Search

Los resultados aportan mediciones de la actividad gamma tanto fase como no fase obtenida mediante la técnica "Temporal Spectral Evolution (TSE)". Se aportan datos de latencia, amplitud y exportaciones de mapas para correlaciones topográficas en formato excel. Los datos corresponden a sujetos sanos en dos mediciones temporales para evaluar replicabilidad, ante un paradigma "oddball visual".

Introducción: Comprender las alteraciones en la anatomía y función del cerebro en los procesos cognitivos para las enfermedades neurodegenerativas es aún un desafío para la neurociencia actual. Desde la neurociencia experimental algunos test computarizados han sido desarrollados para mejorar nuestro conocimiento de las redes neurales involucradas en la cognición. El Attention Network Test (ANT) permite medir la activad de las 3 redes atencionales (alerta, orientación y función ejecutiva). Objetivos: El principal objetivo de esta revisión fue describir todas las alteraciones anatómicas y funcionales encontradas en diversas enfermedades neurológicas usando el ANT. Material y métodos: Un protocolo de revisión fue aplicado seleccionando estudios desde 2010 en la base de datos PubMed, que involucraban al ATN en diferentes enfermedades neurológicas. Se obtuvieron 32 artículos para esclerosis múltiple, epilepsia o Parkinson entre otras enfermedades. Conclusiones: Se confirman algunas de las estructuras anatómicas propuestas para el modelo de 3 grandes redes atencionales. Las estructuras más relevantes para la red de alerta son la corteza prefrontal, las regiones parietales, el tálamo y el cerebelo. El tálamo es también relevante para la red de orientación, junto a regiones parietales posteriores. Respecto a la red ejecutiva no depende exclusivamente de la corteza prefrontal y corteza cingulada anterior, sino también de estructuras subcorticales como los ganglios basales y el cerebelo y sus proyecciones hacia toda la corteza., Introduction: Understanding alterations to brain anatomy and cognitive function associated with neurodegenerative diseases remains a challenge for neuroscience today. In experimental neuroscience, several computerised tests have been developed to contribute to our understanding of neural networks involved in cognition. The Attention Network Test (ANT) enables us to measure the activity of 3 attentional networks (alertness, orienting, and executive function). Objectives: The main aim of this review is to describe all the anatomical and functional alterations found in diverse neurological diseases using the ANT. Material and methods: We collected studies published since 2010 in the PubMed database that employed the ANT in different neurological diseases. Thirty-two articles were obtained, addressing multiple sclerosis, epilepsy, and Parkinson’s disease, among other disorders. Conclusions: Some of the anatomical structures proposed in the 3 attentional networks model were confirmed. The most relevant structures in the alertness network are the prefrontal cortex, parietal region, thalamus, and cerebellum. The thalamus is also relevant in the orienting network, together with posterior parietal regions. The executive network does not depend exclusively on the prefrontal cortex and anterior cingulate cortex, but also involves such subcortical structures as the basal ganglia and cerebellum and their projections towards the entire cortex.

Introducción: La aplicación de la inteligencia artificial y en particular de algoritmos de aprendizaje automático o «machine learning» (ML) constituye un desafío y al mismo tiempo una gran oportunidad en diversas disciplinas científicas, técnicas y clínicas. Las aplicaciones específicas en el estudio de la esclerosis múltiple (EM) no han sido una excepción mostrando un creciente interés en los últimos años. Objetivo: Realizar una revisión sistemática de la aplicación de algoritmos de ML en la EM. Material y métodos: Empleando el motor de búsqueda de libre acceso PubMed que accede a la base de datos MEDLINE, se seleccionaron aquellos estudios que incluyeran simultáneamente los dos siguientes conceptos de búsqueda: «machine learning» y «multiple sclerosis». Se rechazaron aquellos estudios que fueran revisiones, estuvieran en otro idioma que no fuera el castellano o el inglés, y aquellos trabajos que tuvieran un carácter técnico y no fueran aplicados para la EM. Se seleccionaron como válidos 76 artículos y fueron rechazados 38. Conclusiones: Tras la revisión de los estudios seleccionados, se pudo observar que la aplicación del ML en la EM se concentró en cuatro categorías: 1) clasificación de subtipos de pacientes dentro de la enfermedad; 2) diagnóstico del paciente frente a controles sanos u otras enfermedades; 3) predicción de la evolución o de la respuesta a intervenciones terapéuticas y por último 4) otros enfoques. Los resultados hallados hasta la fecha muestran que los diferentes algoritmos de ML pueden ser un gran apoyo para el profesional sanitario tanto en la clínica como en la investigación de la EM., Introduction: The applications of artificial intelligence, and in particular automatic learning or “machine learning” (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years. Objective: We present a systematic review of the application of ML algorithms in MS. Materials and methods: We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords “machine learning” and “multiple sclerosis.” We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected. Conclusions: After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

The aim of this study was to describe the spectral features of pre-stimulus event-related potential (ERP) components elicited in visual tasks such as the Bereitschaftspotential (BP), prefrontal negativity (pN) and visual negativity (vN). ERPs are considered time-locked and phase-locked (evoked) activity, but we have also analyzed the non-phase but time-locked (induced) activity in the same interval by applying the temporal spectral evolution (TSE) method. Participants (N = 26) were tested in a passive task, a simple response task (SRT) and a discriminative response task (DRT), where EEG activity was recorded with 64 scalp electrodes. We analyzed the time-frequency modulations (phase and non-phase) prior to the onset of the stimuli in the sub-delta, delta, theta, alpha, beta, and gamma frequency bands. The results showed that all the pre-stimulus ERP components were mainly regulated by evoked activity in the sub-delta band. On the other hand, induced activity seems to be linked to evoked responses but with a different psychophysiological role. We concluded that other preparatory cognitive mechanisms associated with ERPs can also be detected by the TSE method. This finding may suggest underlying mechanisms in non-phase activity and requires the addition of non-phase activity analysis to the traditional analysis (phase and evoked activity).

The aim of this study was to describe the spectral features of pre-stimulus event-related potential (ERP) components elicited in visual tasks such as the Bereitschaftspotential (BP), prefrontal negativity (pN) and visual negativity (vN). ERPs are considered time-locked and phase-locked (evoked) activity, but we have also analyzed the non-phase but time-locked (induced) activity in the same interval by applying the temporal spectral evolution (TSE) method. Participants (N = 26) were tested in a passive task, a simple response task (SRT) and a discriminative response task (DRT), where EEG activity was recorded with 64 scalp electrodes. We analyzed the time-frequency modulations (phase and non-phase) prior to the onset of the stimuli in the sub-delta, delta, theta, alpha, beta, and gamma frequency bands. The results showed that all the pre-stimulus ERP components were mainly regulated by evoked activity in the sub-delta band. On the other hand, induced activity seems to be linked to evoked responses but with a different psychophysiological role. We concluded that other preparatory cognitive mechanisms associated with ERPs can also be detected by the TSE method. This finding may suggest underlying mechanisms in non-phase activity and requires the addition of non-phase activity analysis to the traditional analysis (phase and evoked activity).

Recent studies have suggested that nonphase-locked activity can reveal cognitive mechanisms that cannot be observed in phase-locked activity. In fact, we describe a concomitant decrease in nonphase-locked alpha activity (desynchronization) when stimuli were processed (alpha phase-locked modulation). This desynchronization may represent a reduction in “background activity” in the visual cortex that facilitates stimulus processing. Alternatively, nonphase-locked gamma activity has been hypothesized to be an index of shifts in attentional focus. In this study, our main aim was to confirm these potential roles for nonphase-locked alpha and gamma activities with a lateralized Go/NoGo paradigm. The results showed that nonphase-locked alpha modulation is bilaterally represented in the scalp compared to the contralateral distribution of the phase-locked response. This finding suggests that the decrease in background activity is not limited to neural areas directly involved in the visual processing of stimuli. Additionally, gamma activity showed a higher desynchronization of nonphase-locked activity in the ipsilateral hemisphere, where the phase-locked activity reached the minimum amplitude. This finding suggests that the possible functions of nonphase-locked gamma activity extend beyond shifts in attentional focus and could represent an attentional filter reducing the gamma representation in the visual area irrelevant to the task.

Currently, there is scarce knowledge about the relation between spectral bands modulations and the basis of cognitive impairment in multiple sclerosis (MS). In this sense, analyzing the evoked or phase activity can confirm results from traditional event-related potential (ERP) studies. However, studying the induced or nonphase activity may be necessary to elucidate hidden compensatory or affected cognitive mechanisms. In this study, 30 remitting-relapsing multiple sclerosis patients and 30 healthy controls (HCs) matched in sociodemographic variables performed a visual oddball task. The main goal was to analyze phase and nonphase alpha and gamma bands by applying temporal spectral evolution (TSE) and its potential relation with cognitive impairment in these patients. The behavioural results showed slower reaction time and poorer accuracy in MS patients compared to controls. In contrast, the time-frequency analysis of electroencephalography (EEG) revealed a delay in latency and lower amplitude in MS patients in evoked and induced alpha compared to controls. With respect to the gamma band, there were no differences between the groups. In summary, MS patients showed deficits in early sensorial (evoked alpha activity) and cognitive processing (induced alpha activity in longer latencies), whereas the induced gamma band supported the hypothesis of its role in translation of attentional focus (induced activity) and did not show strong activity in this paradigm (visual oddball).

Introducción: La aplicación de la inteligencia artificial y en particular de algoritmos de aprendizaje automático o «machine learning» (ML) constituye un desafío y al mismo tiempo una gran oportunidad en diversas disciplinas científicas, técnicas y clínicas. Las aplicaciones específicas en el estudio de la esclerosis múltiple (EM) no han sido una excepción mostrando un creciente interés en los últimos años. Objetivo: Realizar una revisión sistemática de la aplicación de algoritmos de ML en la EM. Material y métodos: Empleando el motor de búsqueda de libre acceso PubMed que accede a la base de datos MEDLINE, se seleccionaron aquellos estudios que incluyeran simultáneamente los dos siguientes conceptos de búsqueda: «machine learning» y «multiple sclerosis». Se rechazaron aquellos estudios que fueran revisiones, estuvieran en otro idioma que no fuera el castellano o el inglés, y aquellos trabajos que tuvieran un carácter técnico y no fueran aplicados para la EM. Se seleccionaron como válidos 76 artículos y fueron rechazados 38. Conclusiones: Tras la revisión de los estudios seleccionados, se pudo observar que la aplicación del ML en la EM se concentró en cuatro categorías: 1) clasificación de subtipos de pacientes dentro de la enfermedad; 2) diagnóstico del paciente frente a controles sanos u otras enfermedades; 3) predicción de la evolución o de la respuesta a intervenciones terapéuticas y por último 4) otros enfoques. Los resultados hallados hasta la fecha muestran que los diferentes algoritmos de ML pueden ser un gran apoyo para el profesional sanitario tanto en la clínica como en la investigación de la EM., Introduction: The applications of artificial intelligence, and in particular automatic learning or “machine learning” (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years. Objective: We present a systematic review of the application of ML algorithms in MS. Materials and methods: We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords “machine learning” and “multiple sclerosis.” We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected. Conclusions: After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

Some of the anatomical and functional basis of cognitive impairment in multiple sclerosis (MS) currently remains unknown. In particular, there is scarce knowledge about modulations in induced EEG (nonphase activity) for diverse frequency bands related to attentional deficits in this pathology. The present study analyzes phase and nonphase alpha and gamma modulations in 26 remitting relapsing multiple sclerosis patients during their participation in the attention network test compared with twenty-six healthy controls (HCs) matched in sociodemographic variables. Behavioral results showed that the MS group exhibited general slowing, suggesting impairment in alerting and orienting networks, as has been previously described in other studies. Time–frequency analysis of EEG revealed that the gamma band was related to the spatial translation of the attentional focus, and the alpha band seemed to be related to the expectancy mechanisms and cognitive processing of the target. Moreover, phase and nonphase modulations differed in their psychophysiological roles and were affected differently in the MS and HC groups. In summary, nonphase modulations can unveil hidden cognitive mechanisms for phase analysis and complete our knowledge of the neural basis of cognitive impairment in multiple sclerosis pathology.

Nowadays it is still unknown some of the anatomical and functional basis of cognitive impairment in multiple sclerosis (MS). In particular, there is scarce knowledge about links between attentional deficits in MS and diverse frequency bands. The present study analyzes phase and non-phase alpha and gamma modulations in 26 remitting-relapsing multiple sclerosis patients during their participation in the attention network test (ANT) and compared with twenty-six healthy controls (HC) matched in sociodemographic variables. Behavioral results showed that MS group exhibited a general slowing suggesting impairment in alerting and orienting networks as has been previously described by other studies. Time-frequency analysis of EEG revealed that gamma band was related to the spatial translation of the attentional focus, whereas alpha band seems related to the expectancy mechanisms and cognitive processing of the target. Moreover, phase and non-phase modulations differed in their psychophysiological roles and were affected differently between MS and HC groups. Summing up, non-phase modulations can unveil hidden cognitive mechanisms for the phase analysis and complete our knowledge of the cognitive impairment neural basis in multiple sclerosis pathology.

Introducción: Comprender las alteraciones en la anatomía y función del cerebro en los procesos cognitivos para las enfermedades neurodegenerativas es aún un desafío para la neurociencia actual. Desde la neurociencia experimental algunos test computarizados han sido desarrollados para mejorar nuestro conocimiento de las redes neurales involucradas en la cognición. El Attention Network Test (ANT) permite medir la activad de las 3 redes atencionales (alerta, orientación y función ejecutiva). Objetivos: El principal objetivo de esta revisión fue describir todas las alteraciones anatómicas y funcionales encontradas en diversas enfermedades neurológicas usando el ANT. Material y métodos: Un protocolo de revisión fue aplicado seleccionando estudios desde 2010 en la base de datos PubMed, que involucraban al ATN en diferentes enfermedades neurológicas. Se obtuvieron 32 artículos para esclerosis múltiple, epilepsia o Parkinson entre otras enfermedades. Conclusiones: Se confirman algunas de las estructuras anatómicas propuestas para el modelo de 3 grandes redes atencionales. Las estructuras más relevantes para la red de alerta son la corteza prefrontal, las regiones parietales, el tálamo y el cerebelo. El tálamo es también relevante para la red de orientación, junto a regiones parietales posteriores. Respecto a la red ejecutiva no depende exclusivamente de la corteza prefrontal y corteza cingulada anterior, sino también de estructuras subcorticales como los ganglios basales y el cerebelo y sus proyecciones hacia toda la corteza., Introduction: Understanding alterations to brain anatomy and cognitive function associated with neurodegenerative diseases remains a challenge for neuroscience today. In experimental neuroscience, several computerised tests have been developed to contribute to our understanding of neural networks involved in cognition. The Attention Network Test (ANT) enables us to measure the activity of 3 attentional networks (alertness, orienting, and executive function). Objectives: The main aim of this review is to describe all the anatomical and functional alterations found in diverse neurological diseases using the ANT. Material and methods: We collected studies published since 2010 in the PubMed database that employed the ANT in different neurological diseases. Thirty-two articles were obtained, addressing multiple sclerosis, epilepsy, and Parkinson’s disease, among other disorders. Conclusions: Some of the anatomical structures proposed in the 3 attentional networks model were confirmed. The most relevant structures in the alertness network are the prefrontal cortex, parietal region, thalamus, and cerebellum. The thalamus is also relevant in the orienting network, together with posterior parietal regions. The executive network does not depend exclusively on the prefrontal cortex and anterior cingulate cortex, but also involves such subcortical structures as the basal ganglia and cerebellum and their projections towards the entire cortex.