Abstract In this paper, we build on our previous analysis Bluhm, R.L., Miller, J., Lanius, R.A., Osuch, E.A., Boksman, K., Neufeld, R.W.J., et al., 2007 Spontaneous low-frequency fluctuations in the ...BOLD signal in schizophrenic patients: anomalies in the default network. Schizophrenia Bulletin 33, 1004–1012 of resting state connectivity in schizophrenia by examining alterations in connectivity of the retrosplenial cortex. We have previously demonstrated altered connectivity of the posterior cingulate/precuneus, particularly with other regions of the “default network” (which includes the medial prefrontal cortex and bilateral lateral parietal cortex). It was hypothesized that the retrosplenial cortex would show aberrant patterns of connectivity with regions of the default network and regions associated with memory. Patients with schizophrenia ( N = 17) and healthy controls ( N = 17) underwent a 5.5-min resting functional magnetic resonance imaging scan. Lower correlations were observed in patients with schizophrenia than in healthy controls between the retrosplenial cortex and both the temporal lobe and regions of the default network. In patients with schizophrenia, activity in the retrosplenial cortex correlated negatively with activity in bilateral anterior cingulate gyrus/medial prefrontal cortex (BA 32/10), despite the fact that these regions, as part of the default network, were expected to show positive correlations in activity. Connectivity of the retrosplenial cortex was greater in patients with more positive symptoms with areas previously associated with hallucinations, particularly the left superior temporal gyrus. These results suggest that spontaneous activity in the retrosplenial cortex during rest is altered in patients with schizophrenia. These alterations may help to explain alterations in self-oriented processing in this patient population.
Spectral analysis of brain activation in different regions, either in the form of network time-courses or regions of interest (ROI) time-series, has been a topic of interest in recent studies. Such ...studies hypothesize that observed brain fluctuations are due to different underlying sources of neurophysiological activation. Among these studies, brain fluctuations during the resting-state, as an unconstrained condition, have been a subject of interest. Some clinical studies have employed spectral analysis to locate differences between diagnostic groups such as schizophrenia and bipolar disorder. Other studies have argued that resting-state brain fluctuations are in fact dynamic, and that activation and connectivity of brain regions develops and evolves spontaneously. In this study, we combine both approaches and focus on capturing dynamics of the spectral properties of network time-courses estimated from independent components analysis (ICA) and categorizing spontaneous frequency profiles of network time-courses into three major profiles, which we call "frequency modes". We show that brain networks have distinct time-varying frequency domain characteristics, differing from one another in their occupancy rates of the frequency modes. Additionally, we identify some networks in which the occurrence rates of the different modes are significantly different based on the gender of the subjects.
Background
Age‐related cognitive decline after 65 is a well‐known phenomenon, but little is known about how brain functional changes are related to cognitive decline. To this end, previous studies ...explored the link between functional network connectivity (FNC) estimated from resting‐state functional MRI (rs‐fMRI) and cognitive scores in healthy adults. FNC is often assumed to be static over time. However, this assumption runs contrary to the dynamic nature of brain FNC, and dynamic FNC (dFNC) has been recently introduced to overcome this limitation. The current study investigated the relationship between dFNC estimated from 36,263 individuals from UK Biobank and cognition.
Method
We used the resting‐state fMRI (duration: 5min) data of 37,784 (20,157 females) adults’ brains, demographic information (age:64.06± 7.51), and cognitive scores from the UK Biobank in which the cognitive scores include fluid intelligence (FI), reaction time (RT), and pairs matching (Pairs). We adapted group independent component analysis to extract 53 data‐driven components for the whole brain using a fully automated approach (Fig.1: Step1). Next, we used the sliding window and Pearson correlation to estimate the dFNC among 53 components (Fig.1: Step2). We used k‐means clustering to put dFNCs of all individuals into three separated states and calculated individual state vectors (Fig.1: Step3). Then, we estimated 32 dFNC features based on three estimated states and state vectors (Fig.1: Step4). Finally, we trained a two‐fold cross‐validation support vector regression to predict the cognitive scores (Fig.1: Step5).
Result
The estimated dFNC feature predicted FI score with high accuracy (the correlation between measure and predicted score is R = 0.043, p = 6.6e−17, Fig.2A), mean time to correctly identify match in RT task (R = 0.065, p = 1.9−34, Fig.2B), and time to complete Pairs task (R = 0.049, p = 1.3e−20, Fig.2C).
Conclusion
Here we explored the link between dFNC features and cognition in the most extensive dFNC study and found that estimated dFNC features can successfully predict cognitive scores in the UK Biobank dataset. Even though the correlation values are very low, the results are still very significant due to large N. Future study is needed to explore the difference between the subgroup with high versus low prediction accuracy.
While the analysis of temporal signal fluctuations and co-fluctuations has long been a fixture of blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging CMRl) research, the ...role and implications of spatial propagation within the 4D neurovascular BOLD signal has been almost entirely neglected As part of a larger research program aimed at capturing and analyzing spatially propagative dynamics in BOLDjMRl, we report here a method that exposes large-scale functional attractors of flow processes defined via Markov processes defined at the voxel level. The brainwide stationary distributions of these voxel-level Markov processes represent patterns of signal accumulation toward which the brain exerts a probabilistic propagative undertow. These probabilistic propagative attractors are spatially structured and organized interpretably over functional regions. They also differ significantly between schizophrenia patients and healthy controls.
Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional ...network connectivity (FNC), estimated from resting state functional magnetic resonance imaging (fMRI), to investigate the biological signature of MDD. However, the majority of them have ignored the temporal change of brain interaction by focusing on static FNC (sFNC). Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In the current study, by applying k-means clustering on dFNC of MDD and healthy subjects (HCs), we estimated 5 different states. Next, we use the hidden Markov model as a potential biomarker to differentiate the dFNC pattern of MDD patients from HCs. Comparing MDD and HC subjects' hidden Markov model (HMM) features, we have highlighted the role of transition probabilities between states as potential biomarkers and identified that transition probability from a lightly-connected state to highly connected one reduces as symptom severity increases in MDD subjects.
Clinicians and developers of deep learning-based neuroimaging clinical decision support systems (CDSS) need to know whether those systems will perform well for specific individuals. However, ...relatively few methods provide this capability. Identifying neuropsychiatric disorder subtypes for which CDSS may have varying performance could offer a solution. Dynamic functional network connectivity (dFNC) is often used to study disorders and develop neuroimaging classifiers. Unfortunately, few studies have identified neurological disorder subtypes using dFNC. In this study, we present a novel approach with which we identify 4 states of dFNC activity and 4 schizophrenia subtypes based on their time spent in each state. We also show how the performance of an explainable diagnostic deep learning classifier is subtype-dependent. We lastly examine how the dFNC features used by the classifier vary across subtypes. Our study provides a novel approach for subtyping disorders that (1) has implications for future scientific studies and (2) could lead to more reliable CDSS.
Employing functional magnetic resonance imaging (fMRI) data from a large schizophrenia study, we use wavelets and frequency-specific spectral thresholding to transform each of the functional network ...timecourses (TCs) produced by group independent component analysis (GICA) into sparse multivariate spectral timeseries that consist only of zeros except at time-frequency points where power exceeds the 95 th percentile for that frequency, i.e. masked to retain only frequency-specific extremal spectral events. From this characterization of network timeseries in terms of transient spectral peaks, we identify new distinctions between the temporal behavior of different functional networks and domains, new anomalies of meso-scale brain activation in schizophrenia patients and relationships between transient muti-network spectral extrema and time-varying network connectivity that have not been reported previously.
Preliminary report of the results of a project
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to introduce peer instruction into a multi-section first semester calculus course taught largely by novice instructors. This paper summarizes the ...instructional approaches instructors chose to use, and the subsequent results of student performance on common exams throughout the course of the term.
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Support for the Good Questions project was provided by the National Science Foundation's Course, Curriculum, and Laboratory Improvement Program under grant DUE-0231154. Opinions expressed are those of the authors and not necessarily those of the Foundation.
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