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

Performance on individual Mini-Mental State Examination (MMSE) items can predict incident Alzheimer's disease (AD). The purpose of the current study is to determine whether, in nondemented persons with and without the apolipoprotein E-4 (APOE-4) genetic risk for AD, a subset of MMSE items predict cognitive decline. Fifty-four nondemented subjects, 23 with at least one copy of the APOE-4 allele and 31 without APOE-4, were given the MMSE and cognitive tests at baseline and 2-year follow-up. MMSE total score and a subset of MMSE items including delayed recall, serial 7s, pentagon, and orientation to time and place were used to predict change on cognitive tests. The subset of MMSE items significantly predicted decline in visuo-spatial construction and naming in APOE-4 carriers but not in noncarriers. Performance on a subset of MMSE items, combined with APOE-4 genotype, may aid in identifying high-risk persons for research or follow-up.

MMSE items predict cognitive decline in persons with genetic risk for Alzheimer's disease.

Mood disturbances in methamphetamine (MA) abusers likely influence drug use, but the neurobiological bases for these problems are poorly understood.

Mood disturbances and regional cerebral metabolic abnormalities in recently abstinent methamphetamine abusers.

The ability to determine the interval and duration of sensory events is fundamental to most forms of sensory processing, including speech and music perception. Recent experimental data support the notion that different mechanisms underlie temporal processing in the subsecond and suprasecond range. Here, we examine the predictions of one class of subsecond timing models: state-dependent networks. We establish that the interval between the comparison and the test interval, interstimulus interval (ISI), in a two-interval forced-choice discrimination task, alters the accuracy of interval discrimination but not the point of subjective equality-i.e. while timing was impaired, subjective time contraction or expansion was not observed. We also examined whether the deficit in temporal processing produced by short ISIs can be reduced by learning, and determined the generalization patterns. These results show that training subjects on a task using a short or long ISI produces dramatically different generalization patterns, suggesting different forms of perceptual learning are being engaged. Together, our results are consistent with the notion that timing in the range of hundreds of milliseconds is local as opposed to centralized, and that rapid stimulus presentation rates impair temporal discrimination. This interference is, however, decreased if the stimuli are presented to different sensory channels.

Influence of the interstimulus interval on temporal processing and learning: testing the state-dependent network model.

Sex differences in age- and puberty-related maturation of human brain structure have been observed in typically developing age-matched boys and girls. Because girls mature 1-2 years earlier than boys, the present study aimed at assessing sex differences in brain structure by studying 80 adolescent boys and girls matched on sexual maturity, rather than age. We evaluated pubertal influences on medial temporal lobe (MTL), thalamic, caudate, and cortical gray matter volumes utilizing structural magnetic resonance imaging and 2 measures of pubertal status: physical sexual maturity and circulating testosterone. As predicted, significant interactions between sex and the effect of puberty were observed in regions with high sex steroid hormone receptor densities; sex differences in the right hippocampus, bilateral amygdala, and cortical gray matter were greater in more sexually mature adolescents. Within sex, we found larger volumes in MTL structures in more sexually mature boys, whereas smaller volumes were observed in more sexually mature girls. Our results demonstrate puberty-related maturation of the hippocampus, amygdala, and cortical gray matter that is not confounded by age, and is different for girls and boys, which may contribute to differences in social and cognitive development during adolescence, and lasting sexual dimorphisms in the adult brain.

Puberty influences medial temporal lobe and cortical gray matter maturation differently in boys than girls matched for sexual maturity.

Structural and metabolic abnormalities in fronto-striatal structures have been reported in children with prenatal methamphetamine (MA) exposure. The current study was designed to quantify functional alterations to the fronto-striatal circuit in children with prenatal MA exposure using functional magnetic resonance imaging (fMRI). Because many women who use MA during pregnancy also use alcohol, a known teratogen, we examined 50 children (age range 7-15), 19 with prenatal MA exposure, 15 of whom had concomitant prenatal alcohol exposure (the MAA group), 13 with heavy prenatal alcohol but no MA exposure (ALC group), and 18 unexposed controls (CON group). We hypothesized that MA exposed children would demonstrate abnormal brain activation during a visuospatial working memory (WM) "N-Back" task. As predicted, the MAA group showed less activation than the CON group in many brain areas, including the striatum and frontal lobe in the left hemisphere. The ALC group showed less activation than the MAA group in several regions, including the right striatum. We found an inverse correlation between performance and activity in the striatum in both the CON and MAA groups. However, this relationship was significant in the caudate of the CON group but not the MAA group, and in the putamen of the MAA group but not the CON group. These findings suggest that structural damage in the fronto-striatal circuit after prenatal MA exposure leads to decreased recruitment of this circuit during a WM challenge, and raise the possibility that a rewiring of cortico-striatal networks may occur in children with prenatal MA exposure.

Abnormal brain activation during working memory in children with prenatal exposure to drugs of abuse: the effects of methamphetamine, alcohol, and polydrug exposure.

Nicotine-dependent smokers exhibit craving and brain activation in the prefrontal and limbic regions when presented with cigarette-related cues. Bupropion hydrochloride treatment reduces cue-induced craving in cigarette smokers; however, the mechanism by which bupropion exerts this effect has not yet been described.

Effect of bupropion treatment on brain activation induced by cigarette-related cues in smokers.

Development of syntactic processing was examined to evaluate maturational processes including left language lateralization functions and increased specialization of brain regions important for syntactic processing. We utilized multimodal methods, including indices of brain activity from fMRI during a syntactic processing task, cortical thickness measurements from structural MRI, and neuropsychological measures. To evaluate hypotheses about increasing lateralization and specialization with development, we examined relationships between cortical thickness and magnitude and spatial activation extent within the left inferior frontal gyrus (IFG) and its right hemisphere homologue. We predicted that increased activation in the left and decreased activation in the right IFG would be associated with increased syntactic proficiency. As predicted, a more mature pattern of increased thickness in the right pars triangularis was associated with decreased activation intensity and extent in the right IFG. These findings suggest a maturational shift towards decreased involvement of the right IFG for syntactic processing. Better syntactic skills were associated with increased activation in the left IFG independent from age, suggesting increased specialization of the left IFG with increased proficiency. Overall, our findings show relationships between structural and functional neurodevelopment that co-occur with improved syntactic processing in critical language regions of the IFG in typically developing children.

fMRI of syntactic processing in typically developing children: structural correlates in the inferior frontal gyrus.

Independent component analysis (ICA) is a popular method for the analysis of functional magnetic resonance imaging (fMRI) signals that is capable of revealing connected brain systems of functional significance. To be computationally tractable, estimating the independent components (ICs) inevitably requires one or more dimension reduction steps. Whereas most algorithms perform such reductions in the time domain, the input data are much more extensive in the spatial domain, and there is broad consensus that the brain obeys rules of localization of function into regions that are smaller in number than the number of voxels in a brain image. These functional units apparently reorganize dynamically into networks under different task conditions. Here we develop a new approach to ICA, producing group results by bagging and clustering over hundreds of pooled single-subject ICA results that have been projected to a lower-dimensional subspace. Averages of anatomically based regions are used to compress the single subject-ICA results prior to clustering and resampling via bagging. The computational advantages of this approach make it possible to perform group-level analyses on datasets consisting of hundreds of scan sessions by combining the results of within-subject analysis, while retaining the theoretical advantage of mimicking what is known of the functional organization of the brain. The result is a compact set of spatial activity patterns that are common and stable across scan sessions and across individuals. Such representations may be used in the context of statistical pattern recognition supporting real-time state classification.

Large Sample Group Independent Component Analysis of Functional Magnetic Resonance Imaging Using Anatomical Atlas-Based Reduction and Bootstrapped Clustering.

Age-related changes in cortical thickness have been observed during adolescence, including thinning in frontal and parietal cortices, and thickening in the lateral temporal lobes. Studies have shown sex differences in hormone-related brain maturation when boys and girls are age-matched, however, because girls mature 1-2 years earlier than boys, these sex differences could be confounded by pubertal maturation. To address puberty effects directly, this study assessed sex differences in testosterone-related cortical maturation by studying 85 boys and girls in a narrow age range and matched on sexual maturity. We expected that testosterone-by-sex interactions on cortical thickness would be observed in brain regions known from the animal literature to be high in androgen receptors. We found sex differences in associations between circulating testosterone and thickness in left inferior parietal lobule, middle temporal gyrus, calcarine sulcus, and right lingual gyrus, all regions known to be high in androgen receptors. Visual areas increased with testosterone in boys, but decreased in girls. All other regions were more impacted by testosterone levels in girls than boys. The regional pattern of sex-by-testosterone interactions may have implications for understanding sex differences in behavior and adolescent-onset neuropsychiatric disorders.

Sex matters during adolescence: testosterone-related cortical thickness maturation differs between boys and girls.

&#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.

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 current study explores relationships between mindfulness, emotional regulation, impulsivity, and stress proneness in a sample of participants recruited in a Diagnostic and Statistical Manual of Mental Disorder Fifth Edition Field Trial for Hypersexual Disorder and healthy controls to assess whether mindfulness attenuates symptoms of hypersexuality.

Mindfulness, Emotional Dysregulation, Impulsivity, and Stress Proneness Among Hypersexual Patients.

The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independent component (IC) features derived from EEG and fMRI data. Our results demonstrate that IC features outperformed features derived from event related spectral perturbations derived from any single spectral band, yet were similar to accuracy across all spectral bands combined. We compared our diagnostic IC spatial maps with our conventional general linear model (GLM) results, and found that informative ICs had significant spatial overlap with our GLM results, yet also revealed unique regions like amygdala that were not statistically significant in GLM analyses. Overall, these results suggest that ICs may yield a parsimonious feature set that can be used along with a decision tree structure for interpretation of features used in classifying complex cognitive processes such as belief and disbelief across both fMRI and EEG neuroimaging modalities.

Article	Year
MMSE items predict cognitive decline in persons with genetic risk for Alzheimer's disease. Journal of geriatric psychiatry and neurology\| PubMed ID: 12801154	2003
Mood disturbances and regional cerebral metabolic abnormalities in recently abstinent methamphetamine abusers. Archives of general psychiatry\| PubMed ID: 14706946	2004
Influence of the interstimulus interval on temporal processing and learning: testing the state-dependent network model. Philosophical transactions of the Royal Society of London. Series B, Biological sciences\| PubMed ID: 19487189	2009
Puberty influences medial temporal lobe and cortical gray matter maturation differently in boys than girls matched for sexual maturity. Cerebral cortex (New York, N.Y. : 1991)\| PubMed ID: 20713504	2011
Abnormal brain activation during working memory in children with prenatal exposure to drugs of abuse: the effects of methamphetamine, alcohol, and polydrug exposure. NeuroImage\| PubMed ID: 21040792	2011
Effect of bupropion treatment on brain activation induced by cigarette-related cues in smokers. Archives of general psychiatry\| PubMed ID: 21199957	2011
fMRI of syntactic processing in typically developing children: structural correlates in the inferior frontal gyrus. Developmental cognitive neuroscience\| PubMed ID: 21743820	2011
Large Sample Group Independent Component Analysis of Functional Magnetic Resonance Imaging Using Anatomical Atlas-Based Reduction and Bootstrapped Clustering. International journal of imaging systems and technology\| PubMed ID: 22049263	2011
Sex matters during adolescence: testosterone-related cortical thickness maturation differs between boys and girls. PloS one\| PubMed ID: 22479458	2012
Automated diagnosis of epilepsy using EEG power spectrum. Epilepsia\| PubMed ID: 22967005	2012
Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET. Frontiers in neurology\| PubMed ID: 23565107	2013
Mindfulness, Emotional Dysregulation, Impulsivity, and Stress Proneness Among Hypersexual Patients. Journal of clinical psychology\| PubMed ID: 23852856	2013
Single trial decoding of belief decision making from EEG and fMRI data using independent components features. Frontiers in human neuroscience\| PubMed ID: 23914164	2013

Jennifer Bramen

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