method-for-simultaneous-fmrieeg-data-collection-during-focused

Method for Simultaneous fMRI/EEG Data Collection during a Focused Attention Suggestion for Differential Thermal Sensation

In the present work, we demonstrate a method for concurrent collection of EEG/fMRI data. In our setup, EEG data are collected using a high-density ...

University of California, Los Angeles

Pleckstrin-2 is composed of 2 pleckstrin homology (PH) domains and a disheveled-Egl-10-pleckstrin (DEP) domain. A lipid-binding assay revealed that pleckstrin-2 binds with greatest affinity to D3 and D5 phosphoinositides. Pleckstrin-2 expressed in Jurkat T cells bound to the cellular membrane and enhanced actin-dependent spreading only after stimulation of the T-cell antigen receptor or the integrin alpha4beta1. A pleckstrin-2 variant containing point mutations in both PH domains failed to associate with the Jurkat membrane and had no effect on spreading under the same conditions. Although still membrane bound, a pleckstrin-2 variant containing point mutations in the DEP domain demonstrated a decreased ability to induce membrane ruffles and spread. Pleckstrin-2 also colocalized with actin at the immune synapse and integrin clusters via its PH domains. Although pleckstrin-2 can bind to purified D3 and D5 phosphoinositides, the intracellular membrane association of pleckstrin-2 and cell spreading are dependent on D3 phosphoinositides, because these effects were disrupted by pharmacologic inhibition of phosphatidylinositol 3-kinase (PI3K). Our results indicate that pleckstrin-2 uses its modular domains to bind to membrane-associated phosphatidylinositols generated by PI3K, whereby it coordinates with the actin cytoskeleton in lymphocyte spreading and immune synapse formation.

PI3K regulates pleckstrin-2 in T-cell cytoskeletal reorganization.

Chemokines acting through G protein-coupled receptors play an essential role in the immune response. PI3K and phospholipase C (PLC) are distinct signaling molecules that have been proposed in the regulation of chemokine-mediated cell migration. Studies with knockout mice have demonstrated a critical role for PI3K in G(alphai) protein-coupled receptor-mediated neutrophil and lymphocyte chemotaxis. Although PLCbeta is not essential for the chemotactic response of neutrophils, its role in lymphocyte migration has not been clearly defined. We compared the chemotactic response of peripheral T cells derived from wild-type mice with mice containing loss-of-function mutations in both of the two predominant lymphocyte PLCbeta isoforms (PLCbeta2 and PLCbeta3), and demonstrate that loss of PLCbeta2 and PLCbeta3 significantly impaired T cell migration. Because second messengers generated by PLCbeta lead to a rise in intracellular calcium and activation of PKC, we analyzed which of these responses was critical for the PLCbeta-mediated chemotaxis. Intracellular calcium chelation decreased the chemotactic response of wild-type lymphocytes, but pharmacologic inhibition of several PKC isoforms had no effect. Furthermore, calcium efflux induced by stromal cell-derived factor-1alpha was undetectable in PLCbeta2beta3-null lymphocytes, suggesting that the migration defect is due to the impaired ability to increase intracellular calcium. This study demonstrates that, in contrast to neutrophils, phospholipid second messengers generated by PLCbeta play a critical role in T lymphocyte chemotaxis.

Phospholipase cbeta is critical for T cell chemotaxis.

&#x53D1;&#x4F5C;&#x95F4;&#x671F;&#x8111;&#x7535;&#x56FE; &#xFF08;EEG&#xFF09; &#x7531;&#x4E8E;&#x5176;&#x4F9D;&#x8D56;&#x7684;&#x75EB;&#x6837;&#x653E;&#x7535;&#x53D1;&#x751F;&#x6709;&#x4E34;&#x5E8A;&#x610F;&#x4E49;&#x9650;&#x5236;&#x5728;&#x5176;&#x654F;&#x611F;&#x6027;&#x548C;&#x7279;&#x5F02;&#x6027;&#x5BF9;&#x766B;&#x75EB;&#x7684;&#x8BCA;&#x65AD;&#x3002;&#x6211;&#x4EEC;&#x5F00;&#x53D1;&#x4E86;&#x4E00;&#x4E2A;&#x8BA1;&#x7B97;&#x673A;&#x8F85;&#x52A9;&#x8BCA;&#x65AD; (CAD) &#x5DE5;&#x5177;&#xFF0C;&#x5BF9;&#x7EDD;&#x5BF9;&#x5149;&#x8C31;&#x80FD;&#x91CF;&#x7684;&#x5E38;&#x89C4;&#x8111;&#x7535;&#x56FE;&#x8FD0;&#x8425;&#xFF0C;&#x5E76;&#x62E5;&#x6709;&#x8F83;&#x9AD8;&#x7684;&#x7075;&#x654F;&#x5EA6;&#x548C;&#x6D88;&#x6781;&#x9884;&#x6D4B;&#x503C;&#x6BD4;&#x53D1;&#x4F5C;&#x95F4;&#x75EB;&#x6837;&#x653E;&#x7535;&#x7684;&#x9274;&#x5B9A;&#x3002;&#x6211;&#x4EEC;&#x7684;&#x65B9;&#x6CD5;&#x7528;&#x4E8E;&#x591A;&#x5C42;&#x611F;&#x77E5;&#x5668;&#x5206;&#x7C7B;&#x89C6;&#x9891;&#x8111;&#x7535;&#x76D1;&#x6D4B; 156 &#x75C5;&#x4EBA;&#x3002;&#x75C5;&#x4EBA;&#x7684;&#x4EBA;&#x53E3;&#x662F;&#x7167;&#x5C04;&#x591A;&#x79CD;&#x591A;&#x6837; &#xFF1B;87 &#x88AB;&#x8BCA;&#x65AD;&#x60A3;&#x6709;&#x5C40;&#x7076;&#x6027;&#x6216;&#x5E7F;&#x4E49;&#x7684;&#x7F09;&#x83B7;&#x91CF;&#x3002;&#x5176;&#x4F59;&#x7684;&#x75C5;&#x4EBA;&#x88AB;&#x8BCA;&#x65AD;&#x4E3A;&#x975E;&#x766B;&#x75EB;&#x53D1;&#x4F5C;&#x3002;&#x7075;&#x654F;&#x5EA6;&#x662F; 92%&#xFF08;95%&#x7F6E;&#x4FE1;&#x533A;&#x95F4; [CI] 85-97%&#xFF09; &#x548C;&#x9634;&#x6027;&#x9884;&#x6D4B;&#x503C;&#x4E3A; 82%&#xFF08;95%CI 67 92%&#xFF09;&#x3002;&#x6211;&#x4EEC;&#x5C06;&#x8BA8;&#x8BBA;&#x5982;&#x4F55;&#x8FD9;&#x4E9B;&#x7814;&#x7A76;&#x7ED3;&#x679C;&#x8868;&#x660E;&#x6B64; CAD &#x53EF;&#x7528;&#x4E8E;&#x8865;&#x5145;&#x57FA;&#x4E8E;&#x4E8B;&#x4EF6;&#x7684;&#x5206;&#x6790;&#x7531;&#x53D7;&#x8FC7;&#x8BAD;&#x7EC3;&#x7684; epileptologists&#x3002;

&#x81EA;&#x52A8;&#x7684;&#x8BCA;&#x65AD;&#x7684;&#x766B;&#x75EB;&#x8111;&#x7535;&#x56FE;&#x529F;&#x7387;&#x8C31;&#x3002;

Interictal electroencephalography (EEG) has clinically meaningful limitations in its sensitivity and specificity in the diagnosis of epilepsy because of its dependence on the occurrence of epileptiform discharges. We have developed a computer-aided diagnostic (CAD) tool that operates on the absolute spectral energy of the routine EEG and has both substantially higher sensitivity and negative predictive value than the identification of interictal epileptiform discharges. Our approach used a multilayer perceptron to classify 156 patients admitted for video-EEG monitoring. The patient population was diagnostically diverse; 87 were diagnosed with either generalized or focal seizures. The remainder of the patients were diagnosed with nonepileptic seizures. The sensitivity was 92% (95% confidence interval [CI] 85-97%) and the negative predictive value was 82% (95% CI 67-92%). We discuss how these findings suggest that this CAD can be used to supplement event-based analysis by trained epileptologists.

Automated diagnosis of epilepsy using EEG power spectrum.

Intercritiques &eacute;lectroenc&eacute;phalographie (EEG) a des limitations cliniquement significatives dans sa sensibilit&eacute; et sp&eacute;cificit&eacute; dans le diagnostic de l&#39;&eacute;pilepsie en raison de sa d&eacute;pendance sur l&#39;occurrence de d&eacute;charges &eacute;pileptiformes. Nous avons d&eacute;velopp&eacute; un outil de diagnostic assist&eacute; par ordinateur (CAD) qui exploite l&#39;&eacute;nergie spectrale absolue de la routine EEG et a la fois sensiblement plus grande sensibilit&eacute; et une valeur pr&eacute;dictive n&eacute;gative que l&#39;identification des d&eacute;charges &eacute;pileptiformes intercritiques. Notre approche a utilis&eacute; un perceptron multicouche pour classer les 156 patients admis pour la surveillance de vid&eacute;o-EEG. La population de patients &eacute;tait diagnostique diverse ; 87 ont &eacute;t&eacute; diagnostiqu&eacute;s avec des convulsions g&eacute;n&eacute;ralis&eacute;es ou focales. Le reste des patients ont &eacute;t&eacute; diagnostiqu&eacute; avec des convulsions &eacute;pileptiques. La sensibilit&eacute; &eacute;tait de 92 % (intervalle de confiance de 95 % [IC] 85-97 %) et la valeur pr&eacute;dictive n&eacute;gative &eacute;tait de 82 % (95 % CI 67-92 %). Nous discutons comment ces r&eacute;sultats sugg&egrave;rent que cette CAD peut &ecirc;tre utilis&eacute; pour compl&eacute;ter l&#39;analyse bas&eacute;e sur des &eacute;v&eacute;nements par les &eacute;pileptologues form&eacute;s.

Automatis&eacute; de diagnostic de l&#39;&eacute;pilepsie en utilisant le spectre de puissance d&#39;EEG.

Interiktale Elektroenzephalographie (EEG) hat klinisch sinnvolle Einschr&auml;nkungen in seiner Sensitivit&auml;t und Spezifit&auml;t bei der Diagnose von Epilepsie aufgrund der Abh&auml;ngigkeit von dem Vorkommen des Epileptiform Entladungen. Wir haben eine computergest&uuml;tzte Diagnose (CAD)-Tool entwickelt, das auf die absolute spektrale Energie der Routine-EEG und hat wesentlich h&ouml;here Sensitivit&auml;t und negativen Vorhersagewert als die Identifizierung von interiktale Epileptiform Entladungen. Unser Ansatz verwendet eine mehrschichtige Perceptron, um 156 Patienten zugelassen f&uuml;r Video-EEG-&Uuml;berwachung zu klassifizieren. Die Patientengruppe war diagnostisch vielf&auml;ltig; 87 wurden generalisierte oder fokale Anf&auml;lle diagnostiziert. Der Rest der Patienten wurden mit nonepileptic Anf&auml;lle diagnostiziert. Die Empfindlichkeit wurde 92 % (95 % Konfidenzintervall [CI] 85-97 %) und der negative Vorhersagewert war 82 % (95 % CI 67-92 %). Wir diskutieren, wie diese Forschungsergebnisse legen nahe, diese CAD kann verwendet werden um ereignisbasierte Analyse zu erg&auml;nzen durch ausgebildete Epileptologists.

Automatisierte Diagnose der Epilepsie mit EEG-Leistungsspektrum.

Interittale elettroencefalografia (EEG) &egrave; clinicamente significative limitazioni nella sua sensibilit&agrave; e specificit&agrave; nella diagnosi di epilessia a causa della sua dipendenza sull&#39;occorrenza degli scarichi epileptiform. Abbiamo sviluppato uno strumento di diagnostica computerizzata (CAD) che opera sull&#39;assoluta energia spettrale dell&#39;EEG di routine e ha sia sostanzialmente pi&ugrave; alta sensibilit&agrave; e valore predittivo negativo rispetto l&#39;identificazione degli scarichi epileptiform interictali. Il nostro approccio utilizzato un percettrone multistrato per classificare 156 pazienti ricoverati per il monitoraggio video-EEG. La popolazione di pazienti era diagnosticamente diversificata; 87 sono stati diagnosticati con convulsioni focali o generalizzate. Il resto dei pazienti sono stato diagnosticato con convulsioni nonepileptic. La sensibilit&agrave; era 92% (95% intervallo di confidenza [CI] 85-97%) e il valore predittivo negativo era 82% (95% CI 67-92%). Discutiamo di come questi risultati suggeriscono che questo CAD pu&ograve; essere utilizzato per integrare l&#39;analisi basata su evento di epileptologists addestrato.

Automatizzato diagnosi di epilessia utilizzando lo spettro di potenza EEG.

&#x767A;&#x4F5C;&#x9593;&#x6B20;&#x671F;&#x8133;&#x6CE2; (EEG) &#x81E8;&#x5E8A;&#x7684;&#x306B;&#x6709;&#x610F;&#x7FA9;&#x306A;&#x5236;&#x9650;&#x305D;&#x306E;&#x611F;&#x5EA6;&#x3068;&#x7279;&#x7570;&#x6027;&#x3066;&#x3093;&#x304B;&#x3093;&#x306E;&#x8A3A;&#x65AD;&#x306B;&#x304A;&#x3051;&#x308B;&#x3066;&#x3093;&#x304B;&#x3093;&#x653E;&#x96FB;&#x306E;&#x767A;&#x751F;&#x306B;&#x4F9D;&#x5B58;&#x306E;&#x305F;&#x3081;&#x304C;&#x3042;&#x308A;&#x307E;&#x3059;&#x3002;&#x6211;&#x3005; &#x306F;&#x3001;&#x30EB;&#x30FC;&#x30C1;&#x30F3;&#x306E;&#x8133;&#x6CE2;&#x306E;&#x7D76;&#x5BFE;&#x30B9;&#x30DA;&#x30AF;&#x30C8;&#x30EB;&#x306E;&#x30A8;&#x30CD;&#x30EB;&#x30AE;&#x30FC;&#x3067;&#x52D5;&#x4F5C;&#x3057;&#x3001;&#x5927;&#x5E45;&#x306B;&#x9AD8;&#x3044;&#x611F;&#x5EA6;&#x3068;&#x767A;&#x4F5C;&#x9593;&#x6B20;&#x671F;&#x3066;&#x3093;&#x304B;&#x3093;&#x653E;&#x96FB;&#x306E;&#x540C;&#x5B9A;&#x3088;&#x308A;&#x306E;&#x8CA0;&#x306E;&#x4E88;&#x6E2C;&#x5024;&#x306E;&#x4E21;&#x65B9;&#x304C;&#x3042;&#x308A;&#x307E;&#x3059;&#x30B3;&#x30F3;&#x30D4;&#x30E5;&#x30FC;&#x30BF;&#x30FC;&#x652F;&#x63F4;&#x8A3A;&#x65AD; (CAD) &#x30C4;&#x30FC;&#x30EB;&#x3092;&#x958B;&#x767A;&#x3057;&#x307E;&#x3057;&#x305F;&#x3002;&#x6211;&#x3005; &#x306E;&#x30A2;&#x30D7;&#x30ED;&#x30FC;&#x30C1;&#x306F;&#x30D3;&#x30C7;&#x30AA;&#x8133;&#x6CE2;&#x30E2;&#x30CB;&#x30BF;&#x30EA;&#x30F3;&#x30B0;&#x306E;&#x305F;&#x3081;&#x5165;&#x9662; 156 &#x60A3;&#x8005;&#x3092;&#x5206;&#x985E;&#x3059;&#x308B;&#x306E;&#x306B;&#x591A;&#x5C64;&#x30D1;&#x30FC;&#x30BB;&#x30D7;&#x30C8;&#x30ED;&#x30F3;&#x3092;&#x4F7F;&#x7528;&#x3057;&#x307E;&#x3057;&#x305F;&#x3002;&#x60A3;&#x8005;&#x96C6;&#x56E3;&#x306F;&#x8A3A;&#x65AD;&#x591A;&#x69D8;&#x3060;&#x3063;&#x305F;;87 &#x4E00;&#x822C;&#x5316;&#x307E;&#x305F;&#x306F;&#x7126;&#x70B9;&#x767A;&#x4F5C;&#x3068;&#x8A3A;&#x65AD;&#x3055;&#x308C;&#x307E;&#x3057;&#x305F;&#x3002;&#x60A3;&#x8005;&#x306E;&#x6B8B;&#x308A;&#x306E;&#x90E8;&#x5206;&#x306F;&#x3001;nonepileptic &#x306E;&#x767A;&#x4F5C;&#x3067;&#x8A3A;&#x65AD;&#x3055;&#x308C;&#x307E;&#x3057;&#x305F;&#x3002;&#x611F;&#x5EA6;&#x306F; 92% (95% &#x4FE1;&#x983C;&#x533A;&#x9593; [CI] 85-97%) &#x3055;&#x308C;&#x8CA0;&#x306E;&#x4E88;&#x6E2C;&#x5024; 82% (95% CI 67 92%) &#x3060;&#x3063;&#x305F;&#x3002;&#x6211;&#x3005; &#x306F;&#x3069;&#x306E;&#x3088;&#x3046;&#x306B;&#x3053;&#x306E; CAD &#x30A4;&#x30D9;&#x30F3;&#x30C8;&#x306B;&#x57FA;&#x3065;&#x3044;&#x3066;&#x5206;&#x6790;&#x3092;&#x88DC;&#x5B8C;&#x3059;&#x308B;&#x305F;&#x3081;&#x306B;&#x8A13;&#x7DF4;&#x3092;&#x53D7;&#x3051;&#x305F; epileptologists &#x306B;&#x3088;&#x3063;&#x3066;&#x4F7F;&#x7528;&#x3053;&#x3068;&#x304C;&#x3067;&#x304D;&#x307E;&#x3059;&#x793A;&#x5506;&#x3092;&#x8B70;&#x8AD6;&#x3057;&#x307E;&#x3059;&#x3002;

&#x8133;&#x6CE2;&#x30D1;&#x30EF;&#x30FC; &#x30B9;&#x30DA;&#x30AF;&#x30C8;&#x30EB;&#x3092;&#x7528;&#x3044;&#x305F;&#x3066;&#x3093;&#x304B;&#x3093;&#x306E;&#x8A3A;&#x65AD;&#x306E;&#x81EA;&#x52D5;&#x5316;&#x3002;

Interictal electroencephalography (&#xB1CC; &#xD30C;) epileptiform &#xBC29;&#xC804;&#xC758; &#xBC1C;&#xC0DD;&#xC5D0; &#xB300; &#xD55C; &#xADF8;&#xAC83;&#xC758; &#xC758;&#xC874; &#xB54C;&#xBB38;&#xC5D0; &#xAC10;&#xB3C4;&#xC5D0; &#xC784;&#xC0C1;&#xC801;&#xC73C;&#xB85C; &#xC758;&#xBBF8; &#xC788;&#xB294; &#xC81C;&#xD55C; &#xBC0F; &#xAC04; &#xC9C8;&#xC758; &#xC9C4;&#xB2E8;&#xC5D0; &#xD2B9;&#xC774;&#xC131;&#xC744;&#xAC00;&#xC9C0;&#xACE0;. &#xC6B0;&#xB9AC;&#xB294; &#xC77C;&#xC0C1;&#xC801;&#xC778; &#xB1CC; &#xD30C;&#xC758; &#xC808;&#xB300; &#xC2A4;&#xD399;&#xD2B8;&#xB7FC; &#xC5D0;&#xB108;&#xC9C0;&#xC5D0;&#xC11C; &#xC791;&#xB3D9; &#xD558; &#xACE0; &#xC2E4;&#xC9C8;&#xC801;&#xC73C;&#xB85C; &#xB354; &#xB192;&#xC740; &#xAC10;&#xB3C4; &#xBD80;&#xC815;&#xC801;&#xC778; &#xC608;&#xCE21; &#xAC12; interictal epileptiform &#xBC29;&#xC804;&#xC758; &#xC2E0;&#xBD84; (CAD) &#xCEF4;&#xD4E8;&#xD130; &#xBCF4;&#xC870; &#xC9C4;&#xB2E8; &#xB3C4;&#xAD6C;&#xB97C; &#xAC1C;&#xBC1C; &#xD588;&#xC2B5;&#xB2C8;&#xB2E4;. &#xC6B0;&#xB9AC;&#xC758; &#xC811;&#xADFC; &#xBC29;&#xBC95; &#xBE44;&#xB514;&#xC624;-&#xB1CC; &#xD30C; &#xBAA8;&#xB2C8;&#xD130;&#xB9C1;&#xC5D0; &#xB300; &#xD55C; &#xC778;&#xC815; 156 &#xD658;&#xC790; &#xBD84;&#xB958;&#xB97C; &#xB2E4;&#xC911; &#xACC4;&#xCE35; &#xD37C;&#xC149;&#xD2B8;&#xB860;&#xC744; &#xC0AC;&#xC6A9;. &#xD658;&#xC790; &#xC778;&#xAD6C;&#xB294; &#xB2E4;&#xC591; &#xD55C; &#xC9C4;&#xB2E8;; 87 &#xC77C;&#xBC18;&#xD654; &#xB610;&#xB294; &#xCD08;&#xC810; &#xBC1C;&#xC791; &#xC9C4;&#xB2E8; &#xD588;&#xB2E4;. &#xD658;&#xC790;&#xC758; &#xB098;&#xBA38;&#xC9C0; nonepileptic &#xBC1C;&#xC791; &#xC9C4;&#xB2E8; &#xD588;&#xB2E4;. &#xAC10;&#xB3C4; 92% (95% &#xC2E0;&#xB8B0; &#xAD6C;&#xAC04; [CI] 85-97%)&#xACFC; &#xBD80;&#xC815;&#xC801;&#xC778; &#xC608;&#xCE21; &#xAC12;&#xC740; 82% (95% CI 67-92%). &#xC774;&#xB7EC;&#xD55C; &#xC5F0;&#xAD6C; &#xACB0;&#xACFC; &#xD6C8;&#xB828; &#xB41C; epileptologists&#xC5D0; &#xC758;&#xD574;&#xC774; CAD &#xC774;&#xBCA4;&#xD2B8; &#xAE30;&#xBC18; &#xBD84;&#xC11D;&#xC744; &#xBCF4;&#xC644; &#xD558;&#xAE30; &#xC704;&#xD574; &#xC0AC;&#xC6A9;&#xD560; &#xC218; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4; &#xC81C;&#xC548; &#xD558;&#xB294; &#xBC29;&#xBC95;&#xC744; &#xC0B4;&#xD3B4;&#xBD05;&#xB2C8;&#xB2E4;.

&#xAC04; &#xC9C8; &#xB1CC; &#xD30C; &#xD30C;&#xC6CC; &#xC2A4;&#xD399;&#xD2B8;&#xB7FC;&#xC744; &#xC0AC;&#xC6A9; &#xD558; &#xC5EC; &#xC790;&#xB3D9;&#xD654; &#xB41C; &#xC9C4;&#xB2E8;&#xC785;&#xB2C8;&#xB2E4;.

La electroencefalograf&iacute;a (EEG) interictal tiene cl&iacute;nicamente significativas limitaciones en su sensibilidad y especificidad en el diagn&oacute;stico de epilepsia por su dependencia de la ocurrencia de descargas epileptiformes. Hemos desarrollado una herramienta de diagn&oacute;stico asistido por ordenador (CAD) que act&uacute;a sobre la energ&iacute;a espectral absoluta de la rutina de EEG con tanto substancialmente m&aacute;s alta sensibilidad y valor predictivo negativo que la identificaci&oacute;n de descargas epileptiformes interictales. Nuestro enfoque utiliza un perceptr&oacute;n multicapa para clasificar 156 pacientes ingresados para la supervisi&oacute;n del v&iacute;deo-EEG. La poblaci&oacute;n de pacientes fue de diagn&oacute;stico diversa; 87 fueron diagnosticados con convulsiones generalizadas o focales. El resto de los pacientes fueron diagnosticado con asimientos nonepileptic. La sensibilidad fue de 92% (intervalo de confianza [IC] del 95% 85-97%) y el valor predictivo negativo fue de 82% (95% CI 67-92%). Discutimos c&oacute;mo estos resultados sugieren que este CAD puede utilizarse para complementar el an&aacute;lisis basado en eventos por epileptologists entrenado.

Diagn&oacute;stico automatizado de epilepsia utilizando el espectro de energ&iacute;a de EEG.

The electronic health record mandate within the American Recovery and Reinvestment Act of 2009 will have a far-reaching affect on medicine. In this article, we provide an in-depth analysis of how this mandate is expected to stimulate the production of large-scale, digitized databases of patient information. There is evidence to suggest that millions of patients and the National Institutes of Health will fully support the mining of such databases to better understand the process of diagnosing patients. This data mining likely will reaffirm and quantify known risk factors for many diagnoses. This quantification may be leveraged to further develop computer-aided diagnostic tools that weigh risk factors and provide decision support for health care providers. We expect that creation of these databases will stimulate the development of computer-aided diagnostic support tools that will become an integral part of modern medicine.

The future of medical diagnostics: large digitized databases.

The technology of fluoro-deoxyglucose positron emission tomography (PET) has drastically increased our ability to visualize the metabolic process of numerous neurological diseases. The relationship between the methodological noise sources inherent to PET technology and the resulting noise in the reconstructed image is complex. In this study, we use Monte Carlo simulations to examine the effect of Poisson noise in the PET signal on the noise in reconstructed space for two pervasive reconstruction algorithms: the historical filtered back-projection (FBP) and the more modern expectation maximization (EM). We confirm previous observations that the image reconstructed with the FBP biases all intensity values toward the mean, likely due to spatial spreading of high intensity voxels. However, we demonstrate that in both algorithms the variance from high intensity voxels spreads to low intensity voxels and obliterates their signal to noise ratio. This finding has profound impacts on the clinical interpretation of hypometabolic lesions. Our results suggest that PET is relatively insensitive when it comes to detecting and quantifying changes in hypometabolic tissue. Further, the images reconstructed with EM visually match the original images more closely, but more detailed analysis reveals as much as a 40 percent decrease in the signal to noise ratio for high intensity voxels relative to the FBP. This suggests that even though the apparent spatial resolution of EM outperforms FBP, the signal to noise ratio of the intensity of each voxel may be higher in the FBP. Therefore, EM may be most appropriate for manual visualization of pathology, but FBP should be used when analyzing quantitative markers of the PET signal. This suggestion that different reconstruction algorithms should be used for quantification versus visualization represents a major paradigm shift in the analysis and interpretation of PET images.

Poisson noise obscures hypometabolic lesions in PET.

Interictal FDG-PET (iPET) is a core tool for localizing the epileptogenic focus, potentially before structural MRI, that does not require rare and transient epileptiform discharges or seizures on EEG. The visual interpretation of iPET is challenging and requires years of epilepsy-specific expertise. We have developed an automated computer-aided diagnostic (CAD) tool that has the potential to work both independent of and synergistically with expert analysis. Our tool operates on distributed metabolic changes across the whole brain measured by iPET to both diagnose and lateralize temporal lobe epilepsy (TLE). When diagnosing left TLE (LTLE) or right TLE (RTLE) vs. non-epileptic seizures (NES), our accuracy in reproducing the results of the gold standard long term video-EEG monitoring was 82% [95% confidence interval (CI) 69-90%] or 88% (95% CI 76-94%), respectively. The classifier that both diagnosed and lateralized the disease had overall accuracy of 76% (95% CI 66-84%), where 89% (95% CI 77-96%) of patients correctly identified with epilepsy were correctly lateralized. When identifying LTLE, our CAD tool utilized metabolic changes across the entire brain. By contrast, only temporal regions and the right frontal lobe cortex, were needed to identify RTLE accurately, a finding consistent with clinical observations and indicative of a potential pathophysiological difference between RTLE and LTLE. The goal of CADs is to complement - not replace - expert analysis. In our dataset, the accuracy of manual analysis (MA) of iPET (∼80%) was similar to CAD. The square correlation between our CAD tool and MA, however, was only 30%, indicating that our CAD tool does not recreate MA. The addition of clinical information to our CAD, however, did not substantively change performance. These results suggest that automated analysis might provide clinically valuable information to focus treatment more effectively.

Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET.

The recent Chu et al. (2012) manuscript discusses two key findings regarding feature selection (FS): (1) data driven FS was no better than using whole brain voxel data and (2) a priori biological knowledge was effective to guide FS. Use of FS is highly relevant in neuroimaging-based machine learning, as the number of attributes can greatly exceed the number of exemplars. We strongly endorse their demonstration of both of these findings, and we provide additional important practical and theoretical arguments as to why, in their case, the data-driven FS methods they implemented did not result in improved accuracy. Further, we emphasize that the data-driven FS methods they tested performed approximately as well as the all-voxel case. We discuss why a sparse model may be favored over a complex one with similar performance. We caution readers that the findings in the Chu et al. report should not be generalized to all data-driven FS methods.

The utility of data-driven feature selection: Re: Chu et al. 2012.

In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent dimensions, or "topics," provide a sparse summary of the generative process behind thefeatures for each individual. Topic modeling, an unsupervised generative model, has been used to map seemingly disparate features to a common domain. We use Non-Negative Matrix Factorization (NMF) to infer the latent structure of multimodal ADHD data containing fMRI, MRI, phenotypic and behavioral measurements. We compare four different NMF algorithms and find that the sparsest decomposition is also the most differentiating between ADHD and healthy patients. We identify dimensions that map to interpretable, recognizable dimensions such as motion, default mode network activity, and other such features of the input data. For example, structural and functional graph theory features related to default mode subnetworks clustered with the ADHD inattentive diagnosis. Structural measurements of the default mode network (DMN) regions such as the posterior cingulate, precuneus, and parahippocampal regions were all related to the ADHD-Inattentive diagnosis. Ventral DMN subnetworks may have more functional connections in ADHD-I, while dorsal DMN may have less. We also find that ADHD topics may be dependent upon diagnostic site, raising the possibility of the diagnostic differences across geographic locations. We assess our findings in light of theADHD-200 classification competition, and contrast our unsupervised, nominated topics with previously published supervised learning methods. Finally, we demonstrate the validity of these latent variables as biomarkers by using them for classification of ADHD in 730 patients. Cumulatively, this manuscript addresses how multimodal data in ADHD can be interpreted by latent dimensions.

Article	Year
PI3K regulates pleckstrin-2 in T-cell cytoskeletal reorganization. Blood\| PubMed ID: 17008542	2007
Phospholipase cbeta is critical for T cell chemotaxis. Journal of immunology (Baltimore, Md. : 1950)\| PubMed ID: 17675482	2007
Automated diagnosis of epilepsy using EEG power spectrum. Epilepsia\| PubMed ID: 22967005	2012
The future of medical diagnostics: large digitized databases. The Yale journal of biology and medicine\| PubMed ID: 23012584	2012
Poisson noise obscures hypometabolic lesions in PET. The Yale journal of biology and medicine\| PubMed ID: 23239953	2012
Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET. Frontiers in neurology\| PubMed ID: 23565107	2013
The utility of data-driven feature selection: Re: Chu et al. 2012. NeuroImage\| PubMed ID: 23891886	2014
Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD. NeuroImage\| PubMed ID: 24361664	2013

Wesley T. Kerr

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