Hand classification of fMRI ICA noise components

We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets

State and trait characteristics of anterior insula time-varying functional connectivity.

The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures

Glioblastoma induces whole-brain spectral change in resting state fMRI: Associations with clinical comorbidities and overall survival

Glioblastoma, a highly aggressive form of brain tumor, is a brain-wide disease. We evaluated the impact of tumor burden on whole brain resting-state functional magnetic resonance imaging (rs-fMRI) activity. Specifically, we analyzed rs-fMRI signals in the temporal frequency domain in terms of the power-law exponent and fractional amplitude of low-frequency fluctuations (fALFF). We contrasted 189 patients with newly-diagnosed glioblastoma versus 189 age-matched healthy reference participants from an external dataset. The patient and reference datasets were matched for age and head motion. The principal finding was markedly flatter spectra and reduced grey matter fALFF in the patients as compared to the reference dataset. We posit that the whole-brain spectral change is attributable to global dysregulation of excitatory and inhibitory balance and metabolic demand in the tumor-bearing brain. Additionally, we observed that clinical comorbidities, in particular, seizures, and MGMT promoter methylation, were associated with flatter spectra. Notably, the degree of change in spectra was predictive of overall survival. Our findings suggest that frequency domain analysis of rs-fMRI activity provides prognostic information in glioblastoma patients and offers a means of noninvasively studying the effects of glioblastoma on the whole brain

Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications

Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163

Effect of scan time on resting state parameters

In the past decade the interest in studying the spontaneous low-frequency fluctuations (LFF) in a resting-state brain has steadily grown. By measuring LFF (\u3c 0.08 Hz) in blood-oxygen-level-dependent (BOLD) signals, resting-state functional magnetic resonance imaging (rs-fMRI) has proven to be a powerful tool in exploring brain network connectivity and functionality. Rs-fMRI data can be used to organize the brain into resting state networks (RSNs). In this thesis, rs-fMRI data are used to determine the minimum data acquisition time necessary to detect local intrinsic brain activity as a function of both the amplitude of low frequency fluctuations (ALFF) and the fractional amplitude of low frequency fluctuations (fALFF) in BOLD signals in healthy subjects. The data are obtained from 22 healthy subjects to use as a baseline for future rs-fMRI analysis. Voxel-wise analysis is performed on the whole brain, gray matter volume, and two previously established RSNs: the default mode network (DMN) and the visual system network, for all the subjects in this study. Pearson’s correlation coefficients (r-values) are calculated from each subject. The entire time series for one subject is divided into 31 subsections and the r-values are calculated between each consecutive subsection in a subject. In total, there are 30 r- values. To better understand what the results mean across subjects and within subjects Fisher transformations are applied to the 30 calculated r-values for each subject to get a normal z-distribution. The mean across 22 subjects’ z-values is calculated for group analysis. In the end, there are 30 mean values. Finally, an exponential curve fit model is calculated across the 22 subjects using the calculated mean values, and an asymptotic growth model is used to detect the minimum data acquisition time required to obtain both ALFF and fALFF of the BOLD signals at rest. The results show that the minimum time required to detect an ALFF and fALFF of the BOLD signals at rest is 12 and 13.33 minutes respectively. Future studies can focus on determining the minimum scanner time using similar analysis for different physiological states of the brain

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Intact Bilateral Resting-State Networks in the Absence of the Corpus Callosum

Temporal correlations between different brain regions in the resting-state BOLD signal are thought to reflect intrinsic functional brain connectivity (Biswal et al., 1995; Greicius et al., 2003; Fox et al., 2007). The functional networks identified are typically bilaterally distributed across the cerebral hemispheres, show similarity to known white matter connections (Greicius et al., 2009), and are seen even in anesthetized monkeys (Vincent et al., 2007). Yet it remains unclear how they arise. Here we tested two distinct possibilities: (1) functional networks arise largely from structural connectivity constraints, and generally require direct interactions between functionally coupled regions mediated by white-matter tracts; and (2) functional networks emerge flexibly with the development of normal cognition and behavior and can be realized in multiple structural architectures. We conducted resting-state fMRI in eight adult humans with complete agenesis of the corpus callosum (AgCC) and normal intelligence, and compared their data to those from eight healthy matched controls. We performed three main analyses: anatomical region-of-interest-based correlations to test homotopic functional connectivity, independent component analysis (ICA) to reveal functional networks with a data-driven approach, and ICA-based interhemispheric correlation analysis. Both groups showed equivalently strong homotopic BOLD correlation. Surprisingly, almost all of the group-level independent components identified in controls were observed in AgCC and were predominantly bilaterally symmetric. The results argue that a normal complement of resting-state networks and intact functional coupling between the hemispheres can emerge in the absence of the corpus callosum, favoring the second over the first possibility listed above

Functional and structural substrates of increased dosage of Grik4 gene elucidated using multi-modal MRI

Grik4 is the gene responsible for encoding the high-affinity GluK4 subunit of the kainate receptors. Increased dosage of this subunit in the forebrain was linked to an increased level of anxiety, lack of social communication, and depression. On the synaptic level, abnormal synaptic transmission was also reported. The manifestations of this abnormal expression have not been investigated at the circuit level, nor the correlations between those circuits and the abnormal patterns of the behavior previously reported. In this line of work, we aspired to use different non-invasive magnetic resonance imaging (MRI) modalities to elucidate any disturbance that might stem from the increased dosage of Grik4 and how those changes might explain the abnormal behaviors. MRI offers a noninvasive way to look into the intact brain in vivo. Resting-state functional MRI casts light on how the brain function at rest on the network level and has the capability to detect any anomalies that might occur within or between those networks. On the microstructural level, the diffusion MRI is concerned with the underlying features of the tissues, using the diffusion of water molecules as a proxy for that end. Moving more macroscopically, using structural scans, voxel-based morphometry can detect subtle differences in the morphology of the different brain structures. We recorded videos of our animals performing two tasks that have long been linked to anxiety, the open field and the plus-maze tests before acquiring structural and functional scans. Lastly, we recorded blood-oxygenationlevel dependent (BOLD) signals in a different set of animals during electrical stimulation of specific white matter tracts in order to investigate how neuronal activity propagates. Our analysis showed a vast spectrum of changes in the transgenic group relative to the animals in the control group. On the resting-state networks level, we observed an increase in the within-network strength spanning different structures such as the hippocampus, some regions of the cortex, and the hypothalamus. The increased internal coherence or strength in the networks contrasted with a significant reduction in between-networks connectivity for some regions such as parts of the cortex and the hypothalamus, suggesting long-range network decorrelation. Supporting this idea, major white matter (WM) tracts, such as the corpus callosum and the hippocampal commissure, suffered from substantial changes compatible with an important reduction in myelination and/or a decrease in the mean axonal diameter. Macrostructurally speaking, the overexpression of GluK4 subunit had a bimodal effect, with expansion in some cortical areas in the transgenic animals accompanied by a shrinkage in the subcortical regions. Upon stimulating the brain with an electrical current, we noticed a difference in activity propagation between the two hemispheres. In transgenic animals, the evoked activity remained more confined to the stimulated hemisphere, again consistent with an impaired long-range connectivity. The structural changes both, at the micro and macro level, were in tight correlation with different aspects of the behavior including markers of anxiety such as the time spent in the open arms vs the closed arms in the plus-maze test and the time spent in the center vs the corners in the open field test. Our findings reveal how the disruption of kainate receptors, or more globally the glutamate receptors, and the abnormal synaptic transmission can translate into brain-wide changes in connectivity and alter the functional equilibrium between macro-and mesoscopic networks. The postsynaptic enhancement previously reported in the transgenic animals was here reflected in the BOLD signal and measured as an increase in the within-network strength. Importantly, the correlations between the structural changes and the behavior help to put the developmental changes and their behavioral ramifications into context. RESUMEN Grik4 es el gen responsable de codificar la subunidad GluK4 de alta afinidad de los receptores de kainato. El aumento de la dosis de esta subunidad en el prosencéfalo se relacionó con un mayor nivel de ansiedad, falta de comunicación social y depresión. A nivel sináptico, también se informó una transmisión sináptica anormal. Las manifestaciones de esta expresión anormal no se han investigado a nivel de circuito, ni las correlaciones entre esos circuitos y los patrones anormales de la conducta previamente informada. En esta línea de trabajo, aspiramos a utilizar diferentes modalidades de imágenes por resonancia magnética (MRI) no invasivas para dilucidar cualquier alteración que pudiera derivarse del aumento de la dosis de Grik4 y cómo esos cambios podrían explicar los comportamientos anormales. La resonancia magnética ofrece una forma no invasiva de observar el cerebro intacto in vivo. La resonancia magnética funcional en estado de reposo arroja luz sobre cómo funciona el cerebro en reposo en el nivel de la red y tiene la capacidad de detectar cualquier anomalía que pueda ocurrir dentro o entre esas redes. En el nivel microestructural, la resonancia magnética de difusión se ocupa de las características subyacentes de los tejidos utilizando la difusión de moléculas de agua como un proxy para ese fin. Moviéndose más macroscópicamente, utilizando escaneos estructurales, la morfometría basada en vóxeles puede detectar diferencias sutiles en la morfología de las diferentes estructuras cerebrales. Grabamos videos de nuestros animales realizando dos tareas que durante mucho tiempo se han relacionado con la ansiedad, el campo abierto y las pruebas de laberinto positivo antes de adquirir escaneos estructurales y funcionales. Por último, registramos señales dependientes del nivel de oxigenación de la sangre (BOLD) en un grupo diferente de animales durante la estimulación eléctrica de tractos específicos de materia blanca para investigar cómo se propaga la actividad neuronal. Nuestro análisis mostró un amplio espectro de cambios en el grupo transgénico en relación con los animales en el grupo de control. En el nivel de las redes de estado de reposo, observamos un aumento en la fuerza dentro de la red que abarca diferentes estructuras como el hipocampo, algunas regiones de la corteza y el hipotálamo. La mayor coherencia interna o fuerza en las redes contrastó con una reducción significativa en la conectividad entre redes para algunas regiones como partes de la corteza y el hipotálamo, lo que sugiere una descorrelación de redes de largo alcance. Apoyando esta idea, los grandes tractos de materia blanca (WM), como el cuerpo calloso y la comisura del hipocampo, sufrieron cambios sustanciales compatibles con una importante reducción de la mielinización y / o una disminución del diámetro axonal medio. Macroestructuralmente hablando, la sobreexpresión de la subunidad GluK4 tuvo un efecto bimodal, con expansión en algunas áreas corticales en los animales transgénicos acompañada de una contracción en las regiones subcorticales. Al estimular el cerebro con una corriente eléctrica, notamos una diferencia en la propagación de la actividad entre las dos hemiesferas. En los animales transgénicos, la actividad evocada permaneció más confinada al hemisferio estimulado, de nuevo consistente con una conectividad de largo alcance deteriorada. Los cambios estructurales, tanto a nivel micro como macro, estaban en estrecha correlación con diferentes aspectos de la conducta, incluidos marcadores de ansiedad como el tiempo pasado con los brazos abiertos frente a los brazos cerrados en la prueba del laberinto positivo y el tiempo pasado en el centro vs las esquinas en la prueba de campo abierto. Nuestros hallazgos revelan cómo la interrupción de los receptores de kainato, o más globalmente los receptores de glutamato, y la transmisión sináptica anormal pueden traducirse en cambios de conectividad en todo el cerebro y alterar el equilibrio funcional entre las redes macro y mesoscópicas. La mejora postsináptica informada anteriormente en los animales transgénicos se reflejó aquí en la señal BOLD y se midió como un aumento en la fuerza dentro de la red. Es importante destacar que las correlaciones entre los cambios estructurales y elcomportamiento ayudan a contextualizar los cambios en el desarrollo y sus ramificaciones conductuales

Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest

Growing evidence has shown that brain activity at rest slowly wanders through a repertoire of different states, where whole-brain functional connectivity (FC) temporarily settles into distinct FC patterns. Nevertheless, the functional role of resting-state activity remains unclear. Here, we investigate how the switching behavior of resting-state FC relates with cognitive performance in healthy older adults. We analyse resting-state fMRI data from 98 healthy adults previously categorized as being among the best or among the worst performers in a cohort study of >1000 subjects aged 50+ who underwent neuropsychological assessment. We use a novel approach focusing on the dominant FC pattern captured by the leading eigenvector of dynamic FC matrices. Recurrent FC patterns - or states - are detected and characterized in terms of lifetime, probability of occurrence and switching profiles. We find that poorer cognitive performance is associated with weaker FC temporal similarity together with altered switching between FC states. These results provide new evidence linking the switching dynamics of FC during rest with cognitive performance in later life, reinforcing the functional role of resting-state activity for effective cognitive processing.This project was financed by the Fundação Calouste Gulbenkian (Portugal) (Contract grant number: P-139977; project “Better mental health during ageing based on temporal prediction of individual brain ageing trajectories (TEMPO)”), co-financed by Portuguese North Regional Operational Program (ON.2) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER) as well as the Projecto Estratégico co-funded by FCT (PEst-C/SAU/LA0026-/2013) and the European Regional Development Fund COMPETE (FCOMP-01-0124-FEDER-037298) and under the scope of the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020) under the Portugal 2020 Partnership Agreement through the European Regional Development Fundinfo:eu-repo/semantics/publishedVersio

Spectral Diversity in Default Mode Network Connectivity Reflects Behavioral State.

Default mode network (DMN) functional connectivity is thought to occur primarily in low frequencies (<0.1 Hz), resulting in most studies removing high frequencies during data preprocessing. In contrast, subtractive task analyses include high frequencies, as these are thought to be task relevant. An emerging line of research explores resting fMRI data at higher-frequency bands, examining the possibility that functional connectivity is a multiband phenomenon. Furthermore, recent studies suggest DMN involvement in cognitive processing; however, without a systematic investigation of DMN connectivity during tasks, its functional contribution to cognition cannot be fully understood. We bridged these concurrent lines of research by examining the contribution of high frequencies in the relationship between DMN and dorsal attention network at rest and during task execution. Our findings revealed that the inclusion of high frequencies alters between network connectivity, resulting in reduced anticorrelation and increased positive connectivity between DMN and dorsal attention network. Critically, increased positive connectivity was observed only during tasks, suggesting an important role for high-frequency fluctuations in functional integration. Moreover, within-DMN connectivity during task execution correlated with RT only when high frequencies were included. These results show that DMN does not simply deactivate during task execution and suggest active recruitment while performing cognitively demanding paradigms