA chez les patients atteints de maladie de Parkinson (MP). Nous avons utilis&eacute; [(18) F] fluorod&eacute;soxyglucose PET scans de patients atteints de PD et de contr&ocirc;les normaux. Motor-related (PDRP) et ax&eacute;es sur la cognition mod&egrave;les m&eacute;taboliques (PPPC) proviennent d&#39;images dans l&#39;espace normalis&eacute;s &agrave; l&#39;aide de quatre versions du logiciel SPM (spm99 spm2, spm5 et spm8). Diff&eacute;rences entre ces mod&egrave;les et les partitions de l&#39;objet ont &eacute;t&eacute; compar&eacute;s &agrave; travers plusieurs groupes ind&eacute;pendants des patients et des sujets t&eacute;moins. Ces patrons et les partitions de l&#39;objet ont &eacute;t&eacute; hautement reproductibles avec des programmes de normalisation diff&eacute;rents en termes de discrimination de la maladie et de corr&eacute;lation cognitive. Sujet scores &eacute;taient &eacute;galement comparables chez les patients avec PD imag&eacute; &agrave; travers de multiples tomographes TEP. Nos r&eacute;sultats confirment un tr&egrave;s haut degr&eacute; de coh&eacute;rence entre les r&eacute;seaux c&eacute;r&eacute;braux et leurs corr&eacute;lats cliniques dans PD &agrave; l&#39;aide d&#39;images normalis&eacute;es dans quatre diff&eacute;rentes plateformes SPM. Bo&icirc;te &agrave; outils SSMPCA peut &ecirc;tre utilis&eacute; avec fiabilit&eacute; pour g&eacute;n&eacute;rateurs sp&eacute;cifiques &agrave; certaines maladies biomarqueurs d&#39;imagerie malgr&eacute; l&#39;&eacute;volution continue des image logiciel dans la communaut&eacute; de neuro-imagerie de pr&eacute;traitement. Les expressions de r&eacute;seau peuvent &ecirc;tre quantifi&eacute;es dans chaque patient ind&eacute;pendant des caract&eacute;ristiques physiques diff&eacute;rentes des cam&eacute;ras de PET. Hum Brain Mapp, 2013. &copy; 2012 Wiley p&eacute;riodiques, Inc.

Caract&eacute;risation des topographies covariance li&eacute;e &agrave; la maladie avec la bo&icirc;te &agrave; outils SSMPCA : effets de normalisation spatiale et tomographes TEP.

Per generare imaging biomarkers dalle reti di malattia-specifici del cervello, abbiamo attuato una serie di strumenti generali per eseguire rapidamente la modellazione in scala subprofile (SSM), basato sull&#39;analisi delle componenti principali (PCA) sulle immagini del cervello di pazienti e normali. Questo toolbox SSMPCA possibile definire modelli di covarianza spaziale cui espressione nei singoli soggetti pu&ograve; discriminare i pazienti da controlli o prevedere misure comportamentali. La tecnica pu&ograve; dipendere da differenze nella normalizzazione spaziale algoritmi e sistemi di imaging cerebrale. Noi abbiamo valutato la riproducibilit&agrave; del caratteristico pattern metabolico generato da SSMPCA nei pazienti con malattia di Parkinson (MdP). Abbiamo usato [(18) F] fluorodesossiglucosio PET scansioni da pazienti con PD e controlli normali. Motore-correlati (PDRP) e cognizione relativi modelli metabolici (PDCP) sono stati derivati da immagini spazialmente normalizzati utilizzando quattro versioni del software SPM (spm99, spm2, spm5 e spm8). Le differenze tra questi modelli e gol di soggetto sono state confrontate attraverso molteplici gruppi indipendenti di pazienti e soggetti di controllo. Questi modelli e gol di soggetto erano altamente riproducibile con programmi di normalizzazione differenti in termini di discriminazione malattia e correlazione cognitiva. Gol di soggetto anche erano paragonabili nei pazienti con PD imaged across multiple di positroni. I nostri risultati confermano un elevato grado di coerenza tra le reti di cervello e loro clinical correlates in PD utilizzando immagini normalizzate in quattro diverse piattaforme SPM. Casella degli strumenti SSMPCA pu&ograve; essere utilizzato in modo affidabile per generare malattie specifiche imaging biomarkers nonostante la continua evoluzione del software nella Comunit&agrave; alle indagini neuroradiologiche di pre-elaborazione di immagini. Espressioni di rete possono essere quantificate in singoli pazienti indipendenti di diverse caratteristiche fisiche delle telecamere di PET. Hum cervello Mapp, 2013. &copy; 2012 Wiley periodici, Inc.

Article	Year
Comparative analysis of striatal FDOPA uptake in Parkinson's disease: ratio method versus graphical approach. Journal of nuclear medicine : official publication, Society of Nuclear Medicine\| PubMed ID: 12368370	2002
Microstructural white matter changes in carriers of the DYT1 gene mutation. Annals of neurology\| PubMed ID: 15293281	2004
Abnormal metabolic network activity in Parkinson's disease: test-retest reproducibility. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism\| PubMed ID: 16804550	2007
New strategies for automated differential diagnosis of degenerative brain disorders. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference\| PubMed ID: 18002732	2007
Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features. NeuroImage\| PubMed ID: 19349238	2009
Scaled subprofile modeling of resting state imaging data in Parkinson's disease: methodological issues. NeuroImage\| PubMed ID: 20969965	2011
Three-fold cross-validation of parkinsonian brain patterns. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference\| PubMed ID: 21095982	2010
Abnormal metabolic brain networks in a nonhuman primate model of parkinsonism. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism\| PubMed ID: 22126913	2012
Abnormal network topographies and changes in global activity: absence of a causal relationship. NeuroImage\| PubMed ID: 22951259	2012
Parkinson's disease: increased motor network activity in the absence of movement. The Journal of neuroscience : the official journal of the Society for Neuroscience\| PubMed ID: 23467370	2013
Characterization of disease-related covariance topographies with SSMPCA toolbox: Effects of spatial normalization and PET scanners. Human brain mapping\| PubMed ID: 23671030	2013

Phoebe Spetsieris

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