Abnormal resting-state functional connectivity in progressive supranuclear palsy and corticobasal syndrome

Background: Pathological and MRI-based evidence suggests that multiple brain structures are likely to be involved in functional disconnection between brain areas. Few studies have investigated resting-state functional connectivity (rsFC) in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). In this study, we investigated within- and between-network rsFC abnormalities in these two conditions. Methods: Twenty patients with PSP, 11 patients with CBS, and 16 healthy subjects (HS) underwent a resting-state fMRI study. Resting-state networks (RSNs) were extracted to evaluate within- and between-network rsFC using the Melodic and FSLNets software packages. results: Increased within-network rsFC was observed in both PSP and CBS patients, with a larger number of RSNs being involved in CBS. Within-network cerebellar rsFC positively correlated with mini-mental state examination scores in patients with PSP. Compared to healthy volunteers, PSP and CBS patients exhibit reduced functional connectivity between the lateral visual and auditory RSNs, with PSP patients additionally showing lower functional connectivity between the cerebellar and insular RSNs. Moreover, rsFC between the salience and executive-control RSNs was increased in patients with CBS compared to HS. conclusion: This study provides evidence of functional brain reorganization in both PSP and CBS. Increased within-network rsFC could represent a higher degree of synchronization in damaged brain areas, while between-network rsFC abnormalities may mainly reflect degeneration of long-range white matter fibers

Simultaneous Robotic Manipulation and Functional Magnetic Resonance Imaging: Feasibility in Children with Autism Spectrum Disorders

An unanswered question concerning the neural basis of autism spectrum disorders (ASD) is how sensorimotor deficits in individuals with ASD are related to abnormalities of brain function. We previously described a robotic joystick and video game system that allows us to record functional magnetic resonance images (FMRI) while adult humans make goal- directed wrist motions. We anticipated several challenges in extending this approach to studying goal-directed behaviors in children with ASD and in typically developing (TYP) children. In particular we were concerned that children with autism may express increased levels of anxiety as compared to typically developing children due to the loud sounds and small enclosed space of the MRI scanner. We also were concerned that both groups of children might become restless during testing, leading to an unacceptable amount of head movement. Here we performed a pilot study evaluating the extent to which autistic and typically developing children exhibit anxiety during our experimental protocol as well as their ability to comply with task instructions. Our experimental controls were successful in minimizing group differences in drop-out due to anxiety. Kinematic performance and head motion also were similar across groups. Both groups of children engaged cortical regions (frontal, parietal, temporal, occipital) while making goal- directed movements. In addition, the ASD group exhibited task- related correlations in subcortical regions (cerebellum, thalamus), whereas correlations in the TYP group did not reach statistical significance in subcortical regions. Four distinct regions in frontal cortex showed a significant group difference such that TYP children exhibited positive correlations between the hemodynamic response and movement, whereas children with ASD exhibited negative correlations. These findings demonstrate feasibility of simultaneous application of robotic manipulation and functional imaging to study goal-directed motor behaviors in autistic and typically developing children. The findings also suggest the presence of marked changes in neural activation during a sensorimotor task requiring goal- directed movement

Brain Age: A State-Of-Mind? On the Stability of Functional Connectivity across Behavioral States

The study of functional connectivity (FC) has become a major branch of functional MRI (fMRI) research. Biswal et al. (1995)'s seminal discovery, that voxels in the sensorimotor cortex exhibited highly correlated activity at rest, seeded the field; however, it took at least 10 more years for it to gain widespread interest (Cordes et al., 2000; Greicius et al., 2003; Fox et al., 2005; Smith et al., 2009). There is currently much research into using FC as a biomarker for clinical diagnosis (Greicius, 2008; Linden, 2012) and, more generally, to gain insight into individual differences in brain function (Smith et al., 2013). Most studies investigate FC in the so-called “resting state”: subjects in the scanner are instructed to “lie still and think of nothing in particular,” with eyes closed, or open and fixating (Patriat et al., 2013); however, FC can also be computed from task fMRI data, usually after regressing out stimulus-evoked activity (Fair et al., 2007). Cole et al. (2014) showed that, on average across subjects, a reliable intrinsic network structure is preserved through all tasks and rest. Additionally, ∼40% of the connections show mild but significant changes that are task- (equivalently, state-) dependent. The variability of FC in individual subjects is now well recognized; functional network structure actually moves through several states within the span of a single resting-state run (Hutchison et al., 2013; Allen et al., 2014). While some authors have used the dynamic nature of individual network structure to their advantage, e.g., Damaraju et al. (2014), there is growing concern that this variability could impede our ability to use FC as a stable, trait-like measure of individual subjects. A recent study in The Journal of Neuroscience (Geerligs et al., 2015) reinforces this concern. Geerligs et al. (2015)'s study is among the first published outputs of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) cohort study, a large-scale (N = ∼700), multimodal (MRI, MEG, and behavioral), cross-sectional, population-based adult lifespan (18–87 years old) investigation of the neural underpinnings of successful cognitive aging (Shafto et al., 2014; Taylor et al., 2015). Geerligs et al. (2015) used state-of-the-art imaging and preprocessing techniques, notably with respect to motion correction, which has been a thorny issue in the functional connectivity literature (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012; Tyszka et al., 2014), and is especially problematic in aging studies (older people tend to move more, as confirmed in this study). Geerligs et al. (2015)'s study boasts a final sample size of 587 subjects (∼100 per decade of life), all of whom completed three different tasks in the scanner: an 8 min, 40 s eyes-closed resting-state run (REST state), an 8 min, 40 s sensorimotor task (detection of brief auditory tones and/or visual checkerboard flashes; TASK state), and an 8 min, 13 s movie-watching run (the movie being a shortened version of Alfred Hitchcock's television episode “Bang, you're dead!,” as described in Hasson et al. (2010); MOVIE state). Whole-brain FC was assessed among 748 nodes from a published functional parcellation (Craddock et al., 2012) (Fig. 1e), in each of the three states (REST, TASK, MOVIE), yielding a 748 × 748 FC matrix for each subject and each state (Fig. 1a). First, the authors performed the same analysis as Cole et al. (2014): they averaged FC matrices across subjects, then quantified the similarity of the average FC matrices for each pair of states using the Pearson correlation coefficient r (Fig. 1b). As in Cole et al. (2014), they found a high similarity between the REST and TASK FC matrices [variance explained r^2 = 87% of total variance (TV)]. Crucially, Geerligs et al. (2015) also quantified the reliability of the average FC matrix in each state using a (conservative) split-half procedure: the explainable variance (EV) was high (99%TV), because of the large number of subjects. The variance attributable to state effects was thus 99%TV − 87%TV = 12%TV; i.e., 12%TV/99%TV = 11.9%EV, for the REST–TASK comparison

Resting state correlates of subdimensions of anxious affect

Resting state fMRI may help identify markers of risk for affective disorder. Given the comorbidity of anxiety and depressive disorders and the heterogeneity of these disorders as defined by DSM, an important challenge is to identify alterations in resting state brain connectivity uniquely associated with distinct profiles of negative affect. The current study aimed to address this by identifying differences in brain connectivity specifically linked to cognitive and physiological profiles of anxiety, controlling for depressed affect. We adopted a two-stage multivariate approach. Hierarchical clustering was used to independently identify dimensions of negative affective style and resting state brain networks. Combining the clustering results, we examined individual differences in resting state connectivity uniquely associated with subdimensions of anxious affect, controlling for depressed affect. Physiological and cognitive subdimensions of anxious affect were identified. Physiological anxiety was associated with widespread alterations in insula connectivity, including decreased connectivity between insula subregions and between the insula and other medial frontal and subcortical networks. This is consistent with the insula facilitating communication between medial frontal and subcortical regions to enable control of physiological affective states. Meanwhile, increased connectivity within a frontoparietal-posterior cingulate cortex-precunous network was specifically associated with cognitive anxiety, potentially reflecting increased spontaneous negative cognition (e.g., worry). These findings suggest that physiological and cognitive anxiety comprise subdimensions of anxiety-related affect and reveal associated alterations in brain connectivity

Simple 1-D Convolutional Networks for Resting-State fMRI Based Classification in Autism

Deep learning methods are increasingly being used with neuroimaging data like structural and function magnetic resonance imaging (MRI) to predict the diagnosis of neuropsychiatric and neurological disorders. For psychiatric disorders in particular, it is believed that one of the most promising modality is the resting-state functional MRI (rsfMRI), which captures the intrinsic connectivity between regions in the brain. Because rsfMRI data points are inherently high-dimensional (~1M), it is impossible to process the entire input in its raw form. In this paper, we propose a very simple transformation of the rsfMRI images that captures all of the temporal dynamics of the signal but sub-samples its spatial extent. As a result, we use a very simple 1-D convolutional network which is fast to train, requires minimal preprocessing and performs at par with the state-of-the-art on the classification of Autism spectrum disorders.Comment: accepted for publication in IJCNN 201