Academic literature on the topic 'FMRI Data'

Multivariate analysis methods have been widely applied to functional Magnetic Resonance Imaging (fMRI) data to reveal brain activity patterns and decode brain states. Among the various multivariate analysis methods, the multivariate regression models that take high-dimensional fMRI data as inputs while using relevant regularization were proposed for voxel selection or decoding. Although some previous studies added the sparse regularization to the multivariate regression model to select relevant voxels, the selected sparse voxels cannot be used to map brain activity of each task. Compared to the sparse regularization, the Euler’s Elastica (EE) regularization that considers the spatial information of data can identify the clustered voxels of fMRI data. Our previous study added EE Regularization to Logical Regression (EELR) and demonstrated its advantages over the other regularizations in fMRI-based decoding. In this study, we further developed a multivariate regression model using EE in 3D space as constraint for voxel selection. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of EE regression model. The performance of EE regression was compared with the Generalized Linear Model (GLM) and Total Variation (TV) regression in brain activity detection, and was compared with GLM, Laplacian Smoothed L0 norm (LSL0) and TV regression methods in feature selection for brain state decoding. The results indicated that EE regression possessed better sensitivity to detect brain regions specific to a task than did GLM and better spatial detection power than TV regression. Moreover, EE regression outperformed GLM, LSL0 and TV in feature selection.

AbstractThe advent of functional magnetic resonance imaging (fMRI) of brain function 20 years ago has provided a new methodology for non-in­vasive measurement of brain function that is now widely used in cognitive neurosci­ence. Traditionally, fMRI data has been an­alyzed looking for overall activity chang­es in brain regions in response to a stimu­lus or a cognitive task. Now, recent develop­ments have introduced more elaborate, con­tent-based analysis techniques. When mul­tivariate decoding is applied to the detailed patterning of regionally-specific fMRI signals, it can be used to assess the amount of infor­mation these encode about specific task-vari­ables. Here we provide an overview of sev­eral developments, spanning from applica­tions in cognitive neuroscience (perception, attention, reward, decision making, emotion­al communication) to methodology (informa­tion flow, surface-based searchlight decod­ing) and medical diagnostics.

Classification of brain states obtained through functional magnetic resonance imaging (fMRI) poses a serious challenges for neuroimaging community to uncover discriminating patterns of brain state activity that define independent thought processes. This challenge came into existence because of the large number of voxels in a typical fMRI scan, the classifier is presented with a massive feature set coupled with a relatively small training samples. One of the most popular research topics in last few years is the application of machine learning algorithms for mental states classification, decoding brain activation, and finding the variable of interest from fMRI data. In classification scenario, different algorithms have different biases, in the sequel performances differs across datasets, and for a particular dataset the accuracy varies from classifier to classifier. To overcome the limitations of individual techniques, hybridization or fusion of these machine learning techniques emerged in recent years which have shown promising result and open up new direction of research. This paper reviews the machine learning techniques ranging from individual classifiers, ensemble, and hybrid techniques used in cognitive classification with a well balance treatment of their applications, performance, and limitations. It also discusses many open research challenges for further research.

Functional Magnetic Resonance Imaging (fMRI) is one of the most popular neuroimaging methods for investigating the activity of the human brain during cognitive tasks. As with many other neuroiroaging tools, the group analysis of fMRI data often requires a transformation of the individual datasets to a common stereotaxic space, where the different brains have a similar global shape and size. However, the local inaccuracy of this procedure gives rise to a series of issues including a lack of true anatomical correspondence and a loss of subject specific activations. Inter-subject parcellation of fMRI data has been proposed as a means to alleviate these problems. Within this frame, the inter-subject correspondence is achieved by isolating homologous functional parcels across individuals, rather than by matching voxels coordinates within a stereotaxic space. However, the large majority of parcellation methods still suffer from a number of shortcomings owing to their dependence on a general linear model. Indeed, for all its appeal, a GLM-based parcellation approach introduces its own biases in the form of a priori knowledge about such matters as the shape of the Hemodynamic Response Function (HRF) and taskrelated signal changes. In this thesis, we propose a model-free data-driven parcellation approach to singleand multi-subject parcellation. By modelling brain activation without an relying on an a priori model, parcellation is optimized for each individual subject. In order to establish correspondences of parcels across different subjects, we cast this problem as a multipartite graph partitioning task. Parcels are considered as the vertices of a weighted complete multipartite graph. Cross subject parcel matching becomes equivalent to partitioning this graph into disjoint cliques with one and only one parcel from each subject in each clique. In order to solve this NP-hard problem, we present three methods: the OBSA algorithm, a method with quadratic programming and an intuitive approach. We also introduce two quantitative measures of the quality of parcellation results. We apply our framework to two fMRI data sets and show that both our single- and multi-subject parcellation techniques rival or outperform model-based methods in terms of parcellation accuracy.

Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2007.
Title from electronic title page (viewed Apr. 25, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.

Thesis (M.S.)--University of West Florida, 2009.
Submitted to the Dept. of Computer Science. Title from title page of source document. Document formatted into pages; contains 90 pages. Includes bibliographical references.

In this thesis a simulated fMRI toolbox is developed in order to generate simulated data to compare and benchmark different functional magnetic resonance image analysis methods. This toolbox is capable of loading a high resolution anatomic brain volume, generating 4D fMRI data in the same data space with the anatomic image, and allowing the user to create block and event-related design paradigms. Common fMRI artifacts such as scanner drift, cardiac pulsation, habituation and task related or spontaneous head movement can be incorporated into the 4D fMRI data. Input to the toolbox is possible through MINC 2.0 file format, and output is provided in ANALYZE format. The major contribution of this toolbox is its facilitation of comparison of fMRI analysis methods by generating several different fMRI data under varying noise and experiment parameters.

Real-time neuroscience can be described as the use of neuroimaging techniques to extract and evaluate brain activations during their ongoing development. The possibility to track these activations opens the doors to new research modalities as well as practical applications in both clinical and everyday life. Moreover, the combination of different neuroimaging techniques, i.e. multimodality, may reduce several limitations present in each single technique. Due to the intrinsic difficulties of real-time experiments, in order to fully exploit their potentialities, advanced signal processing algorithms are needed. In particular, since brain activations are free to evolve in an unpredictable way, data-driven algorithms have the potentials of being more suitable than model-driven ones. In fact, for example, in neurofeedback experiments brain activation tends to change its properties due to training or task eects thus evidencing the need for adaptive algorithms. Blind Source Separation (BSS) methods, and in particular Independent Component Analysis (ICA) algorithms, are naturally suitable to such kind of conditions. Nonetheless, their applicability in this framework needs further investigations. The goals of the present thesis are: i) to develop a working real-time set up for performing experiments; ii) to investigate different state of the art ICA algorithms with the aim of identifying the most suitable (along with their optimal parameters), to be adopted in a real-time MRI environment; iii) to investigate novel ICA-based methods for performing real-time MRI neuroimaging; iv) to investigate novel methods to perform data fusion between EEG and fMRI data acquired simultaneously. The core of this thesis is organized around four "experiments", each one addressing one of these specic aims. The main results can be summarized as follows. Experiment 1: a data analysis software has been implemented along with the hardware acquisition set-up for performing real-time fMRI. The set-up has been developed with the aim of having a framework into which it would be possible to test and run the novel methods proposed to perform real-time fMRI. Experiment 2: to select the more suitable ICA algorithm to be implemented in the system, we investigated theoretically and compared empirically the performance of 14 different ICA algorithms systematically sampling different growing window lengths, model order as well as a priori conditions (none, spatial or temporal). Performance is evaluated by computing the spatial and temporal correlation to a target component of brain activation as well as computation time. Four algorithms are identied as best performing without prior information (constrained ICA, fastICA, jade-opac and evd), with their corresponding parameter choices. Both spatial and temporal priors are found to almost double the similarity to the target at not computation costs for the constrained ICA method. Experiment 3: the results and the suggested parameters choices from experiment 2 were implemented to monitor ongoing activity in a sliding-window approach to investigate different ways in which ICA-derived a priori information could be used to monitor a target independent component: i) back-projection of constant spatial information derived from a functional localizer, ii) dynamic use of temporal , iii) spatial, or both iv) spatial-temporal ICA constrained data. The methods were evaluated based on spatial and/or temporal correlation with the target IC component monitored, computation time and intrinsic stochastic variability of the algorithms. The results show that the back-projection method offers the highest performance both in terms of time course reconstruction and speed. This method is very fast and effective as far as the monitored IC has a strong and well defined behavior, since it relies on an accurate description of the spatial behavior. The dynamic methods oer comparable performances at cost of higher computational time. In particular the spatio-temporal method performs comparably in terms of computational time to back-projection, offering more variable performances in terms of reconstruction of spatial maps and time courses. Experiment 4: finally, Higher Order Partial Least Square based method combined with ICA is proposed and investigated to integrate EEG-fMRI data acquired simultaneously. This method showed to be promising, although more experiments are needed.

High-dimensional functional magnetic resonance imaging (fMRI) data is characterized by complex spatial and temporal patterns related to neural activation. Mixture based Bayesian spatio-temporal modelling is able to extract spatiotemporal components representing distinct haemodyamic response and activation patterns. A recent development of such approach to fMRI data analysis is so-called spatially regularized mixture model of hidden process models (SMM-HPM). SMM-HPM can be used to reduce the four-dimensional fMRI data of a pre-determined region of interest (ROI) to a small number of spatio-temporal prototypes, sufficiently representing the spatio-temporal features of the underlying neural activation. Summary statistics derived from these features can be interpreted as quantification of (1) the spatial extent of sub-ROI activation patterns, (2) how fast the brain respond to external stimuli; and (3) the heterogeneity in single ROIs. This thesis aims to extend the single-subject SMM-HPM to a multi-subject SMM-HPM so that such features can be extracted at group-level, which would enable more robust conclusion to be drawn.

In this thesis, we develop some novel methods for univariate and multivariate analyses of additive genetic factors including heritability and genetic correlation. For the univariate heritability analysis, we present 3 newly proposed estimation methods—Frequentist ReML, LR-SD and LR-SD ReML. The comparison of these novel and those currently available approaches demonstrates the non-iterative LRSD method is extremely fast and free of any convergence issues. The properties of this LR-SD method motivate the use of the non-parametric permutation and bootstrapping inference approaches. The permutation framework also allows the utilization of spatial statistics, which we find increases the statistical sensitivity of the test. For the bivariate genetic analysis, we generalize the univariate LR-SD method to the bivariate case, where the integration of univariate and bivariate LR-SD provides a new estimation method for genetic correlation. Although simulation studies show that our measure of genetic correlation is not ideal, we propose a closely related test statistic based on the ERV, which we show to be a valid hypothesis test for zero genetic correlation. The rapid implementation of this ERV estimator makes it feasible to use with permutation as well. Finally, we consider a method for high-dimensional multivariate genetic analysis based on pair-wise correlations of different subject pairs. While traditional genetic analysis models the correlation over subjects to produce an estimate of heritability, this approach estimates correlation over a (high-dimensional) phenotype for pairs of subjects, and then estimates heritability based on the difference in MZ-pair and DZ-pair correlations. A significant two-sample t-test comparing MZ and DZ correlations implies the existence of heritable elements. The resulting summary measure of aggregate heritability, defined as twice the difference of MZ and DZ mean correlations, can be treated as a quick screening estimate of whole-phenotype heritability that is closely related to the average of traditional heritability.

Negli ultimi anni la teoria dei network è stata applicata agli ambiti più diversi, mostrando proprietà caratterizzanti tutti i network reali. In questo lavoro abbiamo applicato gli strumenti della teoria dei network a dati cerebrali ottenuti tramite MRI funzionale “resting”, provenienti da due esperimenti. I dati di fMRI sono particolarmente adatti ad essere studiati tramite reti complesse, poiché in un esperimento si ottengono tipicamente più di centomila serie temporali per ogni individuo, da più di 100 valori ciascuna. I dati cerebrali negli umani sono molto variabili e ogni operazione di acquisizione dati, così come ogni passo della costruzione del network, richiede particolare attenzione. Per ottenere un network dai dati grezzi, ogni passo nel preprocessamento è stato effettuato tramite software appositi, e anche con nuovi metodi da noi implementati. Il primo set di dati analizzati è stato usato come riferimento per la caratterizzazione delle proprietà del network, in particolare delle misure di centralità, dal momento che pochi studi a riguardo sono stati condotti finora. Alcune delle misure usate indicano valori di centralità significativi, quando confrontati con un modello nullo. Questo comportamento `e stato investigato anche a istanti di tempo diversi, usando un approccio sliding window, applicando un test statistico basato su un modello nullo pi`u complesso. Il secondo set di dati analizzato riguarda individui in quattro diversi stati di riposo, da un livello di completa coscienza a uno di profonda incoscienza. E' stato quindi investigato il potere che queste misure di centralità hanno nel discriminare tra diversi stati, risultando essere dei potenziali bio-marcatori di stati di coscienza. E’ stato riscontrato inoltre che non tutte le misure hanno lo stesso potere discriminante. Secondo i lavori a noi noti, questo `e il primo studio che caratterizza differenze tra stati di coscienza nel cervello di individui sani per mezzo della teoria dei network.

CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.

CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.

CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.

En el transcurso del primer semestre de 2023 la economía colombiana continuó avanzando en el proceso de ajuste requerido para corregir los desequilibrios macroeconómicos y controlar las presiones inflacionarias acumuladas tras los diversos choques de oferta y la rápida expansión de la demanda durante 2021 y 2022, la cual superó el crecimiento potencial de la economía. El ajuste económico en curso ha sido posible gracias a la moderación del crecimiento de la demanda interna y a la progresiva disolución de los choques de oferta que elevaron los costos de producción. La demanda interna comenzó a desacelerase en los últimos meses de 2022 y se contrajo un -0,2 % en el primer trimestre de este año, debido al menor crecimiento del consumo de los hogares y a la caída de la formación bruta de capital. Por su parte, los menores precios internacionales de las materias primas, la paulatina normalización de las cadenas de suministro y la apreciación de la tasa de cambio han contribuido a disipar los choques de oferta, lo cual se ha reflejado en una disminución de la inflación anual de precios al productor desde un nivel del 19,2 % a finales de 2022 al 4,7 % en junio de 2023 1. El menor dinamismo de la demanda interna se ha venido reflejando en una desaceleración progresiva de la actividad económica. Es así como en el primer trimestre de 2023 el PIB registró un crecimiento anual del 3,0 %, ritmo equivalente a una tercera parte del crecimiento promedio anual que se observó durante los tres primeros trimestres de 2022 (9,1 %). Según el indicador de seguimiento de la economía (ISE) que elabora el DANE, esta pérdida de dinamismo continuó en abril y mayo, al registrarse variaciones de ese indicador del -0,8 % y 0,6 %, respectivamente, frente a los mismos meses de 2022. Estos resultados fueron inferiores a lo observado en marzo (1,4 %), en la serie del ISE ajustada por efecto estacional y calendario. Ello apunta a que el crecimiento del PIB seguirá declinando en el segundo trimestre, lo cual es coherente con el pronóstico de crecimiento del PIB ligeramente inferior al 1,0 % para 2023 elaborado por el equipo técnico. A pesar de la desaceleración económica en curso, el mercado laboral sigue mostrando fortaleza, como se deduce del continuo descenso de la tasa de desempleo en el agregado nacional hasta el trimestre móvil terminado en mayo (10,4 %), su valor más bajo desde el inicio de la pandemia del covid-19 2. La desaceleración de la actividad económica es un fenómeno que se anticipaba, en parte, como resultado de la política monetaria restrictiva que adoptó el Banco de la República para controlar las presiones inflacionarias. A la menor actividad económica también estaría contribuyendo una política fiscal menos expansionista que en 2022, según se contempla en los pronósticos presentados en el Marco Fiscal de Mediano Plazo de 2023 (MFMP-23). A esto se añadió una desaceleración de la demanda externa relevante para el país debido al menor crecimiento de los socios comerciales, en un contexto internacional de altas tasas de interés de política monetaria, tasas de inflación por encima de sus metas y elevada incertidumbre generada por la prolongación de la invasión de Rusia a Ucrania. La Junta Directiva del Banco de la República (JDBR) ha sido reiterativa en sus diversas comunicaciones sobre la necesidad de adelantar este proceso de ajuste, para lograr el retorno gradual de la inflación a la meta del 3 %, corregir los desequilibrios macroeconómicos y asegurar la sostenibilidad del crecimiento económico en el largo plazo. La responsabilidad constitucional que recae sobre el Banco de la República, sobre la cual se hizo énfasis en el pasado Informe al Congreso, exige mantener una inflación baja y estable en consonancia con la política económica en general, de manera que permita apoyar un crecimiento económico sostenible y un balance externo financiable. Las decisiones de política monetaria se han adoptado con el respaldo de la sólida base institucional y técnica que soporta el esquema de inflación objetivo, cimentada en la experiencia acumulada durante más de dos décadas por el banco central. Los motivos específicos que ha tenido la JDBR para emprender un proceso de ajuste monetario, el más fuerte desde que el Banco de la República adoptó la estrategia de inflación objetivo, han sido complejos y de diversa índole. Las presiones inflacionarias provinieron inicialmente de choques de oferta de origen externo e interno que presionaron al alza los costos y precios de los alimentos y otros productos de consumo, a las cuales se agregaron presiones de origen cambiario. Estos choques llevaron a un incremento de las expectativas de inflación, lo cual desató un proceso de indexación de precios, que se exacerbó debido a los excesos de demanda que surgieron en 2022. La respuesta de la política monetaria mediante el incremento de las tasas de interés buscaba reducir los excesos de demanda, contener el aumento de las expectativas y limitar los efectos de la indexación de precios. Todo ello crea las condiciones propicias para permitir que, a medida que los choques de oferta cedan y se alivien, y con ello las presiones de costos, la inflación empiece a reducirse. Este es un proceso que se cumple con cierto rezago, pero que, dado el tiempo que la política monetaria lleva actuando, ya se ha empezado a producir, como lo mostró la reciente disminución de la tasa de inflación y la revisión a la baja de sus expectativas a diferentes plazos. Acorde con el mandato constitucional de asegurar una coordinación de la política monetaria con la política económica general, además de mitigar las presiones inflacionarias, el ajuste monetario viene cumpliendo el propósito de corregir los desequilibrios macroeconómicos que ponen en riesgo la estabilidad de la economía colombiana. Al respecto, no cabe duda de que el crecimiento del PIB del 7,3 % en 2022 fue sobresaliente, al haber más que duplicado el crecimiento mundial (3,5 %) y superado ampliamente la expansión de América Latina y el Caribe (3,9 %), según cifras del FMI. Un dinamismo económico tan elevado trae importantes ganancias de bienestar, en particular cuando permite reducir las tasas de desempleo, como ha venido ocurriendo en Colombia; sin embargo, es un crecimiento insostenible en tanto se fundamenta en una situación fiscal ampliamente deficitaria y en un preocupante incremento en el endeudamiento de los hogares. Ello generó un exceso de demanda agregada que no solo presionó al alza la inflación y sus expectativas, sino que también amplió el déficit de la cuenta corriente de la balanza de pagos a niveles históricamente altos durante varios años. El déficit de la cuenta corriente aumentó desde un nivel del 5,6 % del PIB en 2021, que ya era elevado, a uno del 6,2 % del PIB en 2022, uno de los más altos observados en Colombia. La ampliación del desbalance externo en 2022 se produjo en un año en el que los precios internacionales del petróleo, el carbón y el café se mantuvieron en niveles favorables, lo que contribuyó al buen desempeño de las exportaciones. No obstante, para cubrir los faltantes de oferta, la economía incrementó de manera importante su demanda de importaciones, impidiendo una reducción del desbalance externo. Como consecuencia, la economía colombiana recurrió a un mayor endeudamiento externo, bien sea como flujo de inversión de portafolio o como endeudamiento directo. Todo esto muestra la vulnerabilidad que significa para la economía mantener un nivel de gasto que supera significativamente sus ingresos. La política monetaria restrictiva, junto con el aumento en la carga tributaria, han venido induciendo un ajuste progresivo de estos desequilibrios. La desaceleración de la demanda interna iniciada a partir del cuarto trimestre de 2022 ocurrió de la mano de una moderación del consumo de los hogares, cuyo crecimiento en el primer trimestre de 2023 fue del 3,0 %, comparado con un incremento del 9,5 % en 2022. Esto último se ha reflejado en una desaceleración del crédito de consumo, que pasó de crecer desde un ritmo cercano al 23 % anual a finales del tercer trimestre del año anterior, a algo menos del 7,0 % anual a mediados de junio de 2023. De haberse continuado con una expansión tan rápida del crédito de consumo, se habría podido generar una situación insostenible sobre la capacidad de pago de los hogares. Asimismo, la formación bruta de capital, que tuvo un desempeño sobresaliente en 2022, empezó a mostrar ajustes en sus principales componentes. La principal fuente de dicha corrección ha sido la inversión en maquinaria y equipo, que en el primer trimestre del año presentó caídas tanto en términos trimestrales como anuales, principalmente en el rubro de equipo de transporte. A la par con el avance en la corrección de los desequilibrios macroeconómicos, la inflación total interrumpió la tendencia creciente que mantuvo hasta marzo de 2023. En efecto, luego de alcanzar un nivel del 13,1 % al cierre del año anterior, la inflación total se mantuvo estable alrededor del 13,3 % durante los primeros tres meses de 2023, y a partir de abril empezó a descender, para ubicarse en 12,1 % en junio. Los alimentos han sido el rubro que más ha contribuido a este cambio de tendencia, al haber reducido su variación anual del 27,8 % en diciembre pasado al 14,3 % en junio. Esta variación ha sido compensada en alguna medida por el incremento de la inflación de regulados, debido a los sucesivos aumentos en los precios de la gasolina. Por su parte, la inflación básica (sin alimentos ni regulados) continúa mostrando rigidez, al ubicarse en el 10,5 % en junio, lo que refleja procesos de indexación de precios. La subcanasta de servicios ha sido especialmente afectada por el fenómeno de indexación, a lo cual se han agregado las presiones de costos laborales, el aumento en los precios de los alimentos que han presionado al alza las comidas fuera del hogar y la elevada demanda por servicios de entretenimiento. Este comportamiento debería ir cediendo a medida que los efectos de la política monetaria terminen por transmitirse a la economía, y la tendencia decreciente de la inflación se refleje en una revisión a la baja de las expectativas de variación de precios por parte del público. Así lo prevén los pronósticos del equipo técnico y las expectativas del mercado que anticipan una inflación decreciente durante los próximos dos años. ____________________________________________________________ 1 Corresponde a la variación anual del IPP de oferta interna. 2 Al cierre de este Informe se conocieron los datos de la Gran Encuesta Integrada de Hogares de junio, con los cuales la tasa de desempleo se mantuvo estable en su medición desestacionalizada del agregado nacional para el trimestre móvil (10,3 %), aunque con una reducción para el dato puntual de junio