fcMRI Data Research Articles

Assessing Predictive Ability of Dynamic Time Warping Functional Connectivity for ASD Classification.

Functional connectivity MRI (fcMRI) is a technique used to study the functional connectedness of distinct regions of the brain by measuring the temporal correlation between their blood oxygen level-dependent (BOLD) signals. fcMRI is typically measured with the Pearson correlation (PC), which assumes that there is no lag between time series. Dynamic time warping (DTW) is an alternative measure of similarity between time series that is robust to such time lags. We used PC fcMRI data and DTW fcMRI data as predictors in machine learning models for classifying autism spectrum disorder (ASD). When combined with dimension reduction techniques, such as principal component analysis, functional connectivity estimated with DTW showed greater predictive ability than functional connectivity estimated with PC. Our results suggest that DTW fcMRI can be a suitable alternative measure that may be characterizing fcMRI in a different, but complementary, way to PC fcMRI that is worth continued investigation. In studying different variants of cross validation (CV), our results suggest that, when it is necessary to tune model hyperparameters and assess model performance at the same time, a K-fold CV nested within leave-one-out CV may be a competitive contender in terms of performance and computational speed, especially when sample size is not large.

Concordance of functional MRI memory task and resting-state functional MRI connectivity used in surgical planning for pediatric temporal lobe epilepsy.

Assessing memory is often critical in surgical evaluation, although difficult to assess in young children and in patients with variable task abilities. While obtaining interpretable data from task-based functional MRI (fMRI) measures is common in compliant and awake patients, it is not known whether functional connectivity MRI (fcMRI) data show equivalent results. If this were the case, it would have substantial clinical and research generalizability. To evaluate this possibility, the authors evaluated the concordance between fMRI and fcMRI data collected in a presurgical epilepsy cohort. Task-based fMRI data for autobiographical memory tasks and resting-state fcMRI data were collected in patients with epilepsy evaluated at Seattle Children's Hospital between 2010 and 2017. To assess memory-related activation and laterality, signal change in task-based measures was computed as a percentage of the average blood oxygen level-dependent signal over the defined regions of interest. An fcMRI data analysis was performed using 1000 Functional Connectomes Project scripts based on Analysis of Functional NeuroImages and FSL (Functional Magnetic Resonance Imaging of the Brain Software Library) software packages. Lateralization indices (LIs) were estimated for activation and connectivity measures. The concordance between these two measures was evaluated using correlation and regression analysis. In this epilepsy cohort studied, the authors observed concordance between fMRI activation and fcMRI connectivity, with an LI regression coefficient of 0.470 (R2 = 0.221, p = 0.00076). Previously published studies have demonstrated fMRI and fcMRI overlap between measures of vision, attention, and language. In the authors' clinical sample, task-based measures of memory and analogous resting-state mapping were similarly linked in pattern and strength. These results support the use of fcMRI methods as a proxy for task-based memory performance in presurgical patients, perhaps including those who are more limited in their behavioral compliance. Future investigations to extend these results will be helpful to explore how the magnitudes of effect are associated with neuropsychological performance and postsurgical behavioral changes.

Functional Connectivities Are More Informative Than Anatomical Variables in Diagnostic Classification of Autism.

Machine learning techniques have been implemented to reveal brain features that distinguish people with autism spectrum disorders (ASDs) from typically developing (TD) peers. However, it remains unknown whether different neuroimaging modalities are equally informative for diagnostic classification. We combined anatomical magnetic resonance imaging (aMRI), diffusion weighted imaging (DWI), and functional connectivity MRI (fcMRI) using conditional random forest (CRF) for supervised learning to compare how informative each modality was in diagnostic classification. In-house data (N = 93) included 47 TD and 46 ASD participants, matched on age, motion, and nonverbal IQ. Four main analyses consistently indicated that fcMRI variables were significantly more informative than anatomical variables from aMRI and DWI. This was found (1) when the top 100 variables from CRF (run separately in each modality) were combined for multimodal CRF; (2) when only 19 top variables reaching >67% accuracy in each modality were combined in multimodal CRF; and (3) when the large number of initial variables (before dimension reduction) potentially biasing comparisons in favor of fcMRI was reduced using a less granular region of interest scheme. Consistent superiority of fcMRI was even found (4) when 100 variables per modality were randomly selected, removing any such potential bias. Greater informative value of functional than anatomical modalities may relate to the nature of fcMRI data, reflecting more closely behavioral condition, which is also the basis of diagnosis, whereas brain anatomy may be more reflective of neurodevelopmental history.

Changes in resting-state connectivity in pediatric temporal lobe epilepsy.

OBJECTIVE Functional connectivity magnetic resonance imaging (fcMRI) is a form of fMRI that allows for analysis of blood oxygen level-dependent signal changes within a task-free, resting paradigm. This technique has been shown to have efficacy in evaluating network connectivity changes with epilepsy. Presurgical data from patients with unilateral temporal lobe epilepsy were evaluated using the fcMRI technique to define connectivity changes within and between the diseased and healthy temporal lobes using a within-subjects design. METHODS Using presurgical fcMRI data from pediatric patients with unilateral temporal lobe epilepsy, the authors performed seed-based analyses within the diseased and healthy temporal lobes. Connectivity within and between temporal lobe seeds was measured and compared. RESULTS In the cohort studied, local ipsilateral temporal lobe connectivity was significantly increased on the diseased side compared to the healthy temporal lobe. Connectivity of the diseased side to the healthy side, on the other hand, was significantly reduced when compared to connectivity of the healthy side to the diseased temporal lobe. A statistically significant regression was observed when comparing the changes in local ipsilateral temporal lobe connectivity to the changes in inter-temporal lobe connectivity. A statistically significant difference was also noted in ipsilateral connectivity changes between patients with and those without mesial temporal sclerosis. CONCLUSIONS Using fcMRI, significant changes in ipsilateral temporal lobe and inter-temporal lobe connectivity can be appreciated in unilateral temporal lobe epilepsy. Furthermore, fcMRI may have a role in the presurgical evaluation of patients with intractable temporal lobe epilepsy.

Multisite reliability of MR-based functional connectivity

Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies provide an efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed-based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel-wise connectivity measure; and (3) matrix connectivity, a whole-brain, atlas-based approach to assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day-of-scan were quantified and used to assess between-session (test-retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day-of-scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel-wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60–80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i.e., subject, site, and day-of-scan). Thus, for a single 5min scan, reliability across connectivity measures was poor (ICC=0.07–0.17), but increased with increasing scan duration (ICC=0.21–0.36 at 25min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single-subject level.

Prefrontal Transcranial Direct Current Stimulation for Treatment of Schizophrenia With Predominant Negative Symptoms: A Double-Blind, Sham-Controlled Proof-of-Concept Study.

Negative symptoms are highly relevant in the long-term course of schizophrenia and are an important target domain for the development of novel interventions. Recently, transcranial direct current stimulation (tDCS) of the prefrontal cortex has been investigated as a treatment option in schizophrenia. In this proof-of-concept study, 20 schizophrenia patients with predominantly negative symptoms were randomized to either 10 sessions of add-on active (2 mA, 20min) or sham tDCS (anode: left DLPFC/F3; cathode: right supraorbital/F4). Primary outcome measure was the change in the Scale for the Assessment of Negative Symptoms (SANS) sum score; secondary outcomes included reduction in Positive and Negative Syndrome Scale (PANSS) scores and improvement of depressive symptoms, cognitive processing speed, and executive functioning. Sixteen patients underwent 4 functional connectivity magnetic resonance imaging (fcMRI) scans (pre and post 1st and pre and post 10th tDCS) to investigate changes in resting state network connectivity after tDCS. Per-protocol analysis showed a significantly greater decrease in SANS score after active (-36.1%) than after sham tDCS (-0.7%). PANSS sum scores decreased significantly more with active (-23.4%) than with sham stimulation (-2.2%). Explorative analysis of fcMRI data indicated changes in subgenual cortex and dorsolateral prefrontal cortex (DLPFC) connectivity within frontal-thalamic-temporo-parietal networks. The results of this first proof-of-concept study indicate that prefrontal tDCS may be a promising intervention for treatment of schizophrenia with predominant negative symptoms. Large-scale randomized controlled studies are needed to further establish prefrontal tDCS as novel treatment for negative symptoms in schizophrenia.

Test-retest reliability of pain-related functional brain connectivity compared with pain self-report

Test-retest reliability, or reproducibility of results over time, is poorly established for functional brain connectivity (fcMRI) during painful stimulation. As reliability informs the validity of research findings, it is imperative to examine, especially given recent emphasis on using functional neuroimaging as a tool for biomarker development. Although proposed pain neural signatures have been derived using complex, multivariate algorithms, even the reliability of less complex fcMRI findings has yet to be reported. This study examined the test-retest reliability for fcMRI of pain-related brain regions, and self-reported pain (through visual analogue scales [VASs]). Thirty-two healthy individuals completed 3 consecutive fMRI runs of a thermal pain task. Functional connectivity analyses were completed on pain-related brain regions. Intraclass correlations were conducted on fcMRI values and VAS scores across the fMRI runs. Intraclass correlations coefficients for fcMRI values varied widely (range = -.174-.766), with fcMRI between right nucleus accumbens and medial prefrontal cortex showing the highest reliability (range = .649-.766). Intraclass correlations coefficients for VAS scores ranged from .906 to .947. Overall, self-reported pain was more reliable than fcMRI data. These results highlight that fMRI findings might be less reliable than inherently assumed and have implications for future studies proposing pain markers.

Restoring susceptibility induced MRI signal loss in rat brain at 9.4 T: A step towards whole brain functional connectivity imaging.

The aural cavity magnetic susceptibility artifact leads to significant echo planar imaging (EPI) signal dropout in rat deep brain that limits acquisition of functional connectivity fcMRI data. In this study, we provide a method that recovers much of the EPI signal in deep brain. Needle puncture introduction of a liquid-phase fluorocarbon into the middle ear allows acquisition of rat fcMRI data without signal dropout. We demonstrate that with seeds chosen from previously unavailable areas, including the amygdala and the insular cortex, we are able to acquire large scale networks, including the limbic system. This tool allows EPI-based neuroscience and pharmaceutical research in rat brain using fcMRI that was previously not feasible.

Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization.

IS-020 Functional Connectivity In The Infant Brain

<h3></h3> Advanced MRI techniques have been increasingly applied in infants to explore the structural and functional architecture of the developing brain. Functional connectivity magnetic resonance imaging (fcMRI) utilises spontaneous, low-frequency, coherent fluctuations in blood oxygen level dependent signal to identify networks of functional cerebral connections. Application of fcMRI in infants provides unique technical challenges. To obtain high-quality fcMRI data, investigators have applied new technology and modified acquisition practices. Advanced analysis techniques have also been developed to improve anatomic registration, eliminate artifactual variance and improve signal-to-noise ratios. These measures have enabled successful, robust fcMRI investigations in neonates. Importantly, these methods are transferrable across institutions and clinical populations of interest. Neonatal fcMRI investigations have included healthy, term-born infants and prematurely-born infants with and without cerebral injury. In these populations, fcMRI data has been used to identify immature networks as early as 26 weeks postmenstrual age. These networks gradually mature. Prematurity (Figure 1) and white matter injury (Figure 2) significantly affect connectivity, altering network configuration and strength. These results demonstrate the promise of fcMRI as an investigational tool of neurodevelopment, providing insight into the earliest forms of functional cerebral development. While key groundwork has been laid, additional efforts are necessary to apply continued advances in technology and methodology. Expanded investigations will provide greater understanding of the processes underlying typical and atypical cerebral development and the role of these networks in neurodevelopmental outcomes.

Tu1797 The Adoptive Transfer of Anxiety and Gut Dysfunction From IBS Patients to Axenic Mice Through Microbiota Transplantation

Studies of the effect of esophageal sensory stimulation on brain functional connectivity using magnetic resonance imaging (fc-MRI) requires insertion of perfusion or balloon catheters. The contribution of the presence of these catheters themselves on observed brain connectivity changes has not been systematically studied. Aim: To determine the effect of the presence of a catheter within the esophagus on a priori selected regional brain connectivity in healthy and patient volunteers. Methods: In 14 controls (7 female, age 27±4 years) and 12 nonerosive reflux disease (NERD) patients (6 female, age 46±5 years), fc-MRI data of amygdala connectivity to the rest of the brain were gathered during two 9-minute scans, one scan without transnasal esophageal intubation and one scan with esophageal intubation. Functional connectivity was quantified by the correlation coefficient (CC) between average fcMRI time series (seeds) in two spherical volumes centered in each of the right and left amygdala and time series of all fc-MRI volume elements (voxels) across the entire brain. The amygdala seeds were centered at the locations identified by the MNI atlas. Group analysis was performed using unpaired t-tests (with cluster size Monte Carlo simulation to correct for multiple comparison) of correlation coefficients to test amygdala-cortical connectivity differences across NERD patients and healthy subjects. Results: Amygdala connectivity was characterized by strong connections to the right and left superior temporal gyrus. The presence of esophageal catheter in the healthy controls resulted in significant differences in both left and right amygdala connectivity to the left superior temporal gyrus (Figure 1, p<0.001, corrected). No significant catheter effect was observed in the NERD patients. Presence of the catheter also effected the between group comparisons showing significant differences in amygdala connectivity. In the resting state without the catheter present, significant differences in both right and left amygdala connectivity to the right superior temporal gyrus were seen between the two groups (p<0,001, corrected). In the presence of catheter, significant differences were limited to the right amygdala connectivity to the right superior temporal gyrus (p<0,001, corrected). Conclusions: Pharyngo-esophageal stimulation by the presence of a catheter alters the functional connectivity of the amygdala in healthy controls. This response is absent in NERD patients which may represent a state of pre-existing maximum neural activity and associated BOLD signal alterations (ceiling effect). Furthermore, the presence of an esophageal catheter can affect the findings of comparative analyses between groups. This possibility needs to be taken into account when interpreting resting and non-resting state connectivity data involving esophageal stimulation.

(325) fcMRI maps during pain tasks vary based on the inclusion of paradigm modeling in analysis

As an alternative to resting state functional connectivity (fcMRI) analysis, block-design fcMRI studies of pain have been increasingly performed. However, the effect of the stimulus-correlated MRI signal changes on fcMRI studies is still unknown. We compared analyses with and without modeling of the stimulation paradigm during a 30 s block-design experiment of electric nerve stimulation (ENS). Our primary hypothesis was that modeling the stimulation paradigm would result in reduced correlation, compared to an un-modeled analysis. fcMRI data was acquired in 14 healthy adults in an IRB-approved 3 T whole brain functional MRI study of ENS-generated pain in the right index finger (intensity self-adjusted to 7/10). A seed region was chosen in the left (contralateral) insula and modeled for each subject as the primary regressor with and without the stimulation paradigm included as an effect of no interest. These were compared to the fcMRI maps of a similar tonic pain task (without cyclic changes). Group average functional correlation maps were generated. Mean and maximum (max) Z-scores were tabulated from regions of interest (ROIs) with significant correlation. The left insula seed time course was found to be correlated to areas of the pain matrix (anterior cingulate cortex, bilateral primary and secondary somatosensory cortices, and right insula), as expected. Modeling the paradigm in this cyclic pain task decreased the strength of correlation in all significant brain areas, giving a map more similar to that of the tonic pain task. Thus, modeling the stimulus timing removed superimposed task-induced activation, giving more accurate fcMRI maps. As an alternative to resting state functional connectivity (fcMRI) analysis, block-design fcMRI studies of pain have been increasingly performed. However, the effect of the stimulus-correlated MRI signal changes on fcMRI studies is still unknown. We compared analyses with and without modeling of the stimulation paradigm during a 30 s block-design experiment of electric nerve stimulation (ENS). Our primary hypothesis was that modeling the stimulation paradigm would result in reduced correlation, compared to an un-modeled analysis. fcMRI data was acquired in 14 healthy adults in an IRB-approved 3 T whole brain functional MRI study of ENS-generated pain in the right index finger (intensity self-adjusted to 7/10). A seed region was chosen in the left (contralateral) insula and modeled for each subject as the primary regressor with and without the stimulation paradigm included as an effect of no interest. These were compared to the fcMRI maps of a similar tonic pain task (without cyclic changes). Group average functional correlation maps were generated. Mean and maximum (max) Z-scores were tabulated from regions of interest (ROIs) with significant correlation. The left insula seed time course was found to be correlated to areas of the pain matrix (anterior cingulate cortex, bilateral primary and secondary somatosensory cortices, and right insula), as expected. Modeling the paradigm in this cyclic pain task decreased the strength of correlation in all significant brain areas, giving a map more similar to that of the tonic pain task. Thus, modeling the stimulus timing removed superimposed task-induced activation, giving more accurate fcMRI maps.

Quantifying the Statistical Impact of GRAPPA in fcMRI Data With a Real-Valued Isomorphism

The interpolation of missing spatial frequencies through the generalized auto-calibrating partially parallel acquisitions (GRAPPA) parallel magnetic resonance imaging (MRI) model implies a correlation is induced between the acquired and reconstructed frequency measurements. As the parallel image reconstruction algorithms in many medical MRI scanners are based on the GRAPPA model, this study aims to quantify the statistical implications that the GRAPPA model has in functional connectivity studies. The linear mathematical framework derived in the work of Rowe , 2007, is adapted to represent the complex-valued GRAPPA image reconstruction operation in terms of a real-valued isomorphism, and a statistical analysis is performed on the effects that the GRAPPA operation has on reconstructed voxel means and correlations. The interpolation of missing spatial frequencies with the GRAPPA model is shown to result in an artificial correlation induced between voxels in the reconstructed images, and these artificial correlations are shown to reside in the low temporal frequency spectrum commonly associated with functional connectivity. Through a real-valued isomorphism, such as the one outlined in this manuscript, the exact artificial correlations induced by the GRAPPA model are not simply estimated, as they would be with simulations, but are precisely quantified. If these correlations are unaccounted for, they can incur an increase in false positives in functional connectivity studies.

Alterations in functional connectivity between the hippocampus and prefrontal cortex as a correlate of depressive symptoms in temporal lobe epilepsy

Depression is a common comorbidity in temporal lobe epilepsy (TLE) that is thought to have a neurobiological basis. This study investigated the functional connectivity (FC) of medial temporal networks in depression symptomatology of TLE and the relative contribution of structural versus FC measures. Volumetric MRI and functional connectivity MRI (fcMRI) were performed on nineteen patients with TLE and 20 controls. The hippocampi and amygdalae were selected as seeds, and five prefrontal and five cingulate regions of interest (ROIs) were selected as targets. Low-frequency blood-oxygen-level-dependent signals were isolated from fcMRI data, and ROIs with synchronous signal fluctuations with the seeds were identified. Depressive symptoms were measured by the Beck Depression Inventory—II. The patients with TLE showed greater ipsilateral hippocampal atrophy (HA) and reduced FC between the ipsilateral hippocampus and the ventral posterior cingulate cortex (vPCC). Neither HA nor hippocampal–vPCC FC asymmetry was a robust contributor to depressive symptoms. Rather, hippocampal–anterior prefrontal FC was a stronger contributor to depressive symptoms in left TLE (LTLE). Conversely, right amygdala FC was correlated with depressive symptoms in both patient groups, with a positive and negative correlation in LTLE and right TLE (RTLE), respectively. Frontolimbic network dysfunction is a strong contributor to levels of depressive symptoms in TLE and a better contributor than HA in LTLE. In addition, the right amygdala may play a role in depression symptomatology regardless of the side of the epileptogenic focus. These findings may inform the treatment of depressive symptoms in TLE and inspire future research to help guide surgical planning.

Alterations in resting functional connectivity due to recent motor task

The impact of recent experiences of task performance on resting functional connectivity MRI (fcMRI) has important implications for the design of many neuroimaging studies, because, if an effect is present, the fcMRI scan then must be performed before any evoked fMRI or after a time gap to allow it to dissipate. The present study aims to determine the effect of simple button presses, which are used in many cognitive fMRI tasks as a response recording method, on later acquired fcMRI data. Human volunteers were subject to a 23-minute button press motor task. Their resting-state brain activity before and after the task was assessed with fcMRI. It was found that, compared to the pre-task resting period, the post-task resting fcMRI revealed a significantly higher (p=0.002, N=24) cross correlation coefficient (CC) between left and right motor cortices. These changes were not present in sham control studies that matched the paradigm timing but had no actual task. The amplitude of fcMRI signal fluctuation (AF) also demonstrated an increase in the post-task period compared to pre-task. These changes were observed using both the right-hand-only task and the two-hand task. Study of the recovery time course of these effects revealed that the CC changes lasted for about 5 min while the AF change lasted for at least 15 min. Finally, voxelwise analysis revealed that the pre/post-task differences were also observed in several other brain regions, including the auditory cortex, visual areas, and the thalamus. Our data suggest that the recent performance of the simple button press task can result in elevated fcMRI CC and AF in relevant brain networks and that fcMRI scan should be performed either before evoked fMRI or after a sufficient time gap following fMRI.

The Teenage Brain

Distant brain regions are organized into large-scale functional networks specialized for specific cognitive processes. The brain’s functional-network architecture and its development can be investigated using functional connectivity MRI (fcMRI), which measures correlations in spontaneous fluctuations of brain activity. fcMRI studies have provided important insights into typical brain organization and development, as well as insights into the atypical organization of the brain in neuropsychiatric disorders. fcMRI data can be easily collected and carry much information. Therefore, they are now being analyzed using powerful multivariate-pattern-analysis (MVPA) methods, with the goal of one day being able to diagnose disease states in individuals. However, great care must be taken during these analyses to eliminate confounds such as head movement.

Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks

Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.

A Sliding Time-Window ICA Reveals Spatial Variability of the Default Mode Network in Time

Recent evidence on resting-state networks in functional (connectivity) magnetic resonance imaging (fcMRI) suggests that there may be significant spatial variability of activity foci over time. This study used a sliding time window approach with the spatial domain-independent component analysis (SliTICA) to detect spatial maps of resting-state networks over time. The study hypothesis was that the spatial distribution of a functionally connected network would present marked variability over time. The spatial stability of successive sliding-window maps of the default mode network (DMN) from fcMRI data of 12 participants imaged in the resting state was analyzed. Control measures support previous findings on the stability of independent component analysis in measuring sliding-window sources accurately. The spatial similarity of successive DMN maps varied over time at low frequencies and presented a 1/f power spectral pattern. SliTICA maps show marked temporal variation within the DMN; a single voxel was detected inside a group DMN map in maximally 82% of time windows. Mapping of incidental connectivity reveals centrifugally increasing connectivity to the brain cortex outside the DMN core areas. In conclusion, SliTICA shows marked spatial variance of DMN activity in time, which may offer a more comprehensive measurement of the overall functional activity of a network.

Prediction of individual brain maturity using fMRI.

Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals' brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain's major functional networks.

Longitudinal Analysis of Neural Network Development in Preterm Infants

Application of resting state functional connectivity magnetic resonance imaging (fcMRI) to the study of prematurely born infants enables assessment of the earliest forms of cerebral connectivity and characterization of its early development in the human brain. We obtained 90 longitudinal fcMRI data sets from a cohort of preterm infants aged from 26 weeks postmenstrual age (PMA) through term equivalent age at PMA-specific time points. Utilizing seed-based correlation analysis, we identified resting state networks involving varied cortical regions, the thalamus, and cerebellum. Identified networks demonstrated a regionally variable age-specific pattern of development, with more mature forms consisting of localized interhemispheric connections between homotopic counterparts. Anatomical distance was found to play a critical role in the rate of connection development. Prominent differences were noted between networks identified in term control versus premature infants at term equivalent, including in the thalamocortical connections critical for neurodevelopment. Putative precursors of the default mode network were detected in term control infants but were not identified in preterm infants, including those at term equivalent. Identified patterns of network maturation reflect the intricate relationship of structural and functional processes present throughout this important developmental period and are consistent with prior investigations of neurodevelopment in this population.